WorldWideScience

Sample records for satellite images aerial

  1. Automatic geolocation of targets tracked by aerial imaging platforms using satellite imagery

    Science.gov (United States)

    Shukla, P. K.; Goel, S.; Singh, P.; Lohani, B.

    2014-11-01

    Tracking of targets from aerial platforms is an important activity in several applications, especially surveillance. Knowled ge of geolocation of these targets adds additional significant and useful information to the application. This paper determines the geolocation of a target being tracked from an aerial platform using the technique of image registration. Current approaches utilize a POS to determine the location of the aerial platform and then use the same for geolocation of the targets using the principle of photogrammetry. The constraints of cost and low-payload restrict the applicability of this approach using UAV platforms. This paper proposes a methodology for determining the geolocation of a target tracked from an aerial platform in a partially GPS devoid environment. The method utilises automatic feature based registration technique of a georeferenced satellite image with an ae rial image which is already stored in UAV's database to retrieve the geolocation of the target. Since it is easier to register subsequent aerial images due to similar viewing parameters, the subsequent overlapping images are registered together sequentially thus resulting in the registration of each of the images with georeferenced satellite image thus leading to geolocation of the target under interest. Using the proposed approach, the target can be tracked in all the frames in which it is visible. The proposed concept is verified experimentally and the results are found satisfactory. Using the proposed method, a user can obtain location of target of interest as well features on ground without requiring any POS on-board the aerial platform. The proposed approach has applications in surveillance for target tracking, target geolocation as well as in disaster management projects like search and rescue operations.

  2. Automatic geolocation of targets tracked by aerial imaging platforms using satellite imagery

    OpenAIRE

    Shukla, P. K.; Goel, S.; Singh, P.; B. Lohani

    2014-01-01

    Tracking of targets from aerial platforms is an important activity in several applications, especially surveillance. Knowled ge of geolocation of these targets adds additional significant and useful information to the application. This paper determines the geolocation of a target being tracked from an aerial platform using the technique of image registration. Current approaches utilize a POS to determine the location of the aerial platform and then use the same for geolocation of the...

  3. The Need of Nested Grids for Aerial and Satellite Images and Digital Elevation Models

    Science.gov (United States)

    Villa, G.; Mas, S.; Fernández-Villarino, X.; Martínez-Luceño, J.; Ojeda, J. C.; Pérez-Martín, B.; Tejeiro, J. A.; García-González, C.; López-Romero, E.; Soteres, C.

    2016-06-01

    Usual workflows for production, archiving, dissemination and use of Earth observation images (both aerial and from remote sensing satellites) pose big interoperability problems, as for example: non-alignment of pixels at the different levels of the pyramids that makes it impossible to overlay, compare and mosaic different orthoimages, without resampling them and the need to apply multiple resamplings and compression-decompression cycles. These problems cause great inefficiencies in production, dissemination through web services and processing in "Big Data" environments. Most of them can be avoided, or at least greatly reduced, with the use of a common "nested grid" for mutiresolution production, archiving, dissemination and exploitation of orthoimagery, digital elevation models and other raster data. "Nested grids" are space allocation schemas that organize image footprints, pixel sizes and pixel positions at all pyramid levels, in order to achieve coherent and consistent multiresolution coverage of a whole working area. A "nested grid" must be complemented by an appropriate "tiling schema", ideally based on the "quad-tree" concept. In the last years a "de facto standard" grid and Tiling Schema has emerged and has been adopted by virtually all major geospatial data providers. It has also been adopted by OGC in its "WMTS Simple Profile" standard. In this paper we explain how the adequate use of this tiling schema as common nested grid for orthoimagery, DEMs and other types of raster data constitutes the most practical solution to most of the interoperability problems of these types of data.

  4. THE NEED OF NESTED GRIDS FOR AERIAL AND SATELLITE IMAGES AND DIGITAL ELEVATION MODELS

    Directory of Open Access Journals (Sweden)

    G. Villa

    2016-06-01

    Full Text Available Usual workflows for production, archiving, dissemination and use of Earth observation images (both aerial and from remote sensing satellites pose big interoperability problems, as for example: non-alignment of pixels at the different levels of the pyramids that makes it impossible to overlay, compare and mosaic different orthoimages, without resampling them and the need to apply multiple resamplings and compression-decompression cycles. These problems cause great inefficiencies in production, dissemination through web services and processing in “Big Data” environments. Most of them can be avoided, or at least greatly reduced, with the use of a common “nested grid” for mutiresolution production, archiving, dissemination and exploitation of orthoimagery, digital elevation models and other raster data. “Nested grids” are space allocation schemas that organize image footprints, pixel sizes and pixel positions at all pyramid levels, in order to achieve coherent and consistent multiresolution coverage of a whole working area. A “nested grid” must be complemented by an appropriate “tiling schema”, ideally based on the “quad-tree” concept. In the last years a “de facto standard” grid and Tiling Schema has emerged and has been adopted by virtually all major geospatial data providers. It has also been adopted by OGC in its “WMTS Simple Profile” standard. In this paper we explain how the adequate use of this tiling schema as common nested grid for orthoimagery, DEMs and other types of raster data constitutes the most practical solution to most of the interoperability problems of these types of data.

  5. A methodology for near real-time change detection between Unmanned Aerial Vehicle and wide area satellite images

    Science.gov (United States)

    Fytsilis, Anastasios L.; Prokos, Anthony; Koutroumbas, Konstantinos D.; Michail, Dimitrios; Kontoes, Charalambos C.

    2016-09-01

    In this paper a novel integrated hybrid methodology for unsupervised change detection between Unmanned Aerial Vehicle (UAV) and satellite images, which can be utilized in various fields like security applications (e.g. border surveillance) and damage assessment, is proposed. This is a challenging problem mainly due to the difference in geographic coverage and the spatial resolution of the two images, as well as to the acquisition modes which lead to misregistration errors. The methodology consists of the following steps: (a) pre-processing, where the part of the satellite image that corresponds to the UAV image is determined and the UAV image is ortho-rectified using information provided by a Digital Terrain Model, (b) the detection of potential changes, which is based exclusively on intensity and image gradient information, (c) the generation of the region map, where homogeneous regions are produced by the previous potential changes via a seeded region growing algorithm and placed on the region map, and (d) the evaluation of the above regions, in order to characterize them as true changes or not. The methodology has been applied on demanding real datasets with very encouraging results. Finally, its robustness to the misregistration errors is assessed via extensive experimentation.

  6. Exploration of mineral resource deposits based on analysis of aerial and satellite image data employing artificial intelligence methods

    Science.gov (United States)

    Osipov, Gennady

    2013-04-01

    includes noncontact registration of eye motion, reconstruction of "attention landscape" fixed by the expert, recording the comments of the expert who is a specialist in the field of images` interpretation, and transfer this information into knowledge base.Creation of base of ophthalmologic images (OI) includes making semantic contacts from great number of OI based on analysis of OI and expert's comments.Processing of OI and making generalized OI (GOI) is realized by inductive logic algorithms and consists in synthesis of structural invariants of OI. The mode of recognition and interpretation of unknown images consists of several stages, which include: comparison of unknown image with the base of structural invariants of OI; revealing of structural invariants in unknown images; ynthesis of interpretive message of the structural invariants base and OI base (the experts` comments stored in it). We want to emphasize that the training mode does not assume special involvement of experts to teach the system - it is realized in the process of regular experts` work on image interpretation and it becomes possible after installation of a special apparatus for non contact registration of experts` attention. Consequently, the technology, which principles is described there, provides fundamentally new effective solution to the problem of exploration of mineral resource deposits based on computer analysis of aerial and satellite image data.

  7. APPLICABILITY EVALUATION OF OBJECT DETECTION METHOD TO SATELLITE AND AERIAL IMAGERIES

    Directory of Open Access Journals (Sweden)

    K. Kamiya

    2016-06-01

    Full Text Available Since satellite and aerial imageries are recently widely spread and frequently observed, combination of them are expected to complement spatial and temporal resolution each other. One of the prospective applications is traffic monitoring, where objects of interest, or vehicles, need to be recognized automatically. Techniques that employ object detection before object recognition can save a computational time and cost, and thus take a significant role. However, there is not enough knowledge whether object detection method can perform well on satellite and aerial imageries. In addition, it also has to be studied how characteristics of satellite and aerial imageries affect the object detection performance. This study employ binarized normed gradients (BING method that runs significantly fast and is robust to rotation and noise. For our experiments, 11-bits BGR-IR satellite imageries from WorldView-3, and BGR-color aerial imageries are used respectively, and we create thousands of ground truth samples. We conducted several experiments to compare the performances with different images, to verify whether combination of different resolution images improved the performance, and to analyze the applicability of mixing satellite and aerial imageries. The results showed that infrared band had little effect on the detection rate, that 11-bit images performed less than 8-bit images and that the better spatial resolution brought the better performance. Another result might imply that mixing higher and lower resolution images for training dataset could help detection performance. Furthermore, we found that aerial images improved the detection performance on satellite images.

  8. Applicability Evaluation of Object Detection Method to Satellite and Aerial Imageries

    Science.gov (United States)

    Kamiya, K.; Fuse, T.; Takahashi, M.

    2016-06-01

    Since satellite and aerial imageries are recently widely spread and frequently observed, combination of them are expected to complement spatial and temporal resolution each other. One of the prospective applications is traffic monitoring, where objects of interest, or vehicles, need to be recognized automatically. Techniques that employ object detection before object recognition can save a computational time and cost, and thus take a significant role. However, there is not enough knowledge whether object detection method can perform well on satellite and aerial imageries. In addition, it also has to be studied how characteristics of satellite and aerial imageries affect the object detection performance. This study employ binarized normed gradients (BING) method that runs significantly fast and is robust to rotation and noise. For our experiments, 11-bits BGR-IR satellite imageries from WorldView-3, and BGR-color aerial imageries are used respectively, and we create thousands of ground truth samples. We conducted several experiments to compare the performances with different images, to verify whether combination of different resolution images improved the performance, and to analyze the applicability of mixing satellite and aerial imageries. The results showed that infrared band had little effect on the detection rate, that 11-bit images performed less than 8-bit images and that the better spatial resolution brought the better performance. Another result might imply that mixing higher and lower resolution images for training dataset could help detection performance. Furthermore, we found that aerial images improved the detection performance on satellite images.

  9. Measurement of Surface Displacement and Deformation of Mass Movements Using Least Squares Matching of Repeat High Resolution Satellite and Aerial Images

    Directory of Open Access Journals (Sweden)

    Misganu Debella-Gilo

    2012-01-01

    Full Text Available Displacement and deformation are fundamental measures of Earth surface mass movements such as glacier flow, rockglacier creep and rockslides. Ground-based methods of monitoring such mass movements can be costly, time consuming and limited in spatial and temporal coverage. Remote sensing techniques, here matching of repeat optical images, are increasingly used to obtain displacement and deformation fields. Strain rates are usually computed in a post-processing step based on the gradients of the measured velocity field. This study explores the potential of automatically and directly computing velocity, rotation and strain rates on Earth surface mass movements simultaneously from the matching positions and the parameters of the geometric transformation models using the least squares matching (LSM approach. The procedures are exemplified using bi-temporal high resolution satellite and aerial images of glacier flow, rockglacier creep and land sliding. The results show that LSM matches the images and computes longitudinal strain rates, transverse strain rates and shear strain rates reliably with mean absolute deviations in the order of 10−4 (one level of significance below the measured values as evaluated on stable grounds. The LSM also improves the accuracy of displacement estimation of the pixel-precision normalized cross-correlation by over 90% under ideal (simulated circumstances and by about 25% for real multi-temporal images of mass movements.

  10. Flood-threat zoning map of the urban area of Chocó (Quibdó. A study based on interpreting radar, satellite and aerial photograph images

    Directory of Open Access Journals (Sweden)

    Zamir Maturana Córdoba

    2010-04-01

    Full Text Available A zoning map of areas which flood due to the Atrato River and its tributaries (the Cabí, Caraño and Yesca over-flowing in the urban area of Chocó (Quibdo was drawn up to be used by aid authorities and Quibdó city as a planning and control tool. This research relied on CIAF (Centro Interamericano de Fotointerpretación support and assessment. This entity is a subsidiary institution of the Instituto Geográfico Agustín Codazzi which provided their installations and the required geographical material. This research was initially based on interpreting radar (INTERA, satellite (LANDSAT and aerial photographic images; this was verified by field verification of the in-terpreted data. Other variables such as climatic, geological, temperature, topographic conditions, historic and hydrological series and facts regarding the region were studied as additional information required for drawing conclusions. Aerial photographs provided the most reliable images due to their scales, quantity and quality and the date of when they were taken. Radar images (INTERA were also important when visually analysing a sector’s topography as they were produced by an active microwave sensor (totally eliminating climatic obstacles. On the contrary, satellite images did not have great relevance due to the amount of clouds hampering any kind of analysis. Complementing these results, a calibration curve for analysing this section’s maximum flow values was based on historical series data regarding the Atrato River’s flows and maximum levels recorded at the Quibdo hydrographical station and the river-bed’s cross-section. Implications that the river would overflow or has over-flowed were statistically estimated on these results, thereby setting the limits (supported by cartographic data for the corresponding areas at risk of flooding. A map marking areas at risk of flooding in the urban zone of Quibdó was then designed and a document prepared concluding that

  11. Groupwise registration of aerial images

    OpenAIRE

    Arandjelovic, Ognjen; Pham, Duc-Son; Venkatesh, Svetha

    2015-01-01

    This paper addresses the task of time separated aerial image registration. The ability to solve this problem accurately and reliably is important for a variety of subsequent image understanding applications. The principal challenge lies in the extent and nature of transient appearance variation that a land area can undergo, such as that caused by the change in illumination conditions, seasonal variations, or the occlusion by non-persistent objects (people, cars). Our work introduces several n...

  12. COMPARATIVE ASSESSMENT OF VERY HIGH RESOLUTION SATELLITE AND AERIAL ORTHOIMAGERY

    Directory of Open Access Journals (Sweden)

    P. Agrafiotis

    2015-03-01

    Full Text Available This paper aims to assess the accuracy and radiometric quality of orthorectified high resolution satellite imagery from Pleiades-1B satellites through a comparative evaluation of their quantitative and qualitative properties. A Pleiades-B1 stereopair of high resolution images taken in 2013, two adjacent GeoEye-1 stereopairs from 2011 and aerial orthomosaic (LSO provided by NCMA S.A (Hellenic Cadastre from 2007 have been used for the comparison tests. As control dataset orthomosaic from aerial imagery provided also by NCMA S.A (0.25m GSD from 2012 was selected. The process for DSM and orthoimage production was performed using commercial digital photogrammetric workstations. The two resulting orthoimages and the aerial orthomosaic (LSO were relatively and absolutely evaluated for their quantitative and qualitative properties. Test measurements were performed using the same check points in order to establish their accuracy both as far as the single point coordinates as well as their distances are concerned. Check points were distributed according to JRC Guidelines for Best Practice and Quality Checking of Ortho Imagery and NSSDA standards while areas with different terrain relief and land cover were also included. The tests performed were based also on JRC and NSSDA accuracy standards. Finally, tests were carried out in order to assess the radiometric quality of the orthoimagery. The results are presented with a statistical analysis and they are evaluated in order to present the merits and demerits of the imaging sensors involved for orthoimage production. The results also serve for a critical approach for the usability and cost efficiency of satellite imagery for the production of Large Scale Orthophotos.

  13. Comparative Assessment of Very High Resolution Satellite and Aerial Orthoimagery

    Science.gov (United States)

    Agrafiotis, P.; Georgopoulos, A.

    2015-03-01

    This paper aims to assess the accuracy and radiometric quality of orthorectified high resolution satellite imagery from Pleiades-1B satellites through a comparative evaluation of their quantitative and qualitative properties. A Pleiades-B1 stereopair of high resolution images taken in 2013, two adjacent GeoEye-1 stereopairs from 2011 and aerial orthomosaic (LSO) provided by NCMA S.A (Hellenic Cadastre) from 2007 have been used for the comparison tests. As control dataset orthomosaic from aerial imagery provided also by NCMA S.A (0.25m GSD) from 2012 was selected. The process for DSM and orthoimage production was performed using commercial digital photogrammetric workstations. The two resulting orthoimages and the aerial orthomosaic (LSO) were relatively and absolutely evaluated for their quantitative and qualitative properties. Test measurements were performed using the same check points in order to establish their accuracy both as far as the single point coordinates as well as their distances are concerned. Check points were distributed according to JRC Guidelines for Best Practice and Quality Checking of Ortho Imagery and NSSDA standards while areas with different terrain relief and land cover were also included. The tests performed were based also on JRC and NSSDA accuracy standards. Finally, tests were carried out in order to assess the radiometric quality of the orthoimagery. The results are presented with a statistical analysis and they are evaluated in order to present the merits and demerits of the imaging sensors involved for orthoimage production. The results also serve for a critical approach for the usability and cost efficiency of satellite imagery for the production of Large Scale Orthophotos.

  14. Automated Orientation of Aerial Images

    DEFF Research Database (Denmark)

    Høhle, Joachim

    2002-01-01

    Methods for automated orientation of aerial images are presented. They are based on the use of templates, which are derived from existing databases, and area-based matching. The characteristics of available database information and the accuracy requirements for map compilation and orthoimage...... production are discussed on the example of Denmark. Details on the developed methods for interior and exterior orientation are described. Practical examples like the measurement of réseau images, updating of topographic databases and renewal of orthoimages are used to prove the feasibility of the developed...

  15. Marker Detection in Aerial Images

    KAUST Repository

    Alharbi, Yazeed

    2017-04-09

    The problem that the thesis is trying to solve is the detection of small markers in high-resolution aerial images. Given a high-resolution image, the goal is to return the pixel coordinates corresponding to the center of the marker in the image. The marker has the shape of two triangles sharing a vertex in the middle, and it occupies no more than 0.01% of the image size. An improvement on the Histogram of Oriented Gradients (HOG) is proposed, eliminating the majority of baseline HOG false positives for marker detection. The improvement is guided by the observation that standard HOG description struggles to separate markers from negatives patches containing an X shape. The proposed method alters intensities with the aim of altering gradients. The intensity-dependent gradient alteration leads to more separation between filled and unfilled shapes. The improvement is used in a two-stage algorithm to achieve high recall and high precision in detection of markers in aerial images. In the first stage, two classifiers are used: one to quickly eliminate most of the uninteresting parts of the image, and one to carefully select the marker among the remaining interesting regions. Interesting regions are selected by scanning the image with a fast classifier trained on the HOG features of markers in all rotations and scales. The next classifier is more precise and uses our method to eliminate the majority of the false positives of standard HOG. In the second stage, detected markers are tracked forward and backward in time. Tracking is needed to detect extremely blurred or distorted markers that are missed by the previous stage. The algorithm achieves 94% recall with minimal user guidance. An average of 30 guesses are given per image; the user verifies for each whether it is a marker or not. The brute force approach would return 100,000 guesses per image.

  16. Rectification of aerial images using piecewise linear transformation

    Science.gov (United States)

    Liew, L. H.; Lee, B. Y.; Wang, Y. C.; Cheah, W. S.

    2014-02-01

    Aerial images are widely used in various activities by providing visual records. This type of remotely sensed image is helpful in generating digital maps, managing ecology, monitoring crop growth and region surveying. Such images could provide insight into areas of interest that have lower altitude, particularly in regions where optical satellite imaging is prevented due to cloudiness. Aerial images captured using a non-metric cameras contain real details of the images as well as unexpected distortions. Distortions would affect the actual length, direction and shape of objects in the images. There are many sources that could cause distortions such as lens, earth curvature, topographic relief and the attitude of the aircraft that is used to carry the camera. These distortions occur differently, collectively and irregularly in the entire image. Image rectification is an essential image pre-processing step to eliminate or at least reduce the effect of distortions. In this paper, a non-parametric approach with piecewise linear transformation is investigated in rectifying distorted aerial images. The non-parametric approach requires a set of corresponding control points obtained from a reference image and a distorted image. The corresponding control points are then applied with piecewise linear transformation as geometric transformation. Piecewise linear transformation divides the image into regions by triangulation. Different linear transformations are employed separately to triangular regions instead of using a single transformation as the rectification model for the entire image. The result of rectification is evaluated using total root mean square error (RMSE). Experiments show that piecewise linear transformation could assist in improving the limitation of using global transformation to rectify images.

  17. A surge of Perseibreen, Svalbard, examined using aerial photography and ASTER high resolution satellite imagery

    OpenAIRE

    Dowdeswell, Julian A.; Benham, Toby J.

    2003-01-01

    The identification of surge activity is important in assessing the duration of the active and quiescent phases of the surge cycle of Svalbard glaciers. Satellite and aerial photographic images are used to identify and describe the form and flow of Perseibreen, a valley glacier of 59 km2 on the east coast of Spitsbergen. Heavy surface crevassing and a steep ice front, indicative of surge activity, were first observed on Perseibreen in April 2002. Examination of high resolution (15 m) Advanced ...

  18. OVERVIEW OF MODERN RESEARCH OF LANDSLIDES ACCORDING TO AERIAL AND SATELLITE IMAGERY

    Directory of Open Access Journals (Sweden)

    K. M. Lyapishev

    2015-01-01

    Full Text Available This article is an overview of researches of landslides using remote sensing methods such as aerial photography, satellite images, radar interferometry, and their combination with the use of GIS technology. Modern methods of investigation of landslides are very diverse. The authors propose different approaches to the identification, classification and monitoring of landslides. Data analysis techniques can help in creating more sophisticated approach to the analysis of landslides.

  19. Orientation Strategies for Aerial Oblique Images

    Science.gov (United States)

    Wiedemann, A.; Moré, J.

    2012-07-01

    Oblique aerial images become more and more distributed to fill the gap between vertical aerial images and mobile mapping systems. Different systems are on the market. For some applications, like texture mapping, precise orientation data are required. One point is the stable interior orientation, which can be achieved by stable camera systems, the other a precise exterior orientation. A sufficient exterior orientation can be achieved by a large effort in direct sensor orientation, whereas minor errors in the angles have a larger effect than in vertical imagery. The more appropriate approach is by determine the precise orientation parameters by photogrammetric methods using an adapted aerial triangulation. Due to the different points of view towards the object the traditional aerotriangulation matching tools fail, as they produce a bunch of blunders and require a lot of manual work to achieve a sufficient solution. In this paper some approaches are discussed and results are presented for the most promising approaches. We describe a single step approach with an aerotriangulation using all available images; a two step approach with an aerotriangulation only of the vertical images plus a mathematical transformation of the oblique images using the oblique cameras excentricity; and finally the extended functional model for a bundle block adjustment considering the mechanical connection between vertical and oblique images. Beside accuracy also other aspects like efficiency and required manual work have to be considered.

  20. Antarctica: measuring glacier velocity from satellite images.

    Science.gov (United States)

    Lucchitta, B K; Ferguson, H M

    1986-11-28

    Many Landsat images of Antarctica show distinctive flow and crevasse features in the floating part of ice streams and outlet glaciers immediately below their grounding zones. Some of the features, which move with the glacier or ice stream, remain visible over many years and thus allow time-lapse measurements of ice velocities. Measurements taken from Landsat images of features on Byrd Glacier agree well with detailed ground and aerial observations. The satellite-image technique thus offers a rapid and cost-effective method of obtaining average velocities, to a first order of accuracy, of many ice streams and outlet glaciers near their termini.

  1. Automated Archiving of Archaeological Aerial Images

    Directory of Open Access Journals (Sweden)

    Michael Doneus

    2016-03-01

    Full Text Available The main purpose of any aerial photo archive is to allow quick access to images based on content and location. Therefore, next to a description of technical parameters and depicted content, georeferencing of every image is of vital importance. This can be done either by identifying the main photographed object (georeferencing of the image content or by mapping the center point and/or the outline of the image footprint. The paper proposes a new image archiving workflow. The new pipeline is based on the parameters that are logged by a commercial, but cost-effective GNSS/IMU solution and processed with in-house-developed software. Together, these components allow one to automatically geolocate and rectify the (oblique aerial images (by a simple planar rectification using the exterior orientation parameters and to retrieve their footprints with reasonable accuracy, which is automatically stored as a vector file. The data of three test flights were used to determine the accuracy of the device, which turned out to be better than 1° for roll and pitch (mean between 0.0 and 0.21 with a standard deviation of 0.17–0.46 and better than 2.5° for yaw angles (mean between 0.0 and −0.14 with a standard deviation of 0.58–0.94. This turned out to be sufficient to enable a fast and almost automatic GIS-based archiving of all of the imagery.

  2. First results for an image processing workflow for hyperspatial imagery acquired with a low-cost unmanned aerial vehicle (UAV).

    Science.gov (United States)

    Very high-resolution images from unmanned aerial vehicles (UAVs) have great potential for use in rangeland monitoring and assessment, because the imagery fills the gap between ground-based observations and remotely sensed imagery from aerial or satellite sensors. However, because UAV imagery is ofte...

  3. Very high resolution satellite data: New challenges in image analysis

    Digital Repository Service at National Institute of Oceanography (India)

    Sathe, P.V.; Muraleedharan, P.M.

    with the exception that a ground-based view covers the entire optical range from 400 to 700 nm while satellite images will be wavelength-specific. Although the images will not surpass details observed by a human eye, they will, in principle, be comparable with aerial...

  4. Geostationary Satellite (GOES) Images

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Visible and Infrared satellite imagery taken from radiometer instruments on SMS (ATS) and GOES satellites in geostationary orbit. These satellites produced...

  5. Combining satellite, aerial and ground measurements to assess forest carbon stocks in Democratic Republic of Congo

    Science.gov (United States)

    Beaumont, Benjamin; Bouvy, Alban; Stephenne, Nathalie; Mathoux, Pierre; Bastin, Jean-François; Baudot, Yves; Akkermans, Tom

    2015-04-01

    Monitoring tropical forest carbon stocks changes has been a rising topic in the recent years as a result of REDD+ mechanisms negotiations. Such monitoring will be mandatory for each project/country willing to benefit from these financial incentives in the future. Aerial and satellite remote sensing technologies offer cost advantages in implementing large scale forest inventories. Despite the recent progress made in the use of airborne LiDAR for carbon stocks estimation, no widely operational and cost effective method has yet been delivered for central Africa forest monitoring. Within the Maï Ndombe region of Democratic Republic of Congo, the EO4REDD project develops a method combining satellite, aerial and ground measurements. This combination is done in three steps: [1] mapping and quantifying forest cover changes using an object-based semi-automatic change detection (deforestation and forest degradation) methodology based on very high resolution satellite imagery (RapidEye), [2] developing an allometric linear model for above ground biomass measurements based on dendrometric parameters (tree crown areas and heights) extracted from airborne stereoscopic image pairs and calibrated using ground measurements of individual trees on a data set of 18 one hectare plots and [3] relating these two products to assess carbon stocks changes at a regional scale. Given the high accuracies obtained in [1] (> 80% for deforestation and 77% for forest degradation) and the suitable, but still to be improved with a larger calibrating sample, model (R² of 0.7) obtained in [2], EO4REDD products can be seen as a valid and replicable option for carbon stocks monitoring in tropical forests. Further improvements are planned to strengthen the cost effectiveness value and the REDD+ suitability in the second phase of EO4REDD. This second phase will include [A] specific model developments per forest type; [B] measurements of afforestation, reforestation and natural regeneration processes and

  6. Rays in the northern Gulf of Mexico: Aerial Survey and Satellite Telemetry 2008-2012 (NCEI Accession 0129495)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The dataset contains distribution and abundance data for rays in the Gulf of Mexico collected through aerial surveys and satellite telemetry. Aerial survey data...

  7. An algorithm for approximate rectification of digital aerial images

    Science.gov (United States)

    High-resolution aerial photography is one of the most valuable tools available for managing extensive landscapes. With recent advances in digital camera technology, computer hardware, and software, aerial photography is easier to collect, store, and transfer than ever before. Images can be automa...

  8. Accurate Estimation of Orientation Parameters of Uav Images Through Image Registration with Aerial Oblique Imagery

    Science.gov (United States)

    Onyango, F. A.; Nex, F.; Peter, M. S.; Jende, P.

    2017-05-01

    Unmanned Aerial Vehicles (UAVs) have gained popularity in acquiring geotagged, low cost and high resolution images. However, the images acquired by UAV-borne cameras often have poor georeferencing information, because of the low quality on-board Global Navigation Satellite System (GNSS) receiver. In addition, lightweight UAVs have a limited payload capacity to host a high quality on-board Inertial Measurement Unit (IMU). Thus, orientation parameters of images acquired by UAV-borne cameras may not be very accurate. Poorly georeferenced UAV images can be correctly oriented using accurately oriented airborne images capturing a similar scene by finding correspondences between the images. This is not a trivial task considering the image pairs have huge variations in scale, perspective and illumination conditions. This paper presents a procedure to successfully register UAV and aerial oblique imagery. The proposed procedure implements the use of the AKAZE interest operator for feature extraction in both images. Brute force is implemented to find putative correspondences and later on Lowe's ratio test (Lowe, 2004) is used to discard a significant number of wrong matches. In order to filter out the remaining mismatches, the putative correspondences are used in the computation of multiple homographies, which aid in the reduction of outliers significantly. In order to increase the number and improve the quality of correspondences, the impact of pre-processing the images using the Wallis filter (Wallis, 1974) is investigated. This paper presents the test results of different scenarios and the respective accuracies compared to a manual registration of the finally computed fundamental and essential matrices that encode the orientation parameters of the UAV images with respect to the aerial images.

  9. ACCURATE ESTIMATION OF ORIENTATION PARAMETERS OF UAV IMAGES THROUGH IMAGE REGISTRATION WITH AERIAL OBLIQUE IMAGERY

    Directory of Open Access Journals (Sweden)

    F. A. Onyango

    2017-05-01

    Full Text Available Unmanned Aerial Vehicles (UAVs have gained popularity in acquiring geotagged, low cost and high resolution images. However, the images acquired by UAV-borne cameras often have poor georeferencing information, because of the low quality on-board Global Navigation Satellite System (GNSS receiver. In addition, lightweight UAVs have a limited payload capacity to host a high quality on-board Inertial Measurement Unit (IMU. Thus, orientation parameters of images acquired by UAV-borne cameras may not be very accurate. Poorly georeferenced UAV images can be correctly oriented using accurately oriented airborne images capturing a similar scene by finding correspondences between the images. This is not a trivial task considering the image pairs have huge variations in scale, perspective and illumination conditions. This paper presents a procedure to successfully register UAV and aerial oblique imagery. The proposed procedure implements the use of the AKAZE interest operator for feature extraction in both images. Brute force is implemented to find putative correspondences and later on Lowe’s ratio test (Lowe, 2004 is used to discard a significant number of wrong matches. In order to filter out the remaining mismatches, the putative correspondences are used in the computation of multiple homographies, which aid in the reduction of outliers significantly. In order to increase the number and improve the quality of correspondences, the impact of pre-processing the images using the Wallis filter (Wallis, 1974 is investigated. This paper presents the test results of different scenarios and the respective accuracies compared to a manual registration of the finally computed fundamental and essential matrices that encode the orientation parameters of the UAV images with respect to the aerial images.

  10. Aerial Images of Alaska's Arctic Coastal Plain; 1974-1979

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — This dataset is comprised of 10 aerial images of three different study areas on Alaska's Arctic Coastal Plain flown by NASA in 1974, 1977, 1979 and obtained from the...

  11. Aerial Triangulation Close-range Images with Dual Quaternion

    Directory of Open Access Journals (Sweden)

    SHENG Qinghong

    2015-05-01

    Full Text Available A new method for the aerial triangulation of close-range images based on dual quaternion is presented. Using dual quaternion to represent the spiral screw motion of the beam in the space, the real part of dual quaternion represents the angular elements of all the beams in the close-range area networks, the real part and the dual part of dual quaternion represents the line elements corporately. Finally, an aerial triangulation adjustment model based on dual quaternion is established, and the elements of interior orientation and exterior orientation and the object coordinates of the ground points are calculated. Real images and large attitude angle simulated images are selected to run the experiments of aerial triangulation. The experimental results show that the new method for the aerial triangulation of close-range images based on dual quaternion can obtain higher accuracy.

  12. Vibration factors impact analysis on aerial film camera imaging quality

    Science.gov (United States)

    Xie, Jun; Han, Wei; Xu, Zhonglin; Tan, Haifeng; Yang, Mingquan

    2017-08-01

    Aerial film camera can acquire ground target image information advantageous, but meanwhile the change of aircraft attitude, the film features and the work of camera inside system could result in a vibration which could depress the image quality greatly. This paper presented a design basis of vibration mitigation stabilized platform based on the vibration characteristic of the aerial film camera and indicated the application analysis that stabilized platform could support aerial camera to realize the shoot demand of multi-angle and large scale. According to the technique characteristics of stabilized platform, the development direction are high precision, more agility, miniaturization and low power.

  13. Use of simulated neural networks of aerial image classification

    Science.gov (United States)

    Medina, Frances I.; Vasquez, Ramon

    1991-01-01

    The utility of one layer neural network in aerial image classification is examined. The network was trained with the delta rule. This method was shown to be useful as a classifier in aerial images with good resolution. It is fast, it is easy to implement, because it is distribution-free, nothing about statistical distribution of the data is needed, and it is very efficient as a boundary detector.

  14. Biogeography based Satellite Image Classification

    CERN Document Server

    Panchal, V K; Kaur, Navdeep; Kundra, Harish

    2009-01-01

    Biogeography is the study of the geographical distribution of biological organisms. The mindset of the engineer is that we can learn from nature. Biogeography Based Optimization is a burgeoning nature inspired technique to find the optimal solution of the problem. Satellite image classification is an important task because it is the only way we can know about the land cover map of inaccessible areas. Though satellite images have been classified in past by using various techniques, the researchers are always finding alternative strategies for satellite image classification so that they may be prepared to select the most appropriate technique for the feature extraction task in hand. This paper is focused on classification of the satellite image of a particular land cover using the theory of Biogeography based Optimization. The original BBO algorithm does not have the inbuilt property of clustering which is required during image classification. Hence modifications have been proposed to the original algorithm and...

  15. Advanced Image Processing of Aerial Imagery

    Science.gov (United States)

    Woodell, Glenn; Jobson, Daniel J.; Rahman, Zia-ur; Hines, Glenn

    2006-01-01

    Aerial imagery of the Earth is an invaluable tool for the assessment of ground features, especially during times of disaster. Researchers at the NASA Langley Research Center have developed techniques which have proven to be useful for such imagery. Aerial imagery from various sources, including Langley's Boeing 757 Aries aircraft, has been studied extensively. This paper discusses these studies and demonstrates that better-than-observer imagery can be obtained even when visibility is severely compromised. A real-time, multi-spectral experimental system will be described and numerous examples will be shown.

  16. Shadow imaging of geosynchronous satellites

    Science.gov (United States)

    Douglas, Dennis Michael

    Geosynchronous (GEO) satellites are essential for modern communication networks. If communication to a GEO satellite is lost and a malfunction occurs upon orbit insertion such as a solar panel not deploying there is no direct way to observe it from Earth. Due to the GEO orbit distance of ~36,000 km from Earth's surface, the Rayleigh criteria dictates that a 14 m telescope is required to conventionally image a satellite with spatial resolution down to 1 m using visible light. Furthermore, a telescope larger than 30 m is required under ideal conditions to obtain spatial resolution down to 0.4 m. This dissertation evaluates a method for obtaining high spatial resolution images of GEO satellites from an Earth based system by measuring the irradiance distribution on the ground resulting from the occultation of the satellite passing in front of a star. The representative size of a GEO satellite combined with the orbital distance results in the ground shadow being consistent with a Fresnel diffraction pattern when observed at visible wavelengths. A measurement of the ground shadow irradiance is used as an amplitude constraint in a Gerchberg-Saxton phase retrieval algorithm that produces a reconstruction of the satellite's 2D transmission function which is analogous to a reverse contrast image of the satellite. The advantage of shadow imaging is that a terrestrial based redundant set of linearly distributed inexpensive small telescopes, each coupled to high speed detectors, is a more effective resolved imaging system for GEO satellites than a very large telescope under ideal conditions. Modeling and simulation efforts indicate sub-meter spatial resolution can be readily achieved using collection apertures of less than 1 meter in diameter. A mathematical basis is established for the treatment of the physical phenomena involved in the shadow imaging process. This includes the source star brightness and angular extent, and the diffraction of starlight from the satellite

  17. Assessing the performance of aerial image point cloud and spectral metrics in predicting boreal forest canopy cover

    Science.gov (United States)

    Melin, M.; Korhonen, L.; Kukkonen, M.; Packalen, P.

    2017-07-01

    Canopy cover (CC) is a variable used to describe the status of forests and forested habitats, but also the variable used primarily to define what counts as a forest. The estimation of CC has relied heavily on remote sensing with past studies focusing on satellite imagery as well as Airborne Laser Scanning (ALS) using light detection and ranging (lidar). Of these, ALS has been proven highly accurate, because the fraction of pulses penetrating the canopy represents a direct measurement of canopy gap percentage. However, the methods of photogrammetry can be applied to produce point clouds fairly similar to airborne lidar data from aerial images. Currently there is little information about how well such point clouds measure canopy density and gaps. The aim of this study was to assess the suitability of aerial image point clouds for CC estimation and compare the results with those obtained using spectral data from aerial images and Landsat 5. First, we modeled CC for n = 1149 lidar plots using field-measured CCs and lidar data. Next, this data was split into five subsets in north-south direction (y-coordinate). Finally, four CC models (AerialSpectral, AerialPointcloud, AerialCombi (spectral + pointcloud) and Landsat) were created and they were used to predict new CC values to the lidar plots, subset by subset, using five-fold cross validation. The Landsat and AerialSpectral models performed with RMSEs of 13.8% and 12.4%, respectively. AerialPointcloud model reached an RMSE of 10.3%, which was further improved by the inclusion of spectral data; RMSE of the AerialCombi model was 9.3%. We noticed that the aerial image point clouds managed to describe only the outermost layer of the canopy and missed the details in lower canopy, which was resulted in weak characterization of the total CC variation, especially in the tails of the data.

  18. Wafer weak point detection based on aerial images or WLCD

    Science.gov (United States)

    Ning, Guoxiang; Philipp, Peter; Litt, Lloyd C.; Ackmann, Paul; Crell, Christian; Chen, Norman

    2015-10-01

    Aerial image measurement is a key technique for model based optical proximity correction (OPC) verification. Actual aerial images obtained by AIMS (aerial image measurement system) or WLCD (wafer level critical dimension) can detect printed wafer weak point structures in advance of wafer exposure and defect inspection. Normally, the potential wafer weak points are determined based on optical rule check (ORC) simulation in advance. However, the correlation to real wafer weak points is often not perfect due to the contribution of mask three dimension (M3D) effects, actual mask errors, and scanner lens effects. If the design weak points can accurately be detected in advance, it will reduce the wafer fab cost and improve cycle time. WLCD or AIMS tools are able to measure the aerial images CD and bossung curve through focus window. However, it is difficult to detect the wafer weak point in advance without defining selection criteria. In this study, wafer weak points sensitive to mask mean-to-nominal values are characterized for a process with very high MEEF (normally more than 4). Aerial image CD uses fixed threshold to detect the wafer weak points. By using WLCD through threshold and focus window, the efficiency of wafer weak point detection is also demonstrated. A novel method using contrast range evaluation is shown in the paper. Use of the slope of aerial images for more accurate detection of the wafer weak points using WLCD is also discussed. The contrast range can also be used to detect the wafer weak points in advance. Further, since the mean to nominal of the reticle contributes to the effective contrast range in a high MEEF area this work shows that control of the mask error is critical for high MEEF layers such as poly, active and metal layers. Wafer process based weak points that cannot be detected by wafer lithography CD or WLCD will be discussed.

  19. Smoothing of Fused Spectral Consistent Satellite Images

    DEFF Research Database (Denmark)

    Sveinsson, Johannes; Aanæs, Henrik; Benediktsson, Jon Atli

    2006-01-01

    on satellite data. Additionally, most conventional methods are loosely connected to the image forming physics of the satellite image, giving these methods an ad hoc feel. Vesteinsson et al. (2005) proposed a method of fusion of satellite images that is based on the properties of imaging physics...

  20. An automatic high precision registration method between large area aerial images and aerial light detection and ranging data

    OpenAIRE

    Du, Q.; Xie, D; Sun, Y.

    2015-01-01

    The integration of digital aerial photogrammetry and Light Detetion And Ranging (LiDAR) is an inevitable trend in Surveying and Mapping field. We calculate the external orientation elements of images which identical with LiDAR coordinate to realize automatic high precision registration between aerial images and LiDAR data. There are two ways to calculate orientation elements. One is single image spatial resection using image matching 3D points that registered to LiDAR. The other o...

  1. Augmenting camera images for operators of Unmanned Aerial Vehicles

    NARCIS (Netherlands)

    Veltman, J.A.; Oving, A.B.

    2003-01-01

    The manual control of the camera of an unmanned aerial vehicle (UAV) can be difficult due to several factors such as 1) time delays between steering input and changes of the monitor content, 2) low update rates of the camera images and 3) lack of situation awareness due to the remote position of the

  2. Aerial Images of Alaska's Arctic Coastal Plain; 1948, 1949

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — This dataset is comprised of 36 black and white 9x9 inch aerial images of four different study areas on Alaska's Arctic Coastal Plain taken between 1948-1949 and...

  3. Aerial Image of Alaska's Arctic Coastal Plain; 1955

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — This dataset is comprised of a single aerial image of a single area on Alaska's Arctic Coastal Plain taken on 21 June 1955 by the U.S. Air Force and obtained from...

  4. A novel fast target tracking method for UAV aerial image

    Directory of Open Access Journals (Sweden)

    Jianfang Liu

    2017-06-01

    Full Text Available Unmanned aerial vehicles (UAV are able to achieve autonomous flight without drivers, and UAV has been a key tool to extract space data. Therefore, how to detect the trajectories of targets from UAV aerial image sequences is of great importance. Because local features are suitable to detect target tracking, we exploit scaleinvariant feature transform (SIFT features to describe the interesting keypoints of targets. The main innovation of this paper is to utilize Multiple hypothesis tracking (MHT algorithm to track an object (target in a series of image sequences. Particularly, we develop a MHT framework based on a multidimensional assignment formulation and a sliding time window policy. To obtain target tracking from UAV aerial image sequences, three steps should be done, that is, 1 Breaking each track set into tracklet at a specific time, 2 Estimating the association cost of each track set, 3 Merging trajectory fragments to a longer one iteratively. Finally, we collect several UAV aerial image sequences with different target density to construct a dataset, and experimental results demonstrate the effectiveness of the proposed algorithm.

  5. A novel fast target tracking method for UAV aerial image

    Science.gov (United States)

    Jianfang, Liu; Hao, Zheng; Jingli, Gao

    2017-06-01

    Unmanned aerial vehicles (UAV) are able to achieve autonomous flight without drivers, and UAV has been a key tool to extract space data. Therefore, how to detect the trajectories of targets from UAV aerial image sequences is of great importance. Because local features are suitable to detect target tracking, we exploit scaleinvariant feature transform (SIFT) features to describe the interesting keypoints of targets. The main innovation of this paper is to utilize Multiple hypothesis tracking (MHT) algorithm to track an object (target) in a series of image sequences. Particularly, we develop a MHT framework based on a multidimensional assignment formulation and a sliding time window policy. To obtain target tracking from UAV aerial image sequences, three steps should be done, that is, 1) Breaking each track set into tracklet at a specific time, 2) Estimating the association cost of each track set, 3) Merging trajectory fragments to a longer one iteratively. Finally, we collect several UAV aerial image sequences with different target density to construct a dataset, and experimental results demonstrate the effectiveness of the proposed algorithm.

  6. Augmenting camera images for operators of Unmanned Aerial Vehicles

    NARCIS (Netherlands)

    Veltman, J.A.; Oving, A.B.

    2003-01-01

    The manual control of the camera of an unmanned aerial vehicle (UAV) can be difficult due to several factors such as 1) time delays between steering input and changes of the monitor content, 2) low update rates of the camera images and 3) lack of situation awareness due to the remote position of the

  7. An improved dehazing algorithm of aerial high-definition image

    Science.gov (United States)

    Jiang, Wentao; Ji, Ming; Huang, Xiying; Wang, Chao; Yang, Yizhou; Li, Tao; Wang, Jiaoying; Zhang, Ying

    2016-01-01

    For unmanned aerial vehicle(UAV) images, the sensor can not get high quality images due to fog and haze weather. To solve this problem, An improved dehazing algorithm of aerial high-definition image is proposed. Based on the model of dark channel prior, the new algorithm firstly extracts the edges from crude estimated transmission map and expands the extracted edges. Then according to the expended edges, the algorithm sets a threshold value to divide the crude estimated transmission map into different areas and makes different guided filter on the different areas compute the optimized transmission map. The experimental results demonstrate that the performance of the proposed algorithm is substantially the same as the one based on dark channel prior and guided filter. The average computation time of the new algorithm is around 40% of the one as well as the detection ability of UAV image is improved effectively in fog and haze weather.

  8. CMOS Imaging Sensor Technology for Aerial Mapping Cameras

    Science.gov (United States)

    Neumann, Klaus; Welzenbach, Martin; Timm, Martin

    2016-06-01

    In June 2015 Leica Geosystems launched the first large format aerial mapping camera using CMOS sensor technology, the Leica DMC III. This paper describes the motivation to change from CCD sensor technology to CMOS for the development of this new aerial mapping camera. In 2002 the DMC first generation was developed by Z/I Imaging. It was the first large format digital frame sensor designed for mapping applications. In 2009 Z/I Imaging designed the DMC II which was the first digital aerial mapping camera using a single ultra large CCD sensor to avoid stitching of smaller CCDs. The DMC III is now the third generation of large format frame sensor developed by Z/I Imaging and Leica Geosystems for the DMC camera family. It is an evolution of the DMC II using the same system design with one large monolithic PAN sensor and four multi spectral camera heads for R,G, B and NIR. For the first time a 391 Megapixel large CMOS sensor had been used as PAN chromatic sensor, which is an industry record. Along with CMOS technology goes a range of technical benefits. The dynamic range of the CMOS sensor is approx. twice the range of a comparable CCD sensor and the signal to noise ratio is significantly better than with CCDs. Finally results from the first DMC III customer installations and test flights will be presented and compared with other CCD based aerial sensors.

  9. Accuracy assessment of topographic mapping using UAV image integrated with satellite images

    Science.gov (United States)

    Azmi, S. M.; Ahmad, Baharin; Ahmad, Anuar

    2014-02-01

    Unmanned Aerial Vehicle or UAV is extensively applied in various fields such as military applications, archaeology, agriculture and scientific research. This study focuses on topographic mapping and map updating. UAV is one of the alternative ways to ease the process of acquiring data with lower operating costs, low manufacturing and operational costs, plus it is easy to operate. Furthermore, UAV images will be integrated with QuickBird images that are used as base maps. The objective of this study is to make accuracy assessment and comparison between topographic mapping using UAV images integrated with aerial photograph and satellite image. The main purpose of using UAV image is as a replacement for cloud covered area which normally exists in aerial photograph and satellite image, and for updating topographic map. Meanwhile, spatial resolution, pixel size, scale, geometric accuracy and correction, image quality and information contents are important requirements needed for the generation of topographic map using these kinds of data. In this study, ground control points (GCPs) and check points (CPs) were established using real time kinematic Global Positioning System (RTK-GPS) technique. There are two types of analysis that are carried out in this study which are quantitative and qualitative assessments. Quantitative assessment is carried out by calculating root mean square error (RMSE). The outputs of this study include topographic map and orthophoto. From this study, the accuracy of UAV image is ± 0.460 m. As conclusion, UAV image has the potential to be used for updating of topographic maps.

  10. An Aerial Image Mosaic Method Based on UAV Position and Attitude Information

    OpenAIRE

    CHENG Zhenggang; Zhang, Li

    2016-01-01

    As the existing methods for aerial image mosaic take high computational, a fast and effective algorithm based on the position and attitude information of unmanned aerial vehicles (UAV) is proposed. Firstly, the coordinates and attitude angles of UAV can be obtained by airborne GPS and inertial measurement unit (IMU), and each aerial image has a corresponding position and attitude information. The homography matrix between two aerial images with the positions and attitude information can be ca...

  11. BW Trained HMM based Aerial Image Segmentation

    OpenAIRE

    R Rajasree; J. Nalini; S C Ramesh

    2011-01-01

    Image segmentation is an essential preprocessing tread in a complicated and composite image dealing out algorithm. In segmenting arial image the expenditure of misclassification could depend on the factual group of pupils. In this paper, aggravated by modern advances in contraption erudition conjecture, I introduce a modus operandi to make light of the misclassification expenditure with class-dependent expenditure. The procedure assumes the hidden Markov model (HMM) which has been popularly u...

  12. Shadow Detection and Compensation for Color Aerial Images

    Institute of Scientific and Technical Information of China (English)

    WANG Shugen; GUO Zejin; LI Deren

    2003-01-01

    A method for shadow detection and compensation for color aerial images is presented. It is considered that the intensity value of each image pixel is the product of illumination function and ground object reflection, and the shadowed regions on the image are mainly caused by the short of illumination, so the information compensation for the shadowed regions should concentrate on the illumination adjustment of concerned area on the basis of the analysis of whole image. The shadow detection and compensation procedure proposed by this paper consists of four steps.

  13. USGS Global Visualization Viewer for Aerial and Satellite Data

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — Ten million archive images of the Earth's surface are available for immediate selection and free download via the USGS Earth Resources Observation and Science (EROS)...

  14. ACCURACY OF MEASUREMENTS IN OBLIQUE AERIAL IMAGES FOR URBAN ENVIRONMENT

    Directory of Open Access Journals (Sweden)

    W. Ostrowski

    2016-10-01

    Full Text Available Oblique aerial images have been a source of data for urban areas for several years. However, the accuracy of measurements in oblique images during this time has been limited to a single meter due to the use of direct -georeferencing technology and the underlying digital elevation model. Therefore, oblique images have been used mostly for visualization purposes. This situation changed in recent years as new methods, which allowed for a higher accuracy of exterior orientation, were developed. Current developments include the process of determining exterior orientation and the previous but still crucial process of tie point extraction. Progress in this area was shown in the ISPRS/EUROSDR Benchmark on Multi-Platform Photogrammetry and is also noticeable in the growing interest in the use of this kind of imagery. The higher level of accuracy in the orientation of oblique aerial images that has become possible in the last few years should result in a higher level of accuracy in the measurements of these types of images. The main goal of this research was to set and empirically verify the accuracy of measurements in oblique aerial images. The research focused on photogrammetric measurements composed of many images, which use a high overlap within an oblique dataset and different view angles. During the experiments, two series of images of urban areas were used. Both were captured using five DigiCam cameras in a Maltese cross configuration. The tilt angles of the oblique cameras were 45 degrees, and the position of the cameras during flight used a high grade GPS/INS navigation system. The orientation of the images was set using the Pix4D Mapper Pro software with both measurements of the in-flight camera position and the ground control points (measured with GPS RTK technology. To control the accuracy, check points were used (which were also measured with GPS RTK technology. As reference data for the whole study, an area of the city-based map was used

  15. Accuracy of Measurements in Oblique Aerial Images for Urban Environment

    Science.gov (United States)

    Ostrowski, W.

    2016-10-01

    Oblique aerial images have been a source of data for urban areas for several years. However, the accuracy of measurements in oblique images during this time has been limited to a single meter due to the use of direct -georeferencing technology and the underlying digital elevation model. Therefore, oblique images have been used mostly for visualization purposes. This situation changed in recent years as new methods, which allowed for a higher accuracy of exterior orientation, were developed. Current developments include the process of determining exterior orientation and the previous but still crucial process of tie point extraction. Progress in this area was shown in the ISPRS/EUROSDR Benchmark on Multi-Platform Photogrammetry and is also noticeable in the growing interest in the use of this kind of imagery. The higher level of accuracy in the orientation of oblique aerial images that has become possible in the last few years should result in a higher level of accuracy in the measurements of these types of images. The main goal of this research was to set and empirically verify the accuracy of measurements in oblique aerial images. The research focused on photogrammetric measurements composed of many images, which use a high overlap within an oblique dataset and different view angles. During the experiments, two series of images of urban areas were used. Both were captured using five DigiCam cameras in a Maltese cross configuration. The tilt angles of the oblique cameras were 45 degrees, and the position of the cameras during flight used a high grade GPS/INS navigation system. The orientation of the images was set using the Pix4D Mapper Pro software with both measurements of the in-flight camera position and the ground control points (measured with GPS RTK technology). To control the accuracy, check points were used (which were also measured with GPS RTK technology). As reference data for the whole study, an area of the city-based map was used. The archived results

  16. Integration, Testing, and Analysis of Multispectral Imager on Small Unmanned Aerial System for Skin Detection

    Science.gov (United States)

    2014-03-01

    INTEGRATION, TESTING, AND ANALYSIS OF MULTISPECTRAL IMAGER ON SMALL UNMANNED AERIAL SYSTEM FOR SKIN......12 2.5 Image Registration ................................................................................................16 2.6

  17. Preliminary analysis of the forest health state based on multispectral images acquired by Unmanned Aerial Vehicle

    Directory of Open Access Journals (Sweden)

    Czapski Paweł

    2015-09-01

    Full Text Available The main purpose of this publication is to present the current progress of the work associated with the use of a lightweight unmanned platforms for various environmental studies. Current development in information technology, electronics and sensors miniaturisation allows mounting multispectral cameras and scanners on unmanned aerial vehicle (UAV that could only be used on board aircraft and satellites. Remote Sensing Division in the Institute of Aviation carries out innovative researches using multisensory platform and lightweight unmanned vehicle to evaluate the health state of forests in Wielkopolska province. In this paper, applicability of multispectral images analysis acquired several times during the growing season from low altitude (up to 800m is presented. We present remote sensing indicators computed by our software and common methods for assessing state of trees health. The correctness of applied methods is verified using analysis of satellite scenes acquired by Landsat 8 OLI instrument (Operational Land Imager.

  18. Damage Degree Evaluation of Earthquake Area Using UAV Aerial Image

    Directory of Open Access Journals (Sweden)

    Jinhong Chen

    2016-01-01

    Full Text Available An Unmanned Aerial Vehicle (UAV system and its aerial image analysis method are developed to evaluate the damage degree of earthquake area. Both the single-rotor and the six-rotor UAVs are used to capture the visible light image of ground targets. Five types of typical ground targets are considered for the damage degree evaluation: the building, the road, the mountain, the riverway, and the vegetation. When implementing the image analysis, first the Image Quality Evaluation Metrics (IQEMs, that is, the image contrast, the image blur, and the image noise, are used to assess the imaging definition. Second, once the image quality is qualified, the Gray Level Cooccurrence Matrix (GLCM texture feature, the Tamura texture feature, and the Gabor wavelet texture feature are computed. Third, the Support Vector Machine (SVM classifier is employed to evaluate the damage degree. Finally, a new damage degree evaluation (DDE index is defined to assess the damage intensity of earthquake. Many experiment results have verified the correctness of proposed system and method.

  19. D City Transformations by Time Series of Aerial Images

    Science.gov (United States)

    Adami, A.

    2015-02-01

    Recent photogrammetric applications, based on dense image matching algorithms, allow to use not only images acquired by digital cameras, amateur or not, but also to recover the vast heritage of analogue photographs. This possibility opens up many possibilities in the use and enhancement of existing photos heritage. The research of the original figuration of old buildings, the virtual reconstruction of disappeared architectures and the study of urban development are some of the application areas that exploit the great cultural heritage of photography. Nevertheless there are some restrictions in the use of historical images for automatic reconstruction of buildings such as image quality, availability of camera parameters and ineffective geometry of image acquisition. These constrains are very hard to solve and it is difficult to discover good dataset in the case of terrestrial close range photogrammetry for the above reasons. Even the photographic archives of museums and superintendence, while retaining a wealth of documentation, have no dataset for a dense image matching approach. Compared to the vast collection of historical photos, the class of aerial photos meets both criteria stated above. In this paper historical aerial photographs are used with dense image matching algorithms to realize 3d models of a city in different years. The models can be used to study the urban development of the city and its changes through time. The application relates to the city centre of Verona, for which some time series of aerial photographs have been retrieved. The models obtained in this way allowed, right away, to observe the urban development of the city, the places of expansion and new urban areas. But a more interesting aspect emerged from the analytical comparison between models. The difference, as the Euclidean distance, between two models gives information about new buildings or demolitions. As considering accuracy it is necessary point out that the quality of final

  20. BW Trained HMM based Aerial Image Segmentation

    Directory of Open Access Journals (Sweden)

    R Rajasree

    2011-03-01

    Full Text Available Image segmentation is an essential preprocessing tread in a complicated and composite image dealing out algorithm. In segmenting arial image the expenditure of misclassification could depend on the factual group of pupils. In this paper, aggravated by modern advances in contraption erudition conjecture, I introduce a modus operandi to make light of the misclassification expenditure with class-dependent expenditure. The procedure assumes the hidden Markov model (HMM which has been popularly used for image segmentation in recent years. We represent all feasible HMM based segmenters (or classifiers as a set of points in the beneficiary operating characteristic (ROC space. optimizing HMM parameters is still an important and challenging work in automatic image segmentation research area. Usually the Baum-Welch (B-W Algorithm is used to calculate the HMM model parameters. However, the B-W algorithm uses an initial random guess of the parameters, therefore after convergence the output tends to be close to this initial value of the algorithm, which is not necessarily the global optimum of the model parameters. In this project, a Adaptive Baum-Welch (GA-BW is proposed.

  1. Feature extraction with LIDAR data and aerial images

    Science.gov (United States)

    Mao, Jianhua; Liu, Yanjing; Cheng, Penggen; Li, Xianhua; Zeng, Qihong; Xia, Jing

    2006-10-01

    Raw LIDAR data is a irregular spacing 3D point cloud including reflections from bare ground, buildings, vegetation and vehicles etc., and the first task of the data analyses of point cloud is feature extraction. However, the interpretability of LIDAR point cloud is often limited due to the fact that no object information is provided, and the complex earth topography and object morphology make it impossible for a single operator to classify all the point cloud precisely 100%. In this paper, a hierarchy method for feature extraction with LIDAR data and aerial images is discussed. The aerial images provide us information of objects figuration and spatial distribution, and hierarchic classification of features makes it easy to apply automatic filters progressively. And the experiment results show that, using this method, it was possible to detect more object information and get a better result of feature extraction than using automatic filters alone.

  2. Deep person re-identification in aerial images

    Science.gov (United States)

    Schumann, Arne; Schuchert, Tobias

    2016-10-01

    Person re-identification is the problem of matching multiple occurrences of a person in large amounts of image or video data. In this work we propose an approach specifically tailored to re-identify people across different camera views in aerial video recordings. New challenges that arise in aerial data include unusual and more varied view angles, a moving camera and potentially large changes in environment and other in uences between recordings (i.e. between flights). Our approach addresses these new challenges. Due to their recent successes, we apply deep learning to automatically learn features for person re-identification on a number of public datasets. We evaluate these features on aerial data and propose a method to automatically select suitable pretrained features without requiring person id labels on the aerial data. We further show that tailored data augmentation methods are well suited to better cope with the larger variety in view angles. Finally, we combine our model with a metric learning approach to allow for interactive improvement of re-identification results through user feedback. We evaluate the approach on our own video dataset which contains 12 persons recorded from a UAV.

  3. Vehicle Detection and Classification from High Resolution Satellite Images

    Science.gov (United States)

    Abraham, L.; Sasikumar, M.

    2014-11-01

    In the past decades satellite imagery has been used successfully for weather forecasting, geographical and geological applications. Low resolution satellite images are sufficient for these sorts of applications. But the technological developments in the field of satellite imaging provide high resolution sensors which expands its field of application. Thus the High Resolution Satellite Imagery (HRSI) proved to be a suitable alternative to aerial photogrammetric data to provide a new data source for object detection. Since the traffic rates in developing countries are enormously increasing, vehicle detection from satellite data will be a better choice for automating such systems. In this work, a novel technique for vehicle detection from the images obtained from high resolution sensors is proposed. Though we are using high resolution images, vehicles are seen only as tiny spots, difficult to distinguish from the background. But we are able to obtain a detection rate not less than 0.9. Thereafter we classify the detected vehicles into cars and trucks and find the count of them.

  4. Building Detection Using Aerial Images and Digital Surface Models

    Science.gov (United States)

    Mu, J.; Cui, S.; Reinartz, P.

    2017-05-01

    In this paper a method for building detection in aerial images based on variational inference of logistic regression is proposed. It consists of three steps. In order to characterize the appearances of buildings in aerial images, an effective bag-of-Words (BoW) method is applied for feature extraction in the first step. In the second step, a classifier of logistic regression is learned using these local features. The logistic regression can be trained using different methods. In this paper we adopt a fully Bayesian treatment for learning the classifier, which has a number of obvious advantages over other learning methods. Due to the presence of hyper prior in the probabilistic model of logistic regression, approximate inference methods have to be applied for prediction. In order to speed up the inference, a variational inference method based on mean field instead of stochastic approximation such as Markov Chain Monte Carlo is applied. After the prediction, a probabilistic map is obtained. In the third step, a fully connected conditional random field model is formulated and the probabilistic map is used as the data term in the model. A mean field inference is utilized in order to obtain a binary building mask. A benchmark data set consisting of aerial images and digital surfaced model (DSM) released by ISPRS for 2D semantic labeling is used for performance evaluation. The results demonstrate the effectiveness of the proposed method.

  5. Detection of Aspens Using High Resolution Aerial Laser Scanning Data and Digital Aerial Images

    Directory of Open Access Journals (Sweden)

    Kalle Eerikäinen

    2008-08-01

    Full Text Available The aim was to use high resolution Aerial Laser Scanning (ALS data and aerial images to detect European aspen (Populus tremula L. from among other deciduous trees. The field data consisted of 14 sample plots of 30 m × 30 m size located in the Koli National Park in the North Karelia, Eastern Finland. A Canopy Height Model (CHM was interpolated from the ALS data with a pulse density of 3.86/m2, low-pass filtered using Height-Based Filtering (HBF and binarized to create the mask needed to separate the ground pixels from the canopy pixels within individual areas. Watershed segmentation was applied to the low-pass filtered CHM in order to create preliminary canopy segments, from which the non-canopy elements were extracted to obtain the final canopy segmentation, i.e. the ground mask was analysed against the canopy mask. A manual classification of aerial images was employed to separate the canopy segments of deciduous trees from those of coniferous trees. Finally, linear discriminant analysis was applied to the correctly classified canopy segments of deciduous trees to classify them into segments belonging to aspen and those belonging to other deciduous trees. The independent variables used in the classification were obtained from the first pulse ALS point data. The accuracy of discrimination between aspen and other deciduous trees was 78.6%. The independent variables in the classification function were the proportion of vegetation hits, the standard deviation of in pulse heights, accumulated intensity at the 90th percentile and the proportion of laser points reflected at the 60th height percentile. The accuracy of classification corresponded to the validation results of earlier ALS-based studies on the classification of individual deciduous trees to tree species.

  6. a Fast Approach for Stitching of Aerial Images

    Science.gov (United States)

    Moussa, A.; El-Sheimy, N.

    2016-06-01

    The last few years have witnessed an increasing volume of aerial image data because of the extensive improvements of the Unmanned Aerial Vehicles (UAVs). These newly developed UAVs have led to a wide variety of applications. A fast assessment of the achieved coverage and overlap of the acquired images of a UAV flight mission is of great help to save the time and cost of the further steps. A fast automatic stitching of the acquired images can help to visually assess the achieved coverage and overlap during the flight mission. This paper proposes an automatic image stitching approach that creates a single overview stitched image using the acquired images during a UAV flight mission along with a coverage image that represents the count of overlaps between the acquired images. The main challenge of such task is the huge number of images that are typically involved in such scenarios. A short flight mission with image acquisition frequency of one second can capture hundreds to thousands of images. The main focus of the proposed approach is to reduce the processing time of the image stitching procedure by exploiting the initial knowledge about the images positions provided by the navigation sensors. The proposed approach also avoids solving for all the transformation parameters of all the photos together to save the expected long computation time if all the parameters were considered simultaneously. After extracting the points of interest of all the involved images using Scale-Invariant Feature Transform (SIFT) algorithm, the proposed approach uses the initial image's coordinates to build an incremental constrained Delaunay triangulation that represents the neighborhood of each image. This triangulation helps to match only the neighbor images and therefore reduces the time-consuming features matching step. The estimated relative orientation between the matched images is used to find a candidate seed image for the stitching process. The pre-estimated transformation

  7. A FAST APPROACH FOR STITCHING OF AERIAL IMAGES

    Directory of Open Access Journals (Sweden)

    A. Moussa

    2016-06-01

    Full Text Available The last few years have witnessed an increasing volume of aerial image data because of the extensive improvements of the Unmanned Aerial Vehicles (UAVs. These newly developed UAVs have led to a wide variety of applications. A fast assessment of the achieved coverage and overlap of the acquired images of a UAV flight mission is of great help to save the time and cost of the further steps. A fast automatic stitching of the acquired images can help to visually assess the achieved coverage and overlap during the flight mission. This paper proposes an automatic image stitching approach that creates a single overview stitched image using the acquired images during a UAV flight mission along with a coverage image that represents the count of overlaps between the acquired images. The main challenge of such task is the huge number of images that are typically involved in such scenarios. A short flight mission with image acquisition frequency of one second can capture hundreds to thousands of images. The main focus of the proposed approach is to reduce the processing time of the image stitching procedure by exploiting the initial knowledge about the images positions provided by the navigation sensors. The proposed approach also avoids solving for all the transformation parameters of all the photos together to save the expected long computation time if all the parameters were considered simultaneously. After extracting the points of interest of all the involved images using Scale-Invariant Feature Transform (SIFT algorithm, the proposed approach uses the initial image’s coordinates to build an incremental constrained Delaunay triangulation that represents the neighborhood of each image. This triangulation helps to match only the neighbor images and therefore reduces the time-consuming features matching step. The estimated relative orientation between the matched images is used to find a candidate seed image for the stitching process. The pre

  8. Combining Constraint Types From Public Data in Aerial Image Segmentation

    DEFF Research Database (Denmark)

    Jacobsen, Thomas Stig; Jensen, Jacob Jon; Jensen, Daniel Rune

    2013-01-01

    detect the number of clusters using the elongated K-means algorithm instead of using the standard spectral clustering approach of a predefined number of clusters. The results are refined by filtering out noise with a binary morphological operator. Point data is used for semi-supervised labelling......We introduce a method for image segmentation that constraints the clustering with map and point data. The method is showcased by applying the spectral clustering algorithm on aerial images for building detection with constraints built from a height map and address point data. We automatically...

  9. Eyes in the sky: a comparative spatial analysis of aerial and satellite surveillance of east coast Canadian oil pollution

    Energy Technology Data Exchange (ETDEWEB)

    Lieske, David J. [Mount Allison University (Canada)], email: dlieske@mta.ca; Mahoney, Matthew [Environment Canada (Canada); Wilhelm, Sabina I. [Canadian Wildlife Service, Environment Canada (Canada); Weir, Laurie [Marine and Ice Services, Meteorological Service of Canada, Environment Canada (Canada); O' Hara, Patrick [Canadian Wildlife Service, Institute of Ocean Science (canada)

    2011-07-01

    This study offers an insight into three different oil spill detection schemes used in Canadian waters: visually unaided aerial surveillance, SLAR-assisted aerial surveillance, and RADARSAT-based image processing. The main purpose of this study is to compare the effectiveness of these schemes with respect to spill surveillance methodology, and to make recommendations for future planning techniques. In order to quantify the surveillance capacity of each method, flight paths had to be generated, followed by calculating oil loading. Flight paths were generated manually from images and records for the first approach, while this information was extracted from digitalized records for the second and third approaches. Results showed that SLAR technology increased the efficiency of NASP aircraft in spill detection by increasing the area surveyed and by its not having to rely on naked-eye spotting of the spills. Moreover, ISTOP widened the aircraft coverage range due to its satellite-based imagery, which can operate at night as well as in daylight.

  10. Forecasting Hurricane by Satellite Image

    Science.gov (United States)

    Liu, M. Y.

    Earth is an endanger planet. Severe weather, especially hurricanes, results in great disaster all the world. World Meteorology Organization and United Nations Environment Program established intergovernment Panel on Climate Change (IPCC) to offer warnings about the present and future disasters of the Earth. It is the mission for scientists to design warning system to predict the severe weather system and to reduce the damage of the Earth. Hurricanes invade all the world every year and made millions damage to all the people. Scientists in weather service applied satellite images and synoptic data to forecast the information for the next hours for warning purposes. Regularly, hurricane hits on Taiwan island directly will pass through her domain and neighbor within 10 hours. In this study, we are going to demonstrate a tricky hurricane NARI invaded Taiwan on September 16, 2000. She wandered in the neighborhood of the island more than 72 hours and brought heavy rainfall over the island. Her track is so tricky that scientists can not forecast her path using the regular method. Fortunately, all scientists in the Central Weather Bureau paid their best effort to fight against the tricky hurricane. Applying the new developed technique to analysis the satellite images with synoptic data and radar echo, scientists forecasted the track, intensity and rainfall excellently. Thus the damage of the severe weather reduced significantly.

  11. An automatic high precision registration method between large area aerial images and aerial light detection and ranging data

    Science.gov (United States)

    Du, Q.; Xie, D.; Sun, Y.

    2015-06-01

    The integration of digital aerial photogrammetry and Light Detetion And Ranging (LiDAR) is an inevitable trend in Surveying and Mapping field. We calculate the external orientation elements of images which identical with LiDAR coordinate to realize automatic high precision registration between aerial images and LiDAR data. There are two ways to calculate orientation elements. One is single image spatial resection using image matching 3D points that registered to LiDAR. The other one is Position and Orientation System (POS) data supported aerotriangulation. The high precision registration points are selected as Ground Control Points (GCPs) instead of measuring GCPs manually during aerotriangulation. The registration experiments indicate that the method which registering aerial images and LiDAR points has a great advantage in higher automation and precision compare with manual registration.

  12. Environmental waste site characterization utilizing aerial photographs and satellite imagery: Three sites in New Mexico, USA

    Energy Technology Data Exchange (ETDEWEB)

    Van Eeckhout, E.; Pope, P.; Becker, N.; Wells, B. [Los Alamos National Lab., NM (United States); Lewis, A.; David, N. [Environmental Research Inst. of Michigan, Santa Fe, NM (United States)

    1996-04-01

    The proper handling and characterization of past hazardous waste sites is becoming more and more important as world population extends into areas previously deemed undesirable. Historical photographs, past records, current aerial satellite imagery can play an important role in characterizing these sites. These data provide clear insight into defining problem areas which can be surface samples for further detail. Three such areas are discussed in this paper: (1) nuclear wastes buried in trenches at Los Alamos National Laboratory, (2) surface dumping at one site at Los Alamos National Laboratory, and (3) the historical development of a municipal landfill near Las Cruces, New Mexico.

  13. Large-Scale Aerial Image Categorization Using a Multitask Topological Codebook.

    Science.gov (United States)

    Zhang, Luming; Wang, Meng; Hong, Richang; Yin, Bao-Cai; Li, Xuelong

    2016-02-01

    Fast and accurately categorizing the millions of aerial images on Google Maps is a useful technique in pattern recognition. Existing methods cannot handle this task successfully due to two reasons: 1) the aerial images' topologies are the key feature to distinguish their categories, but they cannot be effectively encoded by a conventional visual codebook and 2) it is challenging to build a realtime image categorization system, as some geo-aware Apps update over 20 aerial images per second. To solve these problems, we propose an efficient aerial image categorization algorithm. It focuses on learning a discriminative topological codebook of aerial images under a multitask learning framework. The pipeline can be summarized as follows. We first construct a region adjacency graph (RAG) that describes the topology of each aerial image. Naturally, aerial image categorization can be formulated as RAG-to-RAG matching. According to graph theory, RAG-to-RAG matching is conducted by enumeratively comparing all their respective graphlets (i.e., small subgraphs). To alleviate the high time consumption, we propose to learn a codebook containing topologies jointly discriminative to multiple categories. The learned topological codebook guides the extraction of the discriminative graphlets. Finally, these graphlets are integrated into an AdaBoost model for predicting aerial image categories. Experimental results show that our approach is competitive to several existing recognition models. Furthermore, over 24 aerial images are processed per second, demonstrating that our approach is ready for real-world applications.

  14. STUDY OF AUTOMATIC IMAGE RECTIFICATION AND REGISTRATION OF SCANNED HISTORICAL AERIAL PHOTOGRAPHS

    OpenAIRE

    Chen , H.R.; Tseng, Y H

    2016-01-01

    Historical aerial photographs directly provide good evidences of past times. The Research Center for Humanities and Social Sciences (RCHSS) of Taiwan Academia Sinica has collected and scanned numerous historical maps and aerial images of Taiwan and China. Some maps or images have been geo-referenced manually, but most of historical aerial images have not been registered since there are no GPS or IMU data for orientation assisting in the past. In our research, we developed an automatic process...

  15. Deblurring Texture Extraction from Digital Aerial Image by Reforming "Steep Edge" Curve

    Institute of Scientific and Technical Information of China (English)

    WU Jun; CHEN Danqing

    2005-01-01

    Texture extract from digital aerial image is widely used in three-dimensional city modeling to generate "photo-realistic" views. In this paper, a method based on reforming "Steep edge" curve, which clearly explains how the diffraction of the sunlight makes digital aerial image blurring, is proposed to deblur the texture extraction from digital aerial image, and the experiment shows a good result in visualization and automation.

  16. Algorithm for unmanned aerial vehicle aerial different-source image matching

    Science.gov (United States)

    Zuo, Yujia; Liu, Jinghong; Yang, Mingyu; Wang, Xuan; Sun, Mingchao

    2016-12-01

    The fusion between visible and infrared images captured by unmanned aerial vehicles (UAVs), both complementary to each other, can improve the reliability of target detection and recognition and other tasks. The images captured by UAV are featured by high dynamics and complex air-ground target background. Pixel-level matching should be conducted for the two different-source images, prior to their fusion. Therefore, an improved matching algorithm has been proposed that combines the improved Shi-Tomasi algorithm with the shape context (SC)-based algorithm. First, the Shi-Tomasi algorithm is employed to conduct feature-point detection in the scale space. The tangential direction of the edge contour where the feature-point lies is taken as its main direction, so as to guarantee the algorithm's rotational invariance. Then, this paper conducts the block description for the extracted feature-point within the n×n neighborhood of its edge contour to obtain its descriptors. Finally, a fast library for approximate nearest neighbors matching algorithm is adopted to match all the feature-points. And the experimental results show that, in the scene where the shape of the main target is clear, the algorithm can achieve better matching and registration results for infrared and visible images that have been transformed through rotation, translation, or zooming.

  17. a New Paradigm for Matching - and Aerial Images

    Science.gov (United States)

    Koch, T.; Zhuo, X.; Reinartz, P.; Fraundorfer, F.

    2016-06-01

    This paper investigates the performance of SIFT-based image matching regarding large differences in image scaling and rotation, as this is usually the case when trying to match images captured from UAVs and airplanes. This task represents an essential step for image registration and 3d-reconstruction applications. Various real world examples presented in this paper show that SIFT, as well as A-SIFT perform poorly or even fail in this matching scenario. Even if the scale difference in the images is known and eliminated beforehand, the matching performance suffers from too few feature point detections, ambiguous feature point orientations and rejection of many correct matches when applying the ratio-test afterwards. Therefore, a new feature matching method is provided that overcomes these problems and offers thousands of matches by a novel feature point detection strategy, applying a one-to-many matching scheme and substitute the ratio-test by adding geometric constraints to achieve geometric correct matches at repetitive image regions. This method is designed for matching almost nadir-directed images with low scene depth, as this is typical in UAV and aerial image matching scenarios. We tested the proposed method on different real world image pairs. While standard SIFT failed for most of the datasets, plenty of geometrical correct matches could be found using our approach. Comparing the estimated fundamental matrices and homographies with ground-truth solutions, mean errors of few pixels can be achieved.

  18. Error Estimation Techniques to Refine Overlapping Aerial Image Mosaic Processes via Detected Parameters

    Science.gov (United States)

    Bond, William Glenn

    2012-01-01

    In this paper, I propose to demonstrate a means of error estimation preprocessing in the assembly of overlapping aerial image mosaics. The mosaic program automatically assembles several hundred aerial images from a data set by aligning them, via image registration using a pattern search method, onto a GIS grid. The method presented first locates…

  19. Egypt satellite images for land surface characterization

    DEFF Research Database (Denmark)

    Hasager, Charlotte Bay

    Satellite images provide information on the land surface properties. From optical remote sensing images in the blue, green, red and near-infrared part of the electromagnetic spectrum it is possible to identify a large number of surface features. The report briefly describes different satellite...

  20. Line matching based on planar homography for stereo aerial images

    Science.gov (United States)

    Sun, Yanbiao; Zhao, Liang; Huang, Shoudong; Yan, Lei; Dissanayake, Gamini

    2015-06-01

    We propose an efficient line matching algorithm for a pair of calibrated aerial photogrammetric images, which makes use of sparse 3D points triangulated from 2D point feature correspondences to guide line matching based on planar homography. Two different strategies are applied in the proposed line matching algorithm for two different cases. When three or more points can be found coplanar with the line segment to be matched, the points are used to fit a plane and obtain an accurate planar homography. When one or two points can be found, the approximate terrain plane parallel to the line segment is utilized to compute an approximate planar homography. Six pairs of rural or urban aerial images are used to demonstrate the efficiency and validity of the proposed algorithm. Compared with line matching based on 2D point feature correspondences, the proposed method can increase the number of correctly matched line segments. In addition, compared with most line matching methods that do not use 2D point feature correspondences, the proposed method has better efficiency, although it obtains fewer matches. The C/C++ source code for the proposed algorithm is available at

  1. Direct Penguin Counting Using Unmanned Aerial Vehicle Image

    Science.gov (United States)

    Hyun, C. U.; Kim, H. C.; Kim, J. H.; Hong, S. G.

    2015-12-01

    This study presents an application of unmanned aerial vehicle (UAV) images to monitor penguin colony in Baton Peninsula, King George Island, Antarctica. The area around Narębski Point located on the southeast coast of Barton Peninsula was designated as Antarctic Specially Protected Area No. 171 (ASPA 171), and Chinstrap and Gentoo penguins inhabit in this area. The UAV images were acquired in a part of ASPA 171 from four flights in a single day, Jan 18, 2014. About 360 images were mosaicked as an image of about 3 cm spatial resolution and then a subset including representative penguin rookeries was selected. The subset image was segmented based on gradient map of pixel values, and spectral and spatial attributes were assigned to each segment. The object based image analysis (OBIA) was conducted with consideration of spectral attributes including mean and minimum values of each segment and various shape attributes such as area, length, compactness and roundness to detect individual penguin. The segments indicating individual penguin were effectively detected on rookeries with high contrasts in the spectral and shape attributes. The importance of periodic and precise monitoring of penguins has been recognized because variations of their populations reflect environmental changes and disturbance from human activities. Utilization of very high resolution imaging method shown in this study can be applied to other penguin habitats in Antarctica, and the results will be able to support establishing effective environmental management plans.

  2. AERIAL IMAGES AND LIDAR DATA FUSION FOR DISASTER CHANGE DETECTION

    Directory of Open Access Journals (Sweden)

    J. C. Trinder

    2012-07-01

    Full Text Available Potential applications of airborne LiDAR for disaster monitoring include flood prediction and assessment, monitoring of the growth of volcanoes and assistance in the prediction of eruptions, assessment of crustal elevation changes due to earthquakes, and monitoring of structural damage after earthquakes. Change detection in buildings is an important task in the context of disaster monitoring, especially after earthquakes. Traditionally, change detection is usually done by using multi-temporal images through spectral analyses. This provides two-dimensional spectral information without including heights. This paper will describe the capability of aerial images and LiDAR data fusion for rapid change detection in elevations, and methods of assessment of damage in made-made structures. In order to detect and evaluate changes in buildings, LiDAR-derived DEMs and aerial images from two epochs were used, showing changes in urban buildings due to construction and demolition. The proposed modelling scheme comprises three steps, namely, data pre-processing, change detection, and validation. In the first step for data pre-processing, data registration was carried out based on the multi-source data. In the second step, changes were detected by combining change detection techniques such as image differencing (ID, principal components analysis (PCA, minimum noise fraction (MNF and post-classification comparison (P-C based on support vector machines (SVM, each of which performs differently, based on simple majority vote. In the third step and to meet the objectives, the detected changes were compared against reference data that was generated manually. The comparison is based on two criteria: overall accuracy; and commission and omission errors. The results showed that the average detection accuracies were: 78.9%, 81.4%, 82.7% and 82.8% for post-classification, image differencing, PCA and MNF respectively. On the other hand, the commission and omission errors of

  3. Integration of aerial imaging and variable-rate technology for site-specific aerial herbicide application

    Science.gov (United States)

    As remote sensing and variable rate technology are becoming more available for aerial applicators, practical methodologies on effective integration of these technologies are needed for site-specific aerial applications of crop production and protection materials. The objectives of this study were to...

  4. High-resolution sensing for precision agriculture: from Earth-observing satellites to unmanned aerial vehicles

    KAUST Repository

    McCabe, Matthew

    2016-10-25

    With global population projected to approach 9 billion by 2050, it has been estimated that a 40% increase in cereal production will be required to satisfy the worlds growing nutritional demands. Any such increases in agricultural productivity are likely to occur within a system that has limited room for growth and in a world with a climate that is different from that of today. Fundamental to achieving food and water security, is the capacity to monitor the health and condition of agricultural systems. While space-Agency based satellites have provided the backbone for earth observation over the last few decades, many developments in the field of high-resolution earth observation have been advanced by the commercial sector. These advances relate not just to technological developments in the use of unmanned aerial vehicles (UAVs), but also the advent of nano-satellite constellations that offer a radical shift in the way earth observations are now being retrieved. Such technologies present opportunities for improving our description of the water, energy and carbon cycles. Efforts towards developing new observational techniques and interpretative frameworks are required to provide the tools and information needed to improve the management and security of agricultural and related sectors. These developments are one of the surest ways to better manage, protect and preserve national food and water resources. Here we review the capabilities of recently deployed satellite systems and UAVs and examine their potential for application in precision agriculture.

  5. High-resolution sensing for precision agriculture: from Earth-observing satellites to unmanned aerial vehicles

    Science.gov (United States)

    McCabe, Matthew F.; Houborg, Rasmus; Lucieer, Arko

    2016-10-01

    With global population projected to approach 9 billion by 2050, it has been estimated that a 40% increase in cereal production will be required to satisfy the worlds growing nutritional demands. Any such increases in agricultural productivity are likely to occur within a system that has limited room for growth and in a world with a climate that is different from that of today. Fundamental to achieving food and water security, is the capacity to monitor the health and condition of agricultural systems. While space-agency based satellites have provided the backbone for earth observation over the last few decades, many developments in the field of high-resolution earth observation have been advanced by the commercial sector. These advances relate not just to technological developments in the use of unmanned aerial vehicles (UAVs), but also the advent of nano-satellite constellations that offer a radical shift in the way earth observations are now being retrieved. Such technologies present opportunities for improving our description of the water, energy and carbon cycles. Efforts towards developing new observational techniques and interpretative frameworks are required to provide the tools and information needed to improve the management and security of agricultural and related sectors. These developments are one of the surest ways to better manage, protect and preserve national food and water resources. Here we review the capabilities of recently deployed satellite systems and UAVs and examine their potential for application in precision agriculture.

  6. Research of aerial imaging spectrometer data acquisition technology based on USB 3.0

    Science.gov (United States)

    Huang, Junze; Wang, Yueming; He, Daogang; Yu, Yanan

    2016-11-01

    With the emergence of UAV (unmanned aerial vehicle) platform for aerial imaging spectrometer, research of aerial imaging spectrometer DAS(data acquisition system) faces new challenges. Due to the limitation of platform and other factors, the aerial imaging spectrometer DAS requires small-light, low-cost and universal. Traditional aerial imaging spectrometer DAS system is expensive, bulky, non-universal and unsupported plug-and-play based on PCIe. So that has been unable to meet promotion and application of the aerial imaging spectrometer. In order to solve these problems, the new data acquisition scheme bases on USB3.0 interface.USB3.0 can provide guarantee of small-light, low-cost and universal relying on the forward-looking technology advantage. USB3.0 transmission theory is up to 5Gbps.And the GPIF programming interface achieves 3.2Gbps of the effective theoretical data bandwidth.USB3.0 can fully meet the needs of the aerial imaging spectrometer data transmission rate. The scheme uses the slave FIFO asynchronous data transmission mode between FPGA and USB3014 interface chip. Firstly system collects spectral data from TLK2711 of high-speed serial interface chip. Then FPGA receives data in DDR2 cache after ping-pong data processing. Finally USB3014 interface chip transmits data via automatic-dma approach and uploads to PC by USB3.0 cable. During the manufacture of aerial imaging spectrometer, the DAS can achieve image acquisition, transmission, storage and display. All functions can provide the necessary test detection for aerial imaging spectrometer. The test shows that system performs stable and no data lose. Average transmission speed and storage speed of writing SSD can stabilize at 1.28Gbps. Consequently ,this data acquisition system can meet application requirements for aerial imaging spectrometer.

  7. Deepwater Horizon MC252 - Oil Spill: Ocean Imaging Corp.'s Aerial Multispectral Oil Mapping System

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Ocean Imaging Corp.'s Aerial Multispectral Oil Mapping System employs a customizable 4-spectral channel system and IR imager integrated to allow simultaneous data...

  8. Semantic Segmentation of Aerial Images with AN Ensemble of Cnns

    Science.gov (United States)

    Marmanis, D.; Wegner, J. D.; Galliani, S.; Schindler, K.; Datcu, M.; Stilla, U.

    2016-06-01

    This paper describes a deep learning approach to semantic segmentation of very high resolution (aerial) images. Deep neural architectures hold the promise of end-to-end learning from raw images, making heuristic feature design obsolete. Over the last decade this idea has seen a revival, and in recent years deep convolutional neural networks (CNNs) have emerged as the method of choice for a range of image interpretation tasks like visual recognition and object detection. Still, standard CNNs do not lend themselves to per-pixel semantic segmentation, mainly because one of their fundamental principles is to gradually aggregate information over larger and larger image regions, making it hard to disentangle contributions from different pixels. Very recently two extensions of the CNN framework have made it possible to trace the semantic information back to a precise pixel position: deconvolutional network layers undo the spatial downsampling, and Fully Convolution Networks (FCNs) modify the fully connected classification layers of the network in such a way that the location of individual activations remains explicit. We design a FCN which takes as input intensity and range data and, with the help of aggressive deconvolution and recycling of early network layers, converts them into a pixelwise classification at full resolution. We discuss design choices and intricacies of such a network, and demonstrate that an ensemble of several networks achieves excellent results on challenging data such as the ISPRS semantic labeling benchmark, using only the raw data as input.

  9. Grab a coffee: your aerial images are already analyzed

    Science.gov (United States)

    Garetto, Anthony; Rademacher, Thomas; Schulz, Kristian

    2015-07-01

    For over 2 decades the AIMTM platform has been utilized in mask shops as the standard for actinic review of photomask sites in order to perform defect disposition and repair review. Throughout this time the measurement throughput of the systems has been improved in order to keep pace with the requirements demanded by a manufacturing environment, however the analysis of the sites captured has seen little improvement and remained a manual process. This manual analysis of aerial images is time consuming, subject to error and unreliability and contributes to holding up turn-around time (TAT) and slowing process flow in a manufacturing environment. AutoAnalysis, the first application available for the FAVOR® platform, offers a solution to these problems by providing fully automated data transfer and analysis of AIMTM aerial images. The data is automatically output in a customizable format that can be tailored to your internal needs and the requests of your customers. Savings in terms of operator time arise from the automated analysis which no longer needs to be performed. Reliability is improved as human error is eliminated making sure the most defective region is always and consistently captured. Finally the TAT is shortened and process flow for the back end of the line improved as the analysis is fast and runs in parallel to the measurements. In this paper the concept and approach of AutoAnalysis will be presented as well as an update to the status of the project. A look at the benefits arising from the automation and the customizable approach of the solution will be shown.

  10. A registration strategy for long spatio-temporal aerial remote sensing image sequence

    Science.gov (United States)

    Cao, Yutian; Yan, Dongmei; Li, Jianming; Wang, Gang

    2015-12-01

    A novel registration strategy for aerial image sequence is put forward to adapt to the long spatio-temporal span of the aerial remote sensing imaging. By setting keyframe, this strategy aligns all images in sequence to a unified datum with high registration sustainability and precision. The contrast experiment on different registration strategies is carried out based on SIFT feature matching of mid-infrared aerial sequences. The experiment results show that the proposed strategy performs well on long spatio-temporal sequences with different imaging resolutions and scenes.

  11. Deep learning based multi-category object detection in aerial images

    Science.gov (United States)

    Sommer, Lars W.; Schuchert, Tobias; Beyerer, Jürgen

    2017-05-01

    Multi-category object detection in aerial images is an important task for many applications such as surveillance, tracking or search and rescue tasks. In recent years, deep learning approaches using features extracted by convolutional neural networks (CNN) significantly improved the detection accuracy on detection benchmark datasets compared to traditional approaches based on hand-crafted features as used for object detection in aerial images. However, these approaches are not transferable one to one on aerial images as the used network architectures have an insufficient resolution of feature maps for handling small instances. This consequently results in poor localization accuracy or missed detections as the network architectures are explored and optimized for datasets that considerably differ from aerial images in particular in object size and image fraction occupied by an object. In this work, we propose a deep neural network derived from the Faster R-CNN approach for multi- category object detection in aerial images. We show how the detection accuracy can be improved by replacing the network architecture by an architecture especially designed for handling small object sizes. Furthermore, we investigate the impact of different parameters of the detection framework on the detection accuracy for small objects. Finally, we demonstrate the suitability of our network for object detection in aerial images by comparing our network to traditional baseline approaches and deep learning based approaches on the publicly available DLR 3K Munich Vehicle Aerial Image Dataset that comprises multiple object classes such as car, van, truck, bus and camper.

  12. Coastline Extraction from Aerial Images Based on Edge Detection

    Science.gov (United States)

    Paravolidakis, V.; Moirogiorgou, K.; Ragia, L.; Zervakis, M.; Synolakis, C.

    2016-06-01

    Nowadays coastline extraction and tracking of its changes become of high importance because of the climate change, global warming and rapid growth of human population. Coastal areas play a significant role for the economy of the entire region. In this paper we propose a new methodology for automatic extraction of the coastline using aerial images. A combination of a four step algorithm is used to extract the coastline in a robust and generalizable way. First, noise distortion is reduced in order to ameliorate the input data for the next processing steps. Then, the image is segmented into two regions, land and sea, through the application of a local threshold to create the binary image. The result is further processed by morphological operators with the aim that small objects are being eliminated and only the objects of interest are preserved. Finally, we perform edge detection and active contours fitting in order to extract and model the coastline. These algorithmic steps are illustrated through examples, which demonstrate the efficacy of the proposed methodology.

  13. Automatic urban debris zone extraction from post-hurricane very high-resolution satellite and aerial imagery

    Directory of Open Access Journals (Sweden)

    Shasha Jiang

    2016-05-01

    Full Text Available Automated remote sensing methods have not gained widespread usage for damage assessment after hurricane events, especially for low-rise buildings, such as individual houses and small businesses. Hurricane wind, storm surge with waves, and inland flooding have unique damage signatures, further complicating the development of robust automated assessment methodologies. As a step toward realizing automated damage assessment for multi-hazard hurricane events, this paper presents a mono-temporal image classification methodology that quickly and accurately differentiates urban debris from non-debris areas using post-event images. Three classification approaches are presented: spectral, textural, and combined spectral–textural. The methodology is demonstrated for Gulfport, Mississippi, using IKONOS panchromatic satellite and NOAA aerial colour imagery collected after 2005 Hurricane Katrina. The results show that multivariate texture information significantly improves debris class detection performance by decreasing the confusion between debris and other land cover types, and the extracted debris zone accurately captures debris distribution. Additionally, the extracted debris boundary is approximately equivalent regardless of imagery type, demonstrating the flexibility and robustness of the debris mapping methodology. While the test case presents results for hurricane hazards, the proposed methodology is generally developed and expected to be effective in delineating debris zones for other natural hazards, including tsunamis, tornadoes, and earthquakes.

  14. Structural High-resolution Satellite Image Indexing

    OpenAIRE

    Xia, Gui-Song; YANG, WEN; Delon, Julie; Gousseau, Yann; Sun, Hong; Maître, Henri

    2010-01-01

    International audience; Satellite images with high spatial resolution raise many challenging issues in image understanding and pattern recognition. First, they allow measurement of small objects maybe up to 0.5 m, and both texture and geometrical structures emerge simultaneously. Second, objects in the same type of scenes might appear at different scales and orientations. Consequently, image indexing methods should combine the structure and texture information of images and comply with some i...

  15. Using aerial photography and image analysis to measure changes in giant reed populations

    Science.gov (United States)

    A study was conducted along the Rio Grande in southwest Texas to evaluate color-infrared aerial photography combined with supervised image analysis to quantify changes in giant reed (Arundo donax L.) populations over a 6-year period. Aerial photographs from 2002 and 2008 of the same seven study site...

  16. Detection of object motion regions in aerial image pairs with a multilayer markovian model.

    Science.gov (United States)

    Benedek, Csaba; Szirányi, Tamás; Kato, Zoltan; Zerubia, Josiane

    2009-10-01

    We propose a new Bayesian method for detecting the regions of object displacements in aerial image pairs. We use a robust but coarse 2-D image registration algorithm. Our main challenge is to eliminate the registration errors from the extracted change map. We introduce a three-layer Markov random field (L(3)MRF) model which integrates information from two different features, and ensures connected homogenous regions in the segmented images. Validation is given on real aerial photos.

  17. An Improved Fast SPIHT Image Compression Algorithm for Aerial Applications

    Directory of Open Access Journals (Sweden)

    Ning Zhang

    2011-12-01

    Full Text Available In this paper, an improved fast SPIHT algorithm has been presented. SPIHT and NLS (Not List SPIHT are efficient compression algorithms, but the algorithms application is limited by the shortcomings of the poor error resistance and slow compression speed in the aviation areas. In this paper, the error resilience and the compression speed are improved. The remote sensing images are decomposed by Le Gall5/3 wavelet, and wavelet coefficients are indexed, scanned and allocated by the means of family blocks. The bit-plane importance is predicted by bitwise OR, so the N bit-planes can be encoded at the same time. Compared with the SPIHT algorithm, this improved algorithm is easy implemented by hardware, and the compression speed is improved. The PSNR of reconstructed images encoded by fast SPIHT is higher than SPIHT and CCSDS from 0.3 to 0.9db, and the speed is 4-6 times faster than SPIHT encoding process. The algorithm meets the high speed and reliability requirements of aerial applications.

  18. Satellite imager calibration and validation

    CSIR Research Space (South Africa)

    Vhengani, L

    2010-10-01

    Full Text Available The success or failure of any earth observation mission depends on the quality of its data. Data quality is assessed by determining the radiometric, spatial, spectral and geometric fidelity of the satellite sensor. The process is termed calval...

  19. Model-based satellite image fusion

    DEFF Research Database (Denmark)

    Aanæs, Henrik; Sveinsson, J. R.; Nielsen, Allan Aasbjerg

    2008-01-01

    A method is proposed for pixel-level satellite image fusion derived directly from a model of the imaging sensor. By design, the proposed method is spectrally consistent. It is argued that the proposed method needs regularization, as is the case for any method for this problem. A framework for pixel...

  20. An Aerial Image Mosaic Method Based on UAV Position and Attitude Information

    Directory of Open Access Journals (Sweden)

    CHENG Zhenggang

    2016-06-01

    Full Text Available As the existing methods for aerial image mosaic take high computational, a fast and effective algorithm based on the position and attitude information of unmanned aerial vehicles (UAV is proposed. Firstly, the coordinates and attitude angles of UAV can be obtained by airborne GPS and inertial measurement unit (IMU, and each aerial image has a corresponding position and attitude information. The homography matrix between two aerial images with the positions and attitude information can be calculated. Then the registration of the mosaic images is obtained by the operation of homography matrix. Finally, the multiple images can be stitched and the whole panorama got. A large number of experiments demonstrate this algorithm is efficient.

  1. Comparison of Small Unmanned Aerial Vehicles Performance Using Image Processing

    Directory of Open Access Journals (Sweden)

    Esteban Cano

    2017-01-01

    Full Text Available Precision agriculture is a farm management technology that involves sensing and then responding to the observed variability in the field. Remote sensing is one of the tools of precision agriculture. The emergence of small unmanned aerial vehicles (sUAV have paved the way to accessible remote sensing tools for farmers. This paper describes the development of an image processing approach to compare two popular off-the-shelf sUAVs: 3DR Iris+ and DJI Phantom 2. Both units are equipped with a camera gimbal attached with a GoPro camera. The comparison of the two sUAV involves a hovering test and a rectilinear motion test. In the hovering test, the sUAV was allowed to hover over a known object and images were taken every quarter of a second for two minutes. For the image processing evaluation, the position of the object in the images was measured and this was used to assess the stability of the sUAV while hovering. In the rectilinear test, the sUAV was allowed to follow a straight path and images of a lined track were acquired. The lines on the images were then measured on how accurate the sUAV followed the path. The hovering test results show that the 3DR Iris+ had a maximum position deviation of 0.64 m (0.126 m root mean square RMS displacement while the DJI Phantom 2 had a maximum deviation of 0.79 m (0.150 m RMS displacement. In the rectilinear motion test, the maximum displacement for the 3DR Iris+ and the DJI phantom 2 were 0.85 m (0.134 m RMS displacement and 0.73 m (0.372 m RMS displacement. These results demonstrated that the two sUAVs performed well in both the hovering test and the rectilinear motion test and thus demonstrated that both sUAVs can be used for civilian applications such as agricultural monitoring. The results also showed that the developed image processing approach can be used to evaluate performance of a sUAV and has the potential to be used as another feedback control parameter for autonomous navigation.

  2. Object-based Image Classification of Arctic Sea Ice and Melt Ponds through Aerial Photos

    Science.gov (United States)

    Miao, X.; Xie, H.; Li, Z.; Lei, R.

    2013-12-01

    The last six years have marked the lowest Arctic summer sea ice extents in the modern era, with a new record summer minimum (3.4 million km2) set on 13 September 2012. It has been predicted that the Arctic could be free of summer ice within the next 25-30. The loss of Arctic summer ice could have serious consequences, such as higher water temperature due to the positive feedback of albedo, more powerful and frequent storms, rising sea levels, diminished habitats for polar animals, and more pollution due to fossil fuel exploitation and/ or increased traffic through the Northwest/ Northeast Passage. In these processes, melt ponds play an important role in Earth's radiation balance since they strongly absorb solar radiation rather than reflecting it as snow and ice do. Therefore, it is necessary to develop the ability of predicting the sea ice/ melt pond extents and space-time evolution, which is pivotal to prepare for the variation and uncertainty of the future environment, political, economic, and military needs. A lot of efforts have been put into Arctic sea ice modeling to simulate sea ice processes. However, these sea ice models were initiated and developed based on limited field surveys, aircraft or satellite image data. Therefore, it is necessary to collect high resolution sea ice aerial photo in a systematic way to tune up, validate, and improve models. Currently there are many sea ice aerial photos available, such as Chinese Arctic Exploration (CHINARE 2008, 2010, 2012), SHEBA 1998 and HOTRAX 2005. However, manually delineating of sea ice and melt pond from these images is time-consuming and labor-intensive. In this study, we use the object-based remote sensing classification scheme to extract sea ice and melt ponds efficiently from 1,727 aerial photos taken during the CHINARE 2010. The algorithm includes three major steps as follows. (1) Image segmentation groups the neighboring pixels into objects according to the similarity of spectral and texture

  3. Mapping Urban Tree Canopy Coverage and Structure using Data Fusion of High Resolution Satellite Imagery and Aerial Lidar

    Science.gov (United States)

    Elmes, A.; Rogan, J.; Williams, C. A.; Martin, D. G.; Ratick, S.; Nowak, D.

    2015-12-01

    Urban tree canopy (UTC) coverage is a critical component of sustainable urban areas. Trees provide a number of important ecosystem services, including air pollution mitigation, water runoff control, and aesthetic and cultural values. Critically, urban trees also act to mitigate the urban heat island (UHI) effect by shading impervious surfaces and via evaporative cooling. The cooling effect of urban trees can be seen locally, with individual trees reducing home HVAC costs, and at a citywide scale, reducing the extent and magnitude of an urban areas UHI. In order to accurately model the ecosystem services of a given urban forest, it is essential to map in detail the condition and composition of these trees at a fine scale, capturing individual tree crowns and their vertical structure. This paper presents methods for delineating UTC and measuring canopy structure at fine spatial resolution (HVAC benefits from UTC for individual homes, and for assessing the ecosystem services for entire urban areas. Such maps have previously been made using a variety of methods, typically relying on high resolution aerial or satellite imagery. This paper seeks to contribute to this growing body of methods, relying on a data fusion method to combine the information contained in high resolution WorldView-3 satellite imagery and aerial lidar data using an object-based image classification approach. The study area, Worcester, MA, has recently undergone a large-scale tree removal and reforestation program, following a pest eradication effort. Therefore, the urban canopy in this location provides a wide mix of tree age class and functional type, ideal for illustrating the effectiveness of the proposed methods. Early results show that the object-based classifier is indeed capable of identifying individual tree crowns, while continued research will focus on extracting crown structural characteristics using lidar-derived metrics. Ultimately, the resulting fine resolution UTC map will be

  4. Series of aerial images over Quivira National Wildlife Refuge, acquired on June, 1985

    Data.gov (United States)

    US Fish and Wildlife Service, Department of the Interior — This data set is of nine georeferenced aerial images taken over Quivira National Wildlife Refuge on June 19th and 27th, 1985. This data set is a clipped,...

  5. Advanced Tie Feature Matching for the Registration of Mobile Mapping Imaging Data and Aerial Imagery

    Science.gov (United States)

    Jende, P.; Peter, M.; Gerke, M.; Vosselman, G.

    2016-06-01

    Mobile Mapping's ability to acquire high-resolution ground data is opposing unreliable localisation capabilities of satellite-based positioning systems in urban areas. Buildings shape canyons impeding a direct line-of-sight to navigation satellites resulting in a deficiency to accurately estimate the mobile platform's position. Consequently, acquired data products' positioning quality is considerably diminished. This issue has been widely addressed in the literature and research projects. However, a consistent compliance of sub-decimetre accuracy as well as a correction of errors in height remain unsolved. We propose a novel approach to enhance Mobile Mapping (MM) image orientation based on the utilisation of highly accurate orientation parameters derived from aerial imagery. In addition to that, the diminished exterior orientation parameters of the MM platform will be utilised as they enable the application of accurate matching techniques needed to derive reliable tie information. This tie information will then be used within an adjustment solution to correct affected MM data. This paper presents an advanced feature matching procedure as a prerequisite to the aforementioned orientation update. MM data is ortho-projected to gain a higher resemblance to aerial nadir data simplifying the images' geometry for matching. By utilising MM exterior orientation parameters, search windows may be used in conjunction with a selective keypoint detection and template matching. Originating from different sensor systems, however, difficulties arise with respect to changes in illumination, radiometry and a different original perspective. To respond to these challenges for feature detection, the procedure relies on detecting keypoints in only one image. Initial tests indicate a considerable improvement in comparison to classic detector/descriptor approaches in this particular matching scenario. This method leads to a significant reduction of outliers due to the limited availability

  6. Searching data for supporting archaeo-landscapes in Cyprus: an overview of aerial, satellite, and cartographic datasets of the island

    Science.gov (United States)

    Agapiou, Athos; Lysandrou, Vasiliki; Themistocleous, Kyriakos; Nisantzi, Argyro; Lasaponara, Rosa; Masini, Nicola; Krauss, Thomas; Cerra, Daniele; Gessner, Ursula; Schreier, Gunter; Hadjimitsis, Diofantos

    2016-08-01

    The landscape of Cyprus is characterized by transformations that occurred during the 20th century, with many of such changes being still active today. Landscapes' changes are due to a variety of reasons including war conflicts, environmental conditions and modern development that have often caused the alteration or even the total loss of important information that could have assisted the archaeologists to comprehend the archaeo-landscape. The present work aims to provide detailed information regarding the different existing datasets that can be used to support archaeologists in understanding the transformations that the landscape in Cyprus undergone, from a remote sensing perspective. Such datasets may help archaeologists to visualize a lost landscape and try to retrieve valuable information, while they support researchers for future investigations. As such they can further highlight in a predictive manner and consequently assess the impacts of landscape transformation -being of natural or anthropogenic cause- to cultural heritage. Three main datasets are presented here: aerial images, satellite datasets including spy satellite datasets acquired during the Cold War, and cadastral maps. The variety of data is provided in a chronological order (e.g. year of acquisitions), while other important parameters such as the cost and the accuracy are also determined. Individual examples of archaeological sites in Cyprus are also provided for each dataset in order to underline both their importance and performance. Also some pre- and post-processing remote sensing methodologies are briefly described in order to enhance the final results. The paper within the framework of ATHENA project, dedicated to remote sensing archaeology/CH, aims to fill a significant gap in the recent literature of remote sensing archaeology of the island and to assist current and future archaeologists in their quest for remote sensing information to support their research.

  7. Spectrally Consistent Satellite Image Fusion with Improved Image Priors

    DEFF Research Database (Denmark)

    Nielsen, Allan Aasbjerg; Aanæs, Henrik; Jensen, Thomas B.S.;

    2006-01-01

    Here an improvement to our previous framework for satellite image fusion is presented. A framework purely based on the sensor physics and on prior assumptions on the fused image. The contributions of this paper are two fold. Firstly, a method for ensuring 100% spectrally consistency is proposed......, even when more sophisticated image priors are applied. Secondly, a better image prior is introduced, via data-dependent image smoothing....

  8. Study of Automatic Image Rectification and Registration of Scanned Historical Aerial Photographs

    Science.gov (United States)

    Chen, H. R.; Tseng, Y. H.

    2016-06-01

    Historical aerial photographs directly provide good evidences of past times. The Research Center for Humanities and Social Sciences (RCHSS) of Taiwan Academia Sinica has collected and scanned numerous historical maps and aerial images of Taiwan and China. Some maps or images have been geo-referenced manually, but most of historical aerial images have not been registered since there are no GPS or IMU data for orientation assisting in the past. In our research, we developed an automatic process of matching historical aerial images by SIFT (Scale Invariant Feature Transform) for handling the great quantity of images by computer vision. SIFT is one of the most popular method of image feature extracting and matching. This algorithm extracts extreme values in scale space into invariant image features, which are robust to changing in rotation scale, noise, and illumination. We also use RANSAC (Random sample consensus) to remove outliers, and obtain good conjugated points between photographs. Finally, we manually add control points for registration through least square adjustment based on collinear equation. In the future, we can use image feature points of more photographs to build control image database. Every new image will be treated as query image. If feature points of query image match the features in database, it means that the query image probably is overlapped with control images.With the updating of database, more and more query image can be matched and aligned automatically. Other research about multi-time period environmental changes can be investigated with those geo-referenced temporal spatial data.

  9. Low-Level Tie Feature Extraction of Mobile Mapping Data (mls/images) and Aerial Imagery

    Science.gov (United States)

    Jende, P.; Hussnain, Z.; Peter, M.; Oude Elberink, S.; Gerke, M.; Vosselman, G.

    2016-03-01

    Mobile Mapping (MM) is a technique to obtain geo-information using sensors mounted on a mobile platform or vehicle. The mobile platform's position is provided by the integration of Global Navigation Satellite Systems (GNSS) and Inertial Navigation Systems (INS). However, especially in urban areas, building structures can obstruct a direct line-of-sight between the GNSS receiver and navigation satellites resulting in an erroneous position estimation. Therefore, derived MM data products, such as laser point clouds or images, lack the expected positioning reliability and accuracy. This issue has been addressed by many researchers, whose aim to mitigate these effects mainly concentrates on utilising tertiary reference data. However, current approaches do not consider errors in height, cannot achieve sub-decimetre accuracy and are often not designed to work in a fully automatic fashion. We propose an automatic pipeline to rectify MM data products by employing high resolution aerial nadir and oblique imagery as horizontal and vertical reference, respectively. By exploiting the MM platform's defective, and therefore imprecise but approximate orientation parameters, accurate feature matching techniques can be realised as a pre-processing step to minimise the MM platform's three-dimensional positioning error. Subsequently, identified correspondences serve as constraints for an orientation update, which is conducted by an estimation or adjustment technique. Since not all MM systems employ laser scanners and imaging sensors simultaneously, and each system and data demands different approaches, two independent workflows are developed in parallel. Still under development, both workflows will be presented and preliminary results will be shown. The workflows comprise of three steps; feature extraction, feature matching and the orientation update. In this paper, initial results of low-level image and point cloud feature extraction methods will be discussed as well as an outline of

  10. LOW-LEVEL TIE FEATURE EXTRACTION OF MOBILE MAPPING DATA (MLS/IMAGES AND AERIAL IMAGERY

    Directory of Open Access Journals (Sweden)

    P. Jende

    2016-03-01

    Full Text Available Mobile Mapping (MM is a technique to obtain geo-information using sensors mounted on a mobile platform or vehicle. The mobile platform’s position is provided by the integration of Global Navigation Satellite Systems (GNSS and Inertial Navigation Systems (INS. However, especially in urban areas, building structures can obstruct a direct line-of-sight between the GNSS receiver and navigation satellites resulting in an erroneous position estimation. Therefore, derived MM data products, such as laser point clouds or images, lack the expected positioning reliability and accuracy. This issue has been addressed by many researchers, whose aim to mitigate these effects mainly concentrates on utilising tertiary reference data. However, current approaches do not consider errors in height, cannot achieve sub-decimetre accuracy and are often not designed to work in a fully automatic fashion. We propose an automatic pipeline to rectify MM data products by employing high resolution aerial nadir and oblique imagery as horizontal and vertical reference, respectively. By exploiting the MM platform’s defective, and therefore imprecise but approximate orientation parameters, accurate feature matching techniques can be realised as a pre-processing step to minimise the MM platform’s three-dimensional positioning error. Subsequently, identified correspondences serve as constraints for an orientation update, which is conducted by an estimation or adjustment technique. Since not all MM systems employ laser scanners and imaging sensors simultaneously, and each system and data demands different approaches, two independent workflows are developed in parallel. Still under development, both workflows will be presented and preliminary results will be shown. The workflows comprise of three steps; feature extraction, feature matching and the orientation update. In this paper, initial results of low-level image and point cloud feature extraction methods will be discussed

  11. Low-Cost Satellite Infrared Imager Study

    Science.gov (United States)

    2007-11-02

    2,297.00 10 MATLAB , Simulink , Symbolic Math Toolbox (2 ea @ £894) £1,788.00 11 MATLAB Image Processing Toolbox (2 ea at £192) £384.00 12 MATLAB ...Figure 1: MWIR and TIR satellite imagery. On the left is a BIRD image of forest fires on the Portuguese/ Spanish border3 and the image on right is...space-borne MWIR and TIR imagers, instrument engineers are continually evaluating advances in the miniaturization of detector technology. One

  12. Learning Oriented Region-based Convolutional Neural Networks for Building Detection in Satellite Remote Sensing Images

    Science.gov (United States)

    Chen, C.; Gong, W.; Hu, Y.; Chen, Y.; Ding, Y.

    2017-05-01

    The automated building detection in aerial images is a fundamental problem encountered in aerial and satellite images analysis. Recently, thanks to the advances in feature descriptions, Region-based CNN model (R-CNN) for object detection is receiving an increasing attention. Despite the excellent performance in object detection, it is problematic to directly leverage the features of R-CNN model for building detection in single aerial image. As we know, the single aerial image is in vertical view and the buildings possess significant directional feature. However, in R-CNN model, direction of the building is ignored and the detection results are represented by horizontal rectangles. For this reason, the detection results with horizontal rectangle cannot describe the building precisely. To address this problem, in this paper, we proposed a novel model with a key feature related to orientation, namely, Oriented R-CNN (OR-CNN). Our contributions are mainly in the following two aspects: 1) Introducing a new oriented layer network for detecting the rotation angle of building on the basis of the successful VGG-net R-CNN model; 2) the oriented rectangle is proposed to leverage the powerful R-CNN for remote-sensing building detection. In experiments, we establish a complete and bran-new data set for training our oriented R-CNN model and comprehensively evaluate the proposed method on a publicly available building detection data set. We demonstrate State-of-the-art results compared with the previous baseline methods.

  13. Haystack Ultrawideband Satellite Imaging Radar

    Science.gov (United States)

    2014-09-01

    enable long-range imaging. In 2013, a major upgrade to the facility was completed, adding a millimeter - wave W-band radar capability to Haystack’s X...diameter antenna was completely rebuilt to provide a 100 μm root-mean-square (rms) surface accuracy to support operation at the 3 mm wave - length (W...electromagnetic wave propagation through the troposphere. − The signal processing system lev- eraged Lincoln Laboratory‘s Radar Open Systems

  14. Change detection in satellite images

    Science.gov (United States)

    Thonnessen, U.; Hofele, G.; Middelmann, W.

    2005-05-01

    Change detection plays an important role in different military areas as strategic reconnaissance, verification of armament and disarmament control and damage assessment. It is the process of identifying differences in the state of an object or phenomenon by observing it at different times. The availability of spaceborne reconnaissance systems with high spatial resolution, multi spectral capabilities, and short revisit times offer new perspectives for change detection. Before performing any kind of change detection it is necessary to separate changes of interest from changes caused by differences in data acquisition parameters. In these cases it is necessary to perform a pre-processing to correct the data or to normalize it. Image registration and, corresponding to this task, the ortho-rectification of the image data is a further prerequisite for change detection. If feasible, a 1-to-1 geometric correspondence should be aspired for. Change detection on an iconic level with a succeeding interpretation of the changes by the observer is often proposed; nevertheless an automatic knowledge-based analysis delivering the interpretation of the changes on a semantic level should be the aim of the future. We present first results of change detection on a structural level concerning urban areas. After pre-processing, the images are segmented in areas of interest and structural analysis is applied to these regions to extract descriptions of urban infrastructure like buildings, roads and tanks of refineries. These descriptions are matched to detect changes and similarities.

  15. Satellite image analysis for surveillance, vegetation and climate change

    Energy Technology Data Exchange (ETDEWEB)

    Cai, D Michael [Los Alamos National Laboratory

    2011-01-18

    Recently, many studies have provided abundant evidence to show the trend of tree mortality is increasing in many regions, and the cause of tree mortality is associated with drought, insect outbreak, or fire. Unfortunately, there is no current capability available to monitor vegetation changes, and correlate and predict tree mortality with CO{sub 2} change, and climate change on the global scale. Different survey platforms (methods) have been used for forest management. Typical ground-based forest surveys measure tree stem diameter, species, and alive or dead. The measurements are low-tech and time consuming, but the sample sizes are large, running into millions of trees, covering large areas, and spanning many years. These field surveys provide powerful ground validation for other survey methods such as photo survey, helicopter GPS survey, and aerial overview survey. The satellite imagery has much larger coverage. It is easier to tile the different images together, and more important, the spatial resolution has been improved such that close to or even higher than aerial survey platforms. Today, the remote sensing satellite data have reached sub-meter spatial resolution for panchromatic channels (IKONOS 2: 1 m; Quickbird-2: 0.61 m; Worldview-2: 0.5 m) and meter spatial resolution for multi-spectral channels (IKONOS 2: 4 meter; Quickbird-2: 2.44 m; Worldview-2: 2 m). Therefore, high resolution satellite imagery can allow foresters to discern individual trees. This vital information should allow us to quantify physiological states of trees, e.g. healthy or dead, shape and size of tree crowns, as well as species and functional compositions of trees. This is a powerful data resource, however, due to the vast amount of the data collected daily, it is impossible for human analysts to review the imagery in detail to identify the vital biodiversity information. Thus, in this talk, we will discuss the opportunities and challenges to use high resolution satellite imagery and

  16. Line Matching Algorithm for Aerial Image Combining image and object space similarity constraints

    Science.gov (United States)

    Wang, Jingxue; Wang, Weixi; Li, Xiaoming; Cao, Zhenyu; Zhu, Hong; Li, Miao; He, Biao; Zhao, Zhigang

    2016-06-01

    A new straight line matching method for aerial images is proposed in this paper. Compared to previous works, similarity constraints combining radiometric information in image and geometry attributes in object plane are employed in these methods. Firstly, initial candidate lines and the elevation values of lines projection plane are determined by corresponding points in neighborhoods of reference lines. Secondly, project reference line and candidate lines back forward onto the plane, and then similarity measure constraints are enforced to reduce the number of candidates and to determine the finial corresponding lines in a hierarchical way. Thirdly, "one-to-many" and "many-to-one" matching results are transformed into "one-to-one" by merging many lines into the new one, and the errors are eliminated simultaneously. Finally, endpoints of corresponding lines are detected by line expansion process combing with "image-object-image" mapping mode. Experimental results show that the proposed algorithm can be able to obtain reliable line matching results for aerial images.

  17. Internal waves and vortices in satellite images

    CERN Document Server

    Sparavigna, Amelia Carolina

    2012-01-01

    Some recent papers proposed the use of the satellite images of Google Earth in teaching physics, in particular to see some behaviours of waves. Reflection, refraction, diffraction and interference are easy to be found in these satellite maps. Besides Google Earth, other sites exist, such as Earth Observatory or Earth Snapshot, suitable for illustrating the large-scale phenomena in atmosphere and oceans In this paper, we will see some examples for teaching surface and internal sea waves, and internal waves and the K\\'arm\\'an vortices in the atmosphere. Aim of this proposal is attracting the interest of students of engineering schools to the physics of waves.

  18. AO corrected satellite imaging from Mount Stromlo

    Science.gov (United States)

    Bennet, F.; Rigaut, F.; Price, I.; Herrald, N.; Ritchie, I.; Smith, C.

    2016-07-01

    The Research School of Astronomy and Astrophysics have been developing adaptive optics systems for space situational awareness. As part of this program we have developed satellite imaging using compact adaptive optics systems for small (1-2 m) telescopes such as those operated by Electro Optic Systems (EOS) from the Mount Stromlo Observatory. We have focused on making compact, simple, and high performance AO systems using modern high stroke high speed deformable mirrors and EMCCD cameras. We are able to track satellites down to magnitude 10 with a Strehl in excess of 20% in median seeing.

  19. Critical Assessment of Object Segmentation in Aerial Image Using Geo-Hausdorff Distance

    Science.gov (United States)

    Sun, H.; Ding, Y.; Huang, Y.; Wang, G.

    2016-06-01

    Aerial Image records the large-range earth objects with the ever-improving spatial and radiometric resolution. It becomes a powerful tool for earth observation, land-coverage survey, geographical census, etc., and helps delineating the boundary of different kinds of objects on the earth both manually and automatically. In light of the geo-spatial correspondence between the pixel locations of aerial image and the spatial coordinates of ground objects, there is an increasing need of super-pixel segmentation and high-accuracy positioning of objects in aerial image. Besides the commercial software package of eCognition and ENVI, many algorithms have also been developed in the literature to segment objects of aerial images. But how to evaluate the segmentation results remains a challenge, especially in the context of the geo-spatial correspondence. The Geo-Hausdorff Distance (GHD) is proposed to measure the geo-spatial distance between the results of various object segmentation that can be done with the manual ground truth or with the automatic algorithms.Based on the early-breaking and random-sampling design, the GHD calculates the geographical Hausdorff distance with nearly-linear complexity. Segmentation results of several state-of-the-art algorithms, including those of the commercial packages, are evaluated with a diverse set of aerial images. They have different signal-to-noise ratio around the object boundaries and are hard to trace correctly even for human operators. The GHD value is analyzed to comprehensively measure the suitability of different object segmentation methods for aerial images of different spatial resolution. By critically assessing the strengths and limitations of the existing algorithms, the paper provides valuable insight and guideline for extensive research in automating object detection and classification of aerial image in the nation-wide geographic census. It is also promising for the optimal design of operational specification of remote

  20. A neuromorphic approach to satellite image understanding

    Science.gov (United States)

    Partsinevelos, Panagiotis; Perakakis, Manolis

    2014-05-01

    Remote sensing satellite imagery provides high altitude, top viewing aspects of large geographic regions and as such the depicted features are not always easily recognizable. Nevertheless, geoscientists familiar to remote sensing data, gradually gain experience and enhance their satellite image interpretation skills. The aim of this study is to devise a novel computational neuro-centered classification approach for feature extraction and image understanding. Object recognition through image processing practices is related to a series of known image/feature based attributes including size, shape, association, texture, etc. The objective of the study is to weight these attribute values towards the enhancement of feature recognition. The key cognitive experimentation concern is to define the point when a user recognizes a feature as it varies in terms of the above mentioned attributes and relate it with their corresponding values. Towards this end, we have set up an experimentation methodology that utilizes cognitive data from brain signals (EEG) and eye gaze data (eye tracking) of subjects watching satellite images of varying attributes; this allows the collection of rich real-time data that will be used for designing the image classifier. Since the data are already labeled by users (using an input device) a first step is to compare the performance of various machine-learning algorithms on the collected data. On the long-run, the aim of this work would be to investigate the automatic classification of unlabeled images (unsupervised learning) based purely on image attributes. The outcome of this innovative process is twofold: First, in an abundance of remote sensing image datasets we may define the essential image specifications in order to collect the appropriate data for each application and improve processing and resource efficiency. E.g. for a fault extraction application in a given scale a medium resolution 4-band image, may be more effective than costly

  1. Imaging artificial satellites: An observational challenge

    Science.gov (United States)

    Smith, D. A.; Hill, D. C.

    2016-10-01

    According to the Union of Concerned Scientists, as of the beginning of 2016 there are 1381 active satellites orbiting the Earth, and the United States' Space Surveillance Network tracks about 8000 manmade orbiting objects of baseball-size and larger. NASA estimates debris larger than 1 cm to number more than half a million. The largest ones can be seen by eye—unresolved dots of light that move across the sky in minutes. For most astrophotographers, satellites are annoying streaks that can ruin hours of work. However, capturing a resolved image of an artificial satellite can pose an interesting challenge for a student, and such a project can provide connections between objects in the sky and commercial and political activities here on Earth.

  2. Application of aerial image based information for coastal habitat research

    DEFF Research Database (Denmark)

    Juel, Anders

    2014-01-01

    The coastal ecosystems constitute the most cohesive, and some of the most pristine, nature in Denmark and their conservation must be considered as one of the country’s most important obligations in the field of nature and biodiversity. With their vast extent, combined with their high complexity...... analysis of very-high resolution aerial orthophotos and digital elevation model data....

  3. Aerial image simulation for partial coherent system with programming development in MATLAB

    Science.gov (United States)

    Hasan, Md. Nazmul; Rahman, Md. Momtazur; Udoy, Ariful Banna

    2014-10-01

    Aerial image can be calculated by either Abbe's method or sum of coherent system decomposition (SOCS) method for partial coherent system. This paper introduces a programming with Matlab code that changes the analytical representation of Abbe's method to the matrix form, which has advantages for both Abbe's method and SOCS since matrix calculation is easier than double integration over object plane or pupil plane. First a singular matrix P is derived from a pupil function and effective light source in the spatial frequency domain. By applying Singular Value Decomposition (SVD) to the matrix P, eigenvalues and eigenfunctions are obtained. The aerial image can then be computed by the eigenvalues and eigenfunctions without calculation of Transmission Cross Coefficient (TCC). The aerial final image is almost identical as an original cross mask and the intensity distribution on image plane shows that it is almost uniform across the linewidth of the mask.

  4. Building damage scale proposal from VHR satellite image

    Science.gov (United States)

    Sandu, Constantin; Giulio Tonolo, Fabio; Cotrufo, Silvana; Boccardo, Piero

    2017-04-01

    Natural hazards have a huge impact in terms of economic losses, affected and killed people. Current exploitation of remote sensed images play a fundamental role in the delineation of damages generated by catastrophic events. Institutions like the United Nations and the European Commission designed services that provide information about the impact of disasters rapidly. One of the approach currently used to carry out the damage assessment is based on very high resolution remote sensing imagery (including both aerial and satellite platforms). One of the main focus of the responders, especially in case of events like earthquakes, is on buildings and infrastructures. As far as the buildings are concerned, to date international standard guidelines that provide essential information on how to assess building damages using VHR images still does not exist. The aim of this study is to develop a building damage scale tailored for analyses based on VHR vertical imagery and to propose a standard for the related interpretation guidelines. The task is carried out by comparing the current scales used for damage assessment by the main satellite based emergency mapping services. The study will analyze the datasets produced after the Ecuador (April 2016) and Central Italy(August and October 2016) earthquakes. The results suggest that by using VHR remotely sensed images it is not possible to directly use damage classification scales addressing structural damages (e.g the 5 grades proposed by EMS-98). A fine-tuning of existing damage classes is therefore required and the adoption of an internationally agreed standard should be encouraged, to streamline the use of SEM products generated by different services.

  5. Monitoring of wetlands Ecosystems using satellite images

    Science.gov (United States)

    Dabrowska-Zielinska, K.; Gruszczynska, M.; Yesou, H.; Hoscilo, A.

    Wetlands are very sensitive ecosystems, functioning as habitat for many organisms. Protection and regeneration of wetlands has been the crucial importance in ecological research and in nature conservation. Knowledge on biophysical properties of wetlands vegetation retrieved from satellite images will enable us to improve monitoring of these unique areas, very often impenetrable. The study covers Biebrza wetland situated in the Northeast part of Poland and is considered as Ramsar Convention test site. The research aims at establishing of changes in biophysical parameters as the scrub encroachment, lowering of the water table, and changes of the farming activity caused ecological changes at these areas. Data from the optical and microwave satellite images collected for the area of Biebrza marshland ecosystem have been analysed and compared with the detailed soil-vegetation ground measurements conducted in conjunction with the overflights. Satellite data include Landsat ETM, ERS-2 ATSR and SAR, SPOT VEGETATION, ENVISAT MERIS and ASAR, and NOAA AVHRR. From the optical data various vegetation indices have been calculated, which characterize the vegetation surface roughness, its moisture conditions and stage of development. Landsat ETM image has been used for classification of wetlands vegetation. For each class of vegetation various moisture indices have been developed. Ground data collected include wet and dry biomass, LAI, vegetation height, and TDR soil moisture. The water cloud model has been applied for retrieval of soil vegetation parameters taking into account microwave satellite images acquired at VV, HV and HH polarisations at different viewing angles. The vegetation parameters have been used for to distinguish changes, which occurred at the area. For each of the vegetation class the soil moisture was calculated from microwave data using developed algorithms. Results of this study will help mapping and monitoring wetlands with the high spatial and temporal

  6. HIGH DENSITY AERIAL IMAGE MATCHING: STATE-OF-THE-ART AND FUTURE PROSPECTS

    Directory of Open Access Journals (Sweden)

    N. Haala

    2016-06-01

    Full Text Available Ongoing innovations in matching algorithms are continuously improving the quality of geometric surface representations generated automatically from aerial images. This development motivated the launch of the joint ISPRS/EuroSDR project “Benchmark on High Density Aerial Image Matching”, which aims on the evaluation of photogrammetric 3D data capture in view of the current developments in dense multi-view stereo-image matching. Originally, the test aimed on image based DSM computation from conventional aerial image flights for different landuse and image block configurations. The second phase then put an additional focus on high quality, high resolution 3D geometric data capture in complex urban areas. This includes both the extension of the test scenario to oblique aerial image flights as well as the generation of filtered point clouds as additional output of the respective multi-view reconstruction. The paper uses the preliminary outcomes of the benchmark to demonstrate the state-of-the-art in airborne image matching with a special focus of high quality geometric data capture in urban scenarios.

  7. Stable Imaging and Accuracy Issues of Low-Altitude Unmanned Aerial Vehicle Photogrammetry Systems

    Directory of Open Access Journals (Sweden)

    Ying Yang

    2016-04-01

    Full Text Available Stable imaging of an unmanned aerial vehicle (UAV photogrammetry system is an important issue that affects the data processing and application of the system. Compared with traditional aerial images, the large rotation of roll, pitch, and yaw angles of UAV images decrease image quality and result in image deformation, thereby affecting the ground resolution, overlaps, and the consistency of the stereo models. These factors also cause difficulties in automatic tie point matching, image orientation, and accuracy of aerial triangulation (AT. The issues of large-angle photography of UAV photogrammetry system are discussed and analyzed quantitatively in this paper, and a simple and lightweight three-axis stabilization platform that works with a low-precision integrated inertial navigation system and a three-axis mechanical platform is used to reduce this problem. An experiment was carried out with an airship as the flight platform. Another experimental dataset, which was acquired by the same flight platform without a stabilization platform, was utilized for a comparative test. Experimental results show that the system can effectively isolate the swing of the flying platform. To ensure objective and reliable results, another group of experimental datasets, which were acquired using a fixed-wing UAV platform, was also analyzed. Statistical results of the experimental datasets confirm that stable imaging of a UAV platform can help improve the quality of aerial photography imagery and the accuracy of AT, and potentially improve the application of images acquired by a UAV.

  8. A low-cost dual-camera imaging system for aerial applicators

    Science.gov (United States)

    Agricultural aircraft provide a readily available remote sensing platform as low-cost and easy-to-use consumer-grade cameras are being increasingly used for aerial imaging. In this article, we report on a dual-camera imaging system we recently assembled that can capture RGB and near-infrared (NIR) i...

  9. A two-camera imaging system for pest detection and aerial application

    Science.gov (United States)

    This presentation reports on the design and testing of an airborne two-camera imaging system for pest detection and aerial application assessment. The system consists of two digital cameras with 5616 x 3744 effective pixels. One camera captures normal color images with blue, green and red bands, whi...

  10. [Automatic houses detection with color aerial images based on image segmentation].

    Science.gov (United States)

    He, Pei-Pei; Wan, You-Chuan; Jiang, Peng-Rui; Gao, Xian-Jun; Qin, Jia-Xin

    2014-07-01

    In order to achieve housing automatic detection from high-resolution aerial imagery, the present paper utilized the color information and spectral characteristics of the roofing material, with the image segmentation theory, to study the housing automatic detection method. Firstly, This method proposed in this paper converts the RGB color space to HIS color space, uses the characteristics of each component of the HIS color space and the spectral characteristics of the roofing material for image segmentation to isolate red tiled roofs and gray cement roof areas, and gets the initial segmentation housing areas by using the marked watershed algorithm. Then, region growing is conducted in the hue component with the seed segment sample by calculating the average hue in the marked region. Finally through the elimination of small spots and rectangular fitting process to obtain a clear outline of the housing area. Compared with the traditional pixel-based region segmentation algorithm, the improved method proposed in this paper based on segment growing is in a one-dimensional color space to reduce the computation without human intervention, and can cater to the geometry information of the neighborhood pixels so that the speed and accuracy of the algorithm has been significantly improved. A case study was conducted to apply the method proposed in this paper to high resolution aerial images, and the experimental results demonstrate that this method has a high precision and rational robustness.

  11. Developing Geostationary Satellite Imaging at Lowell Observatory

    Science.gov (United States)

    van Belle, G.

    2016-09-01

    Lowell Observatory operates the Navy Precision Optical Interferometer (NPOI), and owns & operates the Discovery Channel Telescope (DCT). This unique & necessary combination of facilities positions Lowell to develop a robust program of observing geostationary, GPS-plane, and other high-altitude (&1000mi) satellites. NPOI is a six-beam long-baseline optical interferometer, located in Flagstaff, Arizona; the facility is supported by a partnership between Lowell Observatory, the US Naval Observatory, and the Naval Research Laboratory. NPOI operates year-round in the visible with baselines between 8 and 100 meters (up to 432m is available), conducting programs of astronomical research and imaging technology development. NPOI is the only such facility as yet to directly observe geostationary satellites, enabling milliarcsecond resolution of these objects. To enhance this capability towards true imaging of geosats, an ongoing program of facility upgrades will be outlined. These upgrades include AO-assisted 1.0-m apertures feeding each beam line, and new near-infrared instrumentation on the back end. The large apertures will enable `at-will' observations of objects brighter than mK = 8:3 in the near-IR, corresponding to brighter than mV = 11:3 in the visible. At its core, the system is enabled by a `wavelength-baseline bootstrapping' approach discussed herein. A complementary pilot imaging study of visible speckle and aperture masked imaging at Lowell's 4.3-m DCT, for constraining the low-spatial frequency imaging information, is also outlined.

  12. Aerial image mosaics built using images with vegetation index pre-calculated

    Science.gov (United States)

    Rosendo Candido, Leandro; de Castro Jorge, Lúcio André; Luppe, Maximiliam

    2016-10-01

    Precision agriculture (PA) has offered a multitude of benefits to farmers, such as cost reduction, accuracy and speed in decision making. Among the tools that work with PA, the aerial image mosaics have key role in accurate mapping of diseases and pests in crops. A mosaic is the combination of multiple images, creating a new image that covers the property or plots accurately. One of the important analysis for farmers is based on the properties of the reflectance in each range of the electromagnetic spectrum of vegetation. Performing mathematical combinations of the different spectral bands has a better understanding of the spectral response of the vegetation. These combinations are called vegetation index (VI) and are useful for the control of the biomass, water content in leaf, chlorophyll content and others. It is usually calculated VI after the construction of the mosaic, as well the farmer has an accurate analysis of its vegetation. However, building a mosaic of images, it has a high computational cost, taking hours to complete and then apply the VI and to have the first test results. In order to reduce the computational cost of this process, this work aims to present a mosaic of images constructed from images with the VI already pre-calculated providing faster analysis to the farmer, given the fact that applying VI on the image came a this reduction in density image and thus have the gain in computational cost to build the mosaic.

  13. STUDY OF AUTOMATIC IMAGE RECTIFICATION AND REGISTRATION OF SCANNED HISTORICAL AERIAL PHOTOGRAPHS

    Directory of Open Access Journals (Sweden)

    H. R. Chen

    2016-06-01

    Full Text Available Historical aerial photographs directly provide good evidences of past times. The Research Center for Humanities and Social Sciences (RCHSS of Taiwan Academia Sinica has collected and scanned numerous historical maps and aerial images of Taiwan and China. Some maps or images have been geo-referenced manually, but most of historical aerial images have not been registered since there are no GPS or IMU data for orientation assisting in the past. In our research, we developed an automatic process of matching historical aerial images by SIFT (Scale Invariant Feature Transform for handling the great quantity of images by computer vision. SIFT is one of the most popular method of image feature extracting and matching. This algorithm extracts extreme values in scale space into invariant image features, which are robust to changing in rotation scale, noise, and illumination. We also use RANSAC (Random sample consensus to remove outliers, and obtain good conjugated points between photographs. Finally, we manually add control points for registration through least square adjustment based on collinear equation. In the future, we can use image feature points of more photographs to build control image database. Every new image will be treated as query image. If feature points of query image match the features in database, it means that the query image probably is overlapped with control images.With the updating of database, more and more query image can be matched and aligned automatically. Other research about multi-time period environmental changes can be investigated with those geo-referenced temporal spatial data.

  14. Comparison of analysis techniques for aerial image metrology on advanced photomask

    Science.gov (United States)

    Hwang, Seolchong; Woo, Sungha; Jang, Heeyeon; Lee, Youngmo; Kim, Sangpyo; Yang, Hyunjo; Schulz, Kristian; Garetto, Anthony

    2016-05-01

    The standard method for defect disposition and verification of repair success in the mask shop is through the utilization of the aerial imaging platform, AIMSTM. The CD (Critical Dimension) deviation of the defective or repaired region as well as the pattern shift can be calculated by comparing the measured aerial images of this region to that of a reference. Through this analysis it can be determined if the defect or repaired region will be printed on the wafer under the illumination conditions of the scanner. The analysis of the measured aerial images from the AIMSTM are commonly performed manually using the analysis software available on the system or with the help of an analysis software called RV (Repair Verification). Because the process is manual, it is not standardized and is subject to operator variations. This method of manual aerial image analysis is time consuming, dependent on the skill level of the operator and significantly contributes to the overall mask manufacturing process flow. AutoAnalysis (AA), the first application available for the FAVOR® platform, provides fully automated analysis of AIMSTM aerial images [1] and runs in parallel to the measurement of the aerial images. In this paper, we investigate the initial AutoAnalysis performance compared to the conventional method using RV and its application to a production environment. The evaluation is based on the defect CD of three pattern types: contact holes, dense line and spaces and peripheral structure. The defect analysis results for different patterns and illumination conditions will be correlated and challenges in transitioning to the new approach will be discussed.

  15. Embedded Implementation of VHR Satellite Image Segmentation.

    Science.gov (United States)

    Li, Chao; Balla-Arabé, Souleymane; Ginhac, Dominique; Yang, Fan

    2016-05-27

    Processing and analysis of Very High Resolution (VHR) satellite images provide a mass of crucial information, which can be used for urban planning, security issues or environmental monitoring. However, they are computationally expensive and, thus, time consuming, while some of the applications, such as natural disaster monitoring and prevention, require high efficiency performance. Fortunately, parallel computing techniques and embedded systems have made great progress in recent years, and a series of massively parallel image processing devices, such as digital signal processors or Field Programmable Gate Arrays (FPGAs), have been made available to engineers at a very convenient price and demonstrate significant advantages in terms of running-cost, embeddability, power consumption flexibility, etc. In this work, we designed a texture region segmentation method for very high resolution satellite images by using the level set algorithm and the multi-kernel theory in a high-abstraction C environment and realize its register-transfer level implementation with the help of a new proposed high-level synthesis-based design flow. The evaluation experiments demonstrate that the proposed design can produce high quality image segmentation with a significant running-cost advantage.

  16. Building 3D aerial image in photoresist with reconstructed mask image acquired with optical microscope

    Science.gov (United States)

    Chou, C. S.; Tang, Y. P.; Chu, F. S.; Huang, W. C.; Liu, R. G.; Gau, T. S.

    2012-03-01

    Calibration of mask images on wafer becomes more important as features shrink. Two major types of metrology have been commonly adopted. One is to measure the mask image with scanning electron microscope (SEM) to obtain the contours on mask and then simulate the wafer image with optical simulator. The other is to use an optical imaging tool Aerial Image Measurement System (AIMSTM) to emulate the image on wafer. However, the SEM method is indirect. It just gathers planar contours on a mask with no consideration of optical characteristics such as 3D topography structures. Hence, the image on wafer is not predicted precisely. Though the AIMSTM method can be used to directly measure the intensity at the near field of a mask but the image measured this way is not quite the same as that on the wafer due to reflections and refractions in the films on wafer. Here, a new approach is proposed to emulate the image on wafer more precisely. The behavior of plane waves with different oblique angles is well known inside and between planar film stacks. In an optical microscope imaging system, plane waves can be extracted from the pupil plane with a coherent point source of illumination. Once plane waves with a specific coherent illumination are analyzed, the partially coherent component of waves could be reconstructed with a proper transfer function, which includes lens aberration, polarization, reflection and refraction in films. It is a new method that we can transfer near light field of a mask into an image on wafer without the disadvantages of indirect SEM measurement such as neglecting effects of mask topography, reflections and refractions in the wafer film stacks. Furthermore, with this precise latent image, a separated resist model also becomes more achievable.

  17. System refinement for content based satellite image retrieval

    Directory of Open Access Journals (Sweden)

    NourElDin Laban

    2012-06-01

    Full Text Available We are witnessing a large increase in satellite generated data especially in the form of images. Hence intelligent processing of the huge amount of data received by dozens of earth observing satellites, with specific satellite image oriented approaches, presents itself as a pressing need. Content based satellite image retrieval (CBSIR approaches have mainly been driven so far by approaches dealing with traditional images. In this paper we introduce a novel approach that refines image retrieval process using the unique properties to satellite images. Our approach uses a Query by polygon (QBP paradigm for the content of interest instead of using the more conventional rectangular query by image approach. First, we extract features from the satellite images using multiple tiling sizes. Accordingly the system uses these multilevel features within a multilevel retrieval system that refines the retrieval process. Our multilevel refinement approach has been experimentally validated against the conventional one yielding enhanced precision and recall rates.

  18. GRANULOMETRIC MAPS FROM HIGH RESOLUTION SATELLITE IMAGES

    Directory of Open Access Journals (Sweden)

    Catherine Mering

    2011-05-01

    Full Text Available A new method of land cover mapping from satellite images using granulometric analysis is presented here. Discontinuous landscapes such as steppian bushes of semi arid regions and recently growing urban settlements are especially concerned by this study. Spatial organisations of the land cover are quantified by means of the size distribution analysis of the land cover units extracted from high resolution remotely sensed images. A granulometric map is built by automatic classification of every pixel of the image according to the granulometric density inside a sliding neighbourhood. Granulometric mapping brings some advantages over traditional thematic mapping by remote sensing by focusing on fine spatial events and small changes in one peculiar category of the landscape.

  19. An Automated Approach to Extracting River Bank Locations from Aerial Imagery Using Image Texture

    Science.gov (United States)

    2015-11-04

    12·98) (e) FORM CANCELS AND SUPERSEOES AU. PREVIOUS VERSIONS RIVER RESEARCH AND APPLICATIONS River Res. Applic. (2013) Published online in Wiley ... Online Library (wileyonlinelibrary.com) DOI: 10.1002/rra.2701AN AUTOMATED APPROACH TO EXTRACTING RIVER BANK LOCATIONS FROM AERIAL IMAGERY USING IMAGE...Computer Science. Vol. 2. Pratt W. 1991. Digital Image Processing. John Wiley and Sons: New York. Sera J. 1983. Image Analysis and Mathematical Morphology

  20. Geometric calibration of ERS satellite SAR images

    DEFF Research Database (Denmark)

    Mohr, Johan Jacob; Madsen, Søren Nørvang

    2001-01-01

    Geometric calibration of the European Remote Sensing (ERS) Satellite synthetic aperture radar (SAR) slant range images is important in relation to mapping areas without ground reference points and also in relation to automated processing. The relevant SAR system parameters are discussed...... on a seven-year ERS-1 and a four-year ERS-2 time series, the long term stability is found to be sufficient to allow a single calibration covering the entire mission period. A descending and an ascending orbit tandem pair of the ESA calibration site on Flevoland, suitable for calibration of ERS SAR processors...

  1. MODIS 2002-2003 Kansas Satellite Image Database (KSID)

    Data.gov (United States)

    Kansas Data Access and Support Center — The Kansas Satellite Image Database (KSID):2002-2003 consists of image data gathered by three sensors. The first image data are terrain-corrected, precision...

  2. ASTER 2002-2003 Kansas Satellite Image Database (KSID)

    Data.gov (United States)

    Kansas Data Access and Support Center — The Kansas Satellite Image Database (KSID):2002-2003 consists of image data gathered by three sensors. The first image data are terrain-corrected, precision...

  3. Characterization of surface oil thickness distribution patterns observed during the Deepwater Horizon (MC-252) oil spill with aerial and satellite remote sensing.

    Science.gov (United States)

    Svejkovsky, Jan; Hess, Mark; Muskat, Judd; Nedwed, Tim J; McCall, Jenifer; Garcia, Oscar

    2016-09-15

    Knowledge of the spatial distribution of oil thickness patterns within an on-water spill is of obvious importance for immediate spill response activities as well as for subsequent evaluation of the spill impacts. For long-lasting continuous spills like the 2010 3-month Deepwater Horizon (DWH) event in the Gulf of Mexico, it is also important to identify changes in the dominant oil features through time. This study utilized very high resolution (≤5m) aerial and satellite imagery acquired during the DWH spill to evaluate the shape, size and thickness of surface oil features that dominated the DWH slick. Results indicate that outside of the immediate spill source region, oil distributions did not encompass a broad, varied range of thicknesses. Instead, the oil separated into four primary, distinct characterizations: 1) invisible surface films detectable only with Synthetic Aperture Radar imaging because of the decreased surface backscatter, 2) thicker sheen & rainbow areas (oil (>1mm) that were consistently hundreds of meters long but most commonly only 10-50m wide. Where present within the slick footprint, each of the three distinct visible oil thickness classes maintained its shape characteristics both spatially (at different distances from the source and in different portions of the slick), and temporally (from mid-May through July 2010). The region over the source site tended to contain a more continuous range of oil thicknesses, however, our results indicate that the continuous injection of subsurface dispersants starting in late May significantly altered (lowered) that range. In addition to characterizing the oil thickness distribution patterns through the timeline of one of the world's largest oil spills, this paper also details the extension of using high resolution aerial imagery to calibrate medium resolution satellite data sources such as USA's Thematic Mapper (30m) to provide larger-scale spatial views of major spills, and discusses implications for

  4. Using Satellite Images for Wireless Network Planing in Baku City

    Science.gov (United States)

    Gojamanov, M.; Ismayilov, J.

    2013-04-01

    It is a well known fact that the Information-Telecommunication and Space research technologies are the fields getting much more benefits from the achievements of the scientific and technical progress. In many cases, these areas supporting each other have improved the conditions for their further development. For instance, the intensive development in the field of the mobile communication has caused the rapid progress of the Space research technologies and vice versa.Today it is impossible to solve one of the most important tasks of the mobile communication as Radio Frecance planning without the 2D and 3D digital maps. The compiling of such maps is much more efficient by means of the space images. Because the quality of the space images has been improved and developed, especially at the both spectral and spatial resolution points. It has been possible to to use 8 Band images with the spatial resolution of 50 sm. At present, in relation to the function 3G of mobile communications one of the main issues facing mobile operator companies is a high-precision 3D digital maps. It should be noted that the number of mobile phone users in the Republic of Azerbaijan went forward other Community of Independent States Countries. Of course, using of aerial images for 3D mapping would be optimal. However, depending on a number of technical and administrative problems aerial photography cannot be used. Therefore, the experience of many countries shows that it will be more effective to use the space images with the higher resolution for these issues. Concerning the fact that the mobile communication within the city of Baku has included 3G function there were ordered stereo images wih the spatial resolution of 50 cm for the 150 sq.km territory occupying the central part of the city in order to compile 3D digital maps. The images collected from the WorldView-2 satellite are 4-Band Bundle(Pan+MS1) stereo images. Such kind of imagery enable to automatically classificate some required

  5. Application of high resolution images from unmanned aerial vehicles for hydrology and range science

    Science.gov (United States)

    A common problem in many natural resource disciplines is the lack of high-enough spatial resolution images that can be used for monitoring and modeling purposes. Advances have been made in the utilization of Unmanned Aerial Vehicles (UAVs) in hydrology and rangeland science. By utilizing low fligh...

  6. Developing a framework for monitoring coastal habitats using aerial imagery and object-based image analysis

    DEFF Research Database (Denmark)

    Juel, Anders

    of decreased habitat dynamics exists. A valuable source of land cover changes are historical aerial imagery of which Denmark has unique datasets.This poster presents an object-based image analysis approach for mapping and monitoring af coastal habitat stucture, which integrates the high spectral resolution...

  7. A low-cost single-camera imaging system for aerial applicators

    Science.gov (United States)

    Agricultural aircraft provide a readily available and versatile platform for airborne remote sensing. Although various airborne imaging systems are available, most of these systems are either too expensive or too complex to be of practical use for aerial applicators. The objective of this study was ...

  8. Aerial-image enables diagrams and animation to be inserted in motion pictures

    Science.gov (United States)

    Andrews, S. J., Jr.; Tressel, G. W.

    1967-01-01

    Aerial-image unit makes it possible to insert diagrams and animation into live motion pictures, and also lift an element from a confusing background by suppressing general details. The unit includes a combination of two separate lens systems, the camera-projector system and the field lens system.

  9. Vehicle Detection of Aerial Image Using TV-L1 Texture Decomposition

    Science.gov (United States)

    Wang, Y.; Wang, G.; Li, Y.; Huang, Y.

    2016-06-01

    Vehicle detection from high-resolution aerial image facilitates the study of the public traveling behavior on a large scale. In the context of road, a simple and effective algorithm is proposed to extract the texture-salient vehicle among the pavement surface. Texturally speaking, the majority of pavement surface changes a little except for the neighborhood of vehicles and edges. Within a certain distance away from the given vector of the road network, the aerial image is decomposed into a smoothly-varying cartoon part and an oscillatory details of textural part. The variational model of Total Variation regularization term and L1 fidelity term (TV-L1) is adopted to obtain the salient texture of vehicles and the cartoon surface of pavement. To eliminate the noise of texture decomposition, regions of pavement surface are refined by seed growing and morphological operation. Based on the shape saliency analysis of the central objects in those regions, vehicles are detected as the objects of rectangular shape saliency. The proposed algorithm is tested with a diverse set of aerial images that are acquired at various resolution and scenarios around China. Experimental results demonstrate that the proposed algorithm can detect vehicles at the rate of 71.5% and the false alarm rate of 21.5%, and that the speed is 39.13 seconds for a 4656 x 3496 aerial image. It is promising for large-scale transportation management and planning.

  10. Polar-Orbiting Satellite (POES) Images

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Visible and Infrared satellite imagery taken from camera systems or radiometer instruments on satellites in orbit around the poles. Satellite campaigns include...

  11. Registering aerial video images using the projective constraint.

    Science.gov (United States)

    Jackson, Brian P; Goshtasby, A Ardeshir

    2010-03-01

    To separate object motion from camera motion in an aerial video, consecutive frames are registered at their planar background. Feature points are selected in consecutive frames and those that belong to the background are identified using the projective constraint. Corresponding background feature points are then used to register and align the frames. By aligning video frames at the background and knowing that objects move against the background, a means to detect and track moving objects is provided. Only scenes with planar background are considered in this study. Experimental results show improvement in registration accuracy when using the projective constraint to determine the registration parameters as opposed to finding the registration parameters without the projective constraint.

  12. Intra-field CDU map correlation between SEMs and aerial image characterization

    Science.gov (United States)

    Ning, Guoxiang; Philipp, Peter; Litt, Lloyd C.; Meusemann, Stefan; Thaler, Thomas; Schulz, Kristian; Tschinkl, Martin; Ackmann, Paul

    2014-09-01

    Reticle critical dimension uniformity (CDU) is one of the major sources of wafer CD variations which include both inter-field variations and intra-field variations. Generally, wafer critical dimension (CD) measurement sample size interfield is much less than intra-field. Intra-field CDU correction requires time-consumption of metrology. In order to improve wafer intra-field CDU, several methods can be applied such as intra-field dose correction to improve wafer intra-field CDU. Corrections can be based on CD(SEM) or aerial image metrology data from the reticle. Reticle CDU and wafer CDU maps are based on scanning electron microscope (SEM) metrology, while reticle inspection intensity mapping (NuFLare 6000) and wafer level critical dimension (WLCD) utilize aerial images or optical techniques. Reticle inspecton tools such as those from KLA and NuFlare, offer the ability to collect optical measurement data to produce an optical CDU map. WLCD of Zeiss has the advantage of using the same illumination condition as the scanner to measure the aerial images or optical CD. In this study, the intra-field wafer CDU map correlation between SEMs and aerial images are characterized. The layout of metrology structures is very important for the correlation between wafer intra-field CDU, measured by SEM, and the CDU determined by aerial images. The selection of metrology structures effects on the correlation to SEM CD to wafer is also demonstrated. Both reticle CDU, intensity CDU and WLCD are candidates for intra-field wafer CDU characterization and the advantages and limitations of each approach are discussed.

  13. AUTOMATIC APPROACH TO VHR SATELLITE IMAGE CLASSIFICATION

    Directory of Open Access Journals (Sweden)

    P. Kupidura

    2016-06-01

    Full Text Available In this paper, we present a proposition of a fully automatic classification of VHR satellite images. Unlike the most widespread approaches: supervised classification, which requires prior defining of class signatures, or unsupervised classification, which must be followed by an interpretation of its results, the proposed method requires no human intervention except for the setting of the initial parameters. The presented approach bases on both spectral and textural analysis of the image and consists of 3 steps. The first step, the analysis of spectral data, relies on NDVI values. Its purpose is to distinguish between basic classes, such as water, vegetation and non-vegetation, which all differ significantly spectrally, thus they can be easily extracted basing on spectral analysis. The second step relies on granulometric maps. These are the product of local granulometric analysis of an image and present information on the texture of each pixel neighbourhood, depending on the texture grain. The purpose of texture analysis is to distinguish between different classes, spectrally similar, but yet of different texture, e.g. bare soil from a built-up area, or low vegetation from a wooded area. Due to the use of granulometric analysis, based on mathematical morphology opening and closing, the results are resistant to the border effect (qualifying borders of objects in an image as spaces of high texture, which affect other methods of texture analysis like GLCM statistics or fractal analysis. Therefore, the effectiveness of the analysis is relatively high. Several indices based on values of different granulometric maps have been developed to simplify the extraction of classes of different texture. The third and final step of the process relies on a vegetation index, based on near infrared and blue bands. Its purpose is to correct partially misclassified pixels. All the indices used in the classification model developed relate to reflectance values, so the

  14. Automatic Approach to Vhr Satellite Image Classification

    Science.gov (United States)

    Kupidura, P.; Osińska-Skotak, K.; Pluto-Kossakowska, J.

    2016-06-01

    In this paper, we present a proposition of a fully automatic classification of VHR satellite images. Unlike the most widespread approaches: supervised classification, which requires prior defining of class signatures, or unsupervised classification, which must be followed by an interpretation of its results, the proposed method requires no human intervention except for the setting of the initial parameters. The presented approach bases on both spectral and textural analysis of the image and consists of 3 steps. The first step, the analysis of spectral data, relies on NDVI values. Its purpose is to distinguish between basic classes, such as water, vegetation and non-vegetation, which all differ significantly spectrally, thus they can be easily extracted basing on spectral analysis. The second step relies on granulometric maps. These are the product of local granulometric analysis of an image and present information on the texture of each pixel neighbourhood, depending on the texture grain. The purpose of texture analysis is to distinguish between different classes, spectrally similar, but yet of different texture, e.g. bare soil from a built-up area, or low vegetation from a wooded area. Due to the use of granulometric analysis, based on mathematical morphology opening and closing, the results are resistant to the border effect (qualifying borders of objects in an image as spaces of high texture), which affect other methods of texture analysis like GLCM statistics or fractal analysis. Therefore, the effectiveness of the analysis is relatively high. Several indices based on values of different granulometric maps have been developed to simplify the extraction of classes of different texture. The third and final step of the process relies on a vegetation index, based on near infrared and blue bands. Its purpose is to correct partially misclassified pixels. All the indices used in the classification model developed relate to reflectance values, so the preliminary step

  15. Registration of multitemporal aerial optical images using line features

    Science.gov (United States)

    Zhao, Chenyang; Goshtasby, A. Ardeshir

    2016-07-01

    Registration of multitemporal images is generally considered difficult because scene changes can occur between the times the images are obtained. Since the changes are mostly radiometric in nature, features are needed that are insensitive to radiometric differences between the images. Lines are geometric features that represent straight edges of rigid man-made structures. Because such structures rarely change over time, lines represent stable geometric features that can be used to register multitemporal remote sensing images. An algorithm to establish correspondence between lines in two images of a planar scene is introduced and formulas to relate the parameters of a homography transformation to the parameters of corresponding lines in images are derived. Results of the proposed image registration on various multitemporal images are presented and discussed.

  16. Road Extraction and Car Detection from Aerial Image Using Intensity and Color

    Directory of Open Access Journals (Sweden)

    Vahid Ghods

    2011-07-01

    Full Text Available In this paper a new automatic approach to road extraction from aerial images is proposed. The initialization strategies are based on the intensity, color, and Hough transform. After road elements extraction, chain codes are calculated. In the last step, using shadow, cars on the roads are detected. We implemented our method on the 25 images from "Google Earth" database. The experiments show an increase in both the completeness and the quality indexes for the extracted road.

  17. Unmanned Aerial Vehicles for Photogrammetry: Analysis of Orthophoto Images over the Territory of Lithuania

    Directory of Open Access Journals (Sweden)

    J. Suziedelyte Visockiene

    2016-01-01

    Full Text Available It has been recently observed that aircrafts tend to be replaced by light, simple structure unmanned aerial vehicles (UAV or mini unmanned aerial vehicles (MUAV with the purpose of updating the field of aerial photogrammetry. The built-in digital photo camera takes images of the Earth’s surface. To satisfy the photogrammetric requirements of the photographic images, it is necessary to carry out the terrestrial project planning of the flight path before the flight, to select the appropriate flying height, the time for acquiring images, the speed of the UAV, and other parameters. The paper presents the results of project calculations concerning the UAV flights and the analysis of the terrestrial images acquired during the field-testing flights. The experience carried out so far in the Lithuanian landscape is shown. The taken images have been processed by PhotoMod photogrammetric system. The paper presents the results of calculation of the project values of the UAV flights taking the images by digital camera Canon S100 and the analysis of the possibilities of the UAV orthophoto images’ mode.

  18. A Featureless Approach to 3D Polyhedral Building Modeling from Aerial Images

    Directory of Open Access Journals (Sweden)

    Karim Hammoudi

    2010-12-01

    Full Text Available This paper presents a model-based approach for reconstructing 3D polyhedral building models from aerial images. The proposed approach exploits some geometric and photometric properties resulting from the perspective projection of planar structures. Data are provided by calibrated aerial images. The novelty of the approach lies in its featurelessness and in its use of direct optimization based on image rawbrightness. The proposed framework avoids feature extraction and matching. The 3D polyhedral model is directly estimated by optimizing an objective function that combines an image-based dissimilarity measure and a gradient score over several aerial images. The optimization process is carried out by the Differential Evolution algorithm. The proposed approach is intended to provide more accurate 3D reconstruction than feature-based approaches. Fast 3D model rectification and updating can take advantage of the proposed method. Several results and evaluations of performance from real and synthetic images show the feasibility and robustness of the proposed approach.

  19. Evaluation of Color Settings in Aerial Images with the Use of Eye-Tracking User Study

    Science.gov (United States)

    Mirijovsky, J.; Popelka, S.

    2016-06-01

    The main aim of presented paper is to find the most realistic and preferred color settings for four different types of surfaces on the aerial images. This will be achieved through user study with the use of eye-movement recording. Aerial images taken by the unmanned aerial system were used as stimuli. From each image, squared crop area containing one of the studied types of surfaces (asphalt, concrete, water, soil, and grass) was selected. For each type of surface, the real value of reflectance was found with the use of precise spectroradiometer ASD HandHeld 2 which measures the reflectance. The device was used at the same time as aerial images were captured, so lighting conditions and state of vegetation were equal. The spectral resolution of the ASD device is better than 3.0 nm. For defining the RGB values of selected type of surface, the spectral reflectance values recorded by the device were merged into wider groups. Finally, we get three groups corresponding to RGB color system. Captured images were edited with the graphic editor Photoshop CS6. Contrast, clarity, and brightness were edited for all surface types on images. Finally, we get a set of 12 images of the same area with different color settings. These images were put into the grid and used as stimuli for the eye-tracking experiment. Eye-tracking is one of the methods of usability studies and it is considered as relatively objective. Eye-tracker SMI RED 250 with the sampling frequency 250 Hz was used in the study. As respondents, a group of 24 students of Geoinformatics and Geography was used. Their task was to select which image in the grid has the best color settings. The next task was to select which color settings they prefer. Respondents' answers were evaluated and the most realistic and most preferable color settings were found. The advantage of the eye-tracking evaluation was that also the process of the selection of the answers was analyzed. Areas of Interest were marked around each image in the

  20. Oblique aerial images and their use in cultural heritage documentation

    DEFF Research Database (Denmark)

    Höhle, Joachim

    2013-01-01

    Oblique images enable three-dimensional (3d) modelling of objects with vertical dimensions. Such imagery is nowadays systematically taken of cities and may easily become available. The documentation of cultural heritage can take advantage of these sources of information. Two new oblique camera...... systems are presented and characteristics of such images are summarized. A first example uses images of a new multi-camera system for the derivation of orthoimages, façade plots with photo texture, 3d scatter plots, and dynamic 3d models of a historic church. The applied methodology is based...... developments. Special attention is given to the quality of input images. Investigations are carried out on edges in the images. The combination of oblique and nadir images enables new possibilities in the processing. The use of the near-infrared channel besides the red, green, and blue channel of the applied...

  1. Application of machine learning for the evaluation of turfgrass plots using aerial images

    Science.gov (United States)

    Ding, Ke; Raheja, Amar; Bhandari, Subodh; Green, Robert L.

    2016-05-01

    Historically, investigation of turfgrass characteristics have been limited to visual ratings. Although relevant information may result from such evaluations, final inferences may be questionable because of the subjective nature in which the data is collected. Recent advances in computer vision techniques allow researchers to objectively measure turfgrass characteristics such as percent ground cover, turf color, and turf quality from the digital images. This paper focuses on developing a methodology for automated assessment of turfgrass quality from aerial images. Images of several turfgrass plots of varying quality were gathered using a camera mounted on an unmanned aerial vehicle. The quality of these plots were also evaluated based on visual ratings. The goal was to use the aerial images to generate quality evaluations on a regular basis for the optimization of water treatment. Aerial images are used to train a neural network so that appropriate features such as intensity, color, and texture of the turfgrass are extracted from these images. Neural network is a nonlinear classifier commonly used in machine learning. The output of the neural network trained model is the ratings of the grass, which is compared to the visual ratings. Currently, the quality and the color of turfgrass, measured as the greenness of the grass, are evaluated. The textures are calculated using the Gabor filter and co-occurrence matrix. Other classifiers such as support vector machines and simpler linear regression models such as Ridge regression and LARS regression are also used. The performance of each model is compared. The results show encouraging potential for using machine learning techniques for the evaluation of turfgrass quality and color.

  2. USE OF VERTICAL AERIAL IMAGES FOR SEMI-OBLIQUE MAPPING

    Directory of Open Access Journals (Sweden)

    D. Poli

    2017-05-01

    Full Text Available The paper proposes a methodology for the use of the oblique sections of images from large-format photogrammetric cameras, by exploiting the effect of the central perspective geometry in the lateral parts of the nadir images (“semi-oblique” images. The point of origin of the investigation was the execution of a photogrammetric flight over Norcia (Italy, which was seriously damaged after the earthquake of 30/10/2016. Contrary to the original plan of oblique acquisitions, the flight was executed on 15/11/2017 using an UltraCam Eagle camera with focal length 80 mm, and combining two flight plans, rotated by 90º (“crisscross” flight. The images (GSD 5 cm were used to extract a 2.5D DSM cloud, sampled to a XY-grid size of 2 GSD, a 3D point clouds with a mean spatial resolution of 1 GSD and a 3D mesh model at a resolution of 10 cm of the historic centre of Norcia for a quantitative assessment of the damages. From the acquired nadir images the “semi-oblique” images (forward, backward, left and right views could be extracted and processed in a modified version of GEOBLY software for measurements and restitution purposes. The potential of such semi-oblique image acquisitions from nadir-view cameras is hereafter shown and commented.

  3. Use of Vertical Aerial Images for Semi-Oblique Mapping

    Science.gov (United States)

    Poli, D.; Moe, K.; Legat, K.; Toschi, I.; Lago, F.; Remondino, F.

    2017-05-01

    The paper proposes a methodology for the use of the oblique sections of images from large-format photogrammetric cameras, by exploiting the effect of the central perspective geometry in the lateral parts of the nadir images ("semi-oblique" images). The point of origin of the investigation was the execution of a photogrammetric flight over Norcia (Italy), which was seriously damaged after the earthquake of 30/10/2016. Contrary to the original plan of oblique acquisitions, the flight was executed on 15/11/2017 using an UltraCam Eagle camera with focal length 80 mm, and combining two flight plans, rotated by 90º ("crisscross" flight). The images (GSD 5 cm) were used to extract a 2.5D DSM cloud, sampled to a XY-grid size of 2 GSD, a 3D point clouds with a mean spatial resolution of 1 GSD and a 3D mesh model at a resolution of 10 cm of the historic centre of Norcia for a quantitative assessment of the damages. From the acquired nadir images the "semi-oblique" images (forward, backward, left and right views) could be extracted and processed in a modified version of GEOBLY software for measurements and restitution purposes. The potential of such semi-oblique image acquisitions from nadir-view cameras is hereafter shown and commented.

  4. Moving Target Information Extraction Based on Single Satellite Image

    Directory of Open Access Journals (Sweden)

    ZHAO Shihu

    2015-03-01

    Full Text Available The spatial and time variant effects in high resolution satellite push broom imaging are analyzed. A spatial and time variant imaging model is established. A moving target information extraction method is proposed based on a single satellite remote sensing image. The experiment computes two airplanes' flying speed using ZY-3 multispectral image and proves the validity of spatial and time variant model and moving information extracting method.

  5. Oblique aerial images and their use in cultural heritage documentation

    DEFF Research Database (Denmark)

    Höhle, Joachim

    2013-01-01

    on automatically derived point clouds of high density. Each point will be supplemented with colour and other attributes. The problems experienced in these processes and the solutions to these problems are presented. The applied tools are a combination of professional tools, free software, and of own software...... developments. Special attention is given to the quality of input images. Investigations are carried out on edges in the images. The combination of oblique and nadir images enables new possibilities in the processing. The use of the near-infrared channel besides the red, green, and blue channel of the applied...... multispectral imagery is also of advantage. Vegetation close to the object of interest can easily be removed. A second example describes the modelling of a monument by means of a non-metric camera and a standard software package. The presented results regard achieved geometric accuracy and image quality...

  6. THERMAL AND VISIBLE SATELLITE IMAGE FUSION USING WAVELET IN REMOTE SENSING AND SATELLITE IMAGE PROCESSING

    Directory of Open Access Journals (Sweden)

    A. H. Ahrari

    2017-09-01

    Full Text Available Multimodal remote sensing approach is based on merging different data in different portions of electromagnetic radiation that improves the accuracy in satellite image processing and interpretations. Remote Sensing Visible and thermal infrared bands independently contain valuable spatial and spectral information. Visible bands make enough information spatially and thermal makes more different radiometric and spectral information than visible. However low spatial resolution is the most important limitation in thermal infrared bands. Using satellite image fusion, it is possible to merge them as a single thermal image that contains high spectral and spatial information at the same time. The aim of this study is a performance assessment of thermal and visible image fusion quantitatively and qualitatively with wavelet transform and different filters. In this research, wavelet algorithm (Haar and different decomposition filters (mean.linear,ma,min and rand for thermal and panchromatic bands of Landast8 Satellite were applied as shortwave and longwave fusion method . Finally, quality assessment has been done with quantitative and qualitative approaches. Quantitative parameters such as Entropy, Standard Deviation, Cross Correlation, Q Factor and Mutual Information were used. For thermal and visible image fusion accuracy assessment, all parameters (quantitative and qualitative must be analysed with respect to each other. Among all relevant statistical factors, correlation has the most meaningful result and similarity to the qualitative assessment. Results showed that mean and linear filters make better fused images against the other filters in Haar algorithm. Linear and mean filters have same performance and there is not any difference between their qualitative and quantitative results.

  7. Automated Identification of Rivers and Shorelines in Aerial Imagery Using Image Texture

    Science.gov (United States)

    2011-01-01

    defining the criteria for segmenting the image. For these cases certain automated, unsupervised (or minimally supervised), image classification ...banks, image analysis, edge finding, photography, satellite, texture, entropy 16. SECURITY CLASSIFICATION OF: a. REPORT Unclassified b. ABSTRACT...high resolution bank geometry. Much of the globe is covered by various sorts of multi- or hyperspectral imagery and numerous techniques have been

  8. Counter Unmanned Aerial Systems Testing: Evaluation of VIS SWIR MWIR and LWIR passive imagers.

    Energy Technology Data Exchange (ETDEWEB)

    Birch, Gabriel Carlisle [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Woo, Bryana Lynn [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2017-01-01

    This report contains analysis of unmanned aerial systems as imaged by visible, short-wave infrared, mid-wave infrared, and long-wave infrared passive devices. Testing was conducted at the Nevada National Security Site (NNSS) during the week of August 15, 2016. Target images in all spectral bands are shown and contrast versus background is reported. Calculations are performed to determine estimated pixels-on-target for detection and assessment levels, and the number of pixels needed to cover a hemisphere for detection or assessment at defined distances. Background clutter challenges are qualitatively discussed for different spectral bands, and low contrast scenarios are highlighted for long-wave infrared imagers.

  9. Combining Street View and Aerial Images to Create Photo-Realistic 3D City Models

    OpenAIRE

    Ivarsson, Caroline

    2014-01-01

    This thesis evaluates two different approaches of using panoramic street view images for creating more photo-realistic 3D city models comparing to 3D city models based on only aerial images. The thesis work has been carried out at Blom Sweden AB with the use of their software and data. The main purpose of this thesis work has been to investigate if street view images can aid in creating more photo-realistic 3D city models on street level through an automatic or semi-automatic approach. Two di...

  10. Series of aerial images over Quivira National Wildlife Refuge, acquired on September 27th and 29th, 1981

    Data.gov (United States)

    US Fish and Wildlife Service, Department of the Interior — This data set is of seven georeferenced aerial images taken over Quivira National Wildlife Refuge on September 27th and 29th, 1981. This data set is a clipped,...

  11. Aerial imaging with manned aircraft for precision agriculture

    Science.gov (United States)

    Over the last two decades, numerous commercial and custom-built airborne imaging systems have been developed and deployed for diverse remote sensing applications, including precision agriculture. More recently, unmanned aircraft systems (UAS) have emerged as a versatile and cost-effective platform f...

  12. Operational Data Augmentation in Classifying Single Aerial Images of Animals

    NARCIS (Netherlands)

    Okafor, Emmanuel; Smit, Rik; Schomaker, Lambertus; Wiering, Marco

    2017-01-01

    In deep learning, data augmentation is important to increase the amount of training images to obtain higher classification accuracies. Most data-augmentation methods adopt the use of the following techniques: cropping, mirroring, color casting, scaling and rotation for creating additional training

  13. Agricultural cropland mapping using black-and-white aerial photography, Object-Based Image Analysis and Random Forests

    Science.gov (United States)

    Vogels, M. F. A.; de Jong, S. M.; Sterk, G.; Addink, E. A.

    2017-02-01

    Land-use and land-cover (LULC) conversions have an important impact on land degradation, erosion and water availability. Information on historical land cover (change) is crucial for studying and modelling land- and ecosystem degradation. During the past decades major LULC conversions occurred in Africa, Southeast Asia and South America as a consequence of a growing population and economy. Most distinct is the conversion of natural vegetation into cropland. Historical LULC information can be derived from satellite imagery, but these only date back until approximately 1972. Before the emergence of satellite imagery, landscapes were monitored by black-and-white (B&W) aerial photography. This photography is often visually interpreted, which is a very time-consuming approach. This study presents an innovative, semi-automated method to map cropland acreage from B&W photography. Cropland acreage was mapped on two study sites in Ethiopia and in The Netherlands. For this purpose we used Geographic Object-Based Image Analysis (GEOBIA) and a Random Forest classification on a set of variables comprising texture, shape, slope, neighbour and spectral information. Overall mapping accuracies attained are 90% and 96% for the two study areas respectively. This mapping method increases the timeline at which historical cropland expansion can be mapped purely from brightness information in B&W photography up to the 1930s, which is beneficial for regions where historical land-use statistics are mostly absent.

  14. Moving object detection using dynamic motion modelling from UAV aerial images.

    Science.gov (United States)

    Saif, A F M Saifuddin; Prabuwono, Anton Satria; Mahayuddin, Zainal Rasyid

    2014-01-01

    Motion analysis based moving object detection from UAV aerial image is still an unsolved issue due to inconsideration of proper motion estimation. Existing moving object detection approaches from UAV aerial images did not deal with motion based pixel intensity measurement to detect moving object robustly. Besides current research on moving object detection from UAV aerial images mostly depends on either frame difference or segmentation approach separately. There are two main purposes for this research: firstly to develop a new motion model called DMM (dynamic motion model) and secondly to apply the proposed segmentation approach SUED (segmentation using edge based dilation) using frame difference embedded together with DMM model. The proposed DMM model provides effective search windows based on the highest pixel intensity to segment only specific area for moving object rather than searching the whole area of the frame using SUED. At each stage of the proposed scheme, experimental fusion of the DMM and SUED produces extracted moving objects faithfully. Experimental result reveals that the proposed DMM and SUED have successfully demonstrated the validity of the proposed methodology.

  15. A semi-automated single day image differencing technique to identify animals in aerial imagery.

    Directory of Open Access Journals (Sweden)

    Pat Terletzky

    Full Text Available Our research presents a proof-of-concept that explores a new and innovative method to identify large animals in aerial imagery with single day image differencing. We acquired two aerial images of eight fenced pastures and conducted a principal component analysis of each image. We then subtracted the first principal component of the two pasture images followed by heuristic thresholding to generate polygons. The number of polygons represented the number of potential cattle (Bos taurus and horses (Equus caballus in the pasture. The process was considered semi-automated because we were not able to automate the identification of spatial or spectral thresholding values. Imagery was acquired concurrently with ground counts of animal numbers. Across the eight pastures, 82% of the animals were correctly identified, mean percent commission was 53%, and mean percent omission was 18%. The high commission error was due to small mis-alignments generated from image-to-image registration, misidentified shadows, and grouping behavior of animals. The high probability of correctly identifying animals suggests short time interval image differencing could provide a new technique to enumerate wild ungulates occupying grassland ecosystems, especially in isolated or difficult to access areas. To our knowledge, this was the first attempt to use standard change detection techniques to identify and enumerate large ungulates.

  16. A semi-automated single day image differencing technique to identify animals in aerial imagery.

    Science.gov (United States)

    Terletzky, Pat; Ramsey, Robert Douglas

    2014-01-01

    Our research presents a proof-of-concept that explores a new and innovative method to identify large animals in aerial imagery with single day image differencing. We acquired two aerial images of eight fenced pastures and conducted a principal component analysis of each image. We then subtracted the first principal component of the two pasture images followed by heuristic thresholding to generate polygons. The number of polygons represented the number of potential cattle (Bos taurus) and horses (Equus caballus) in the pasture. The process was considered semi-automated because we were not able to automate the identification of spatial or spectral thresholding values. Imagery was acquired concurrently with ground counts of animal numbers. Across the eight pastures, 82% of the animals were correctly identified, mean percent commission was 53%, and mean percent omission was 18%. The high commission error was due to small mis-alignments generated from image-to-image registration, misidentified shadows, and grouping behavior of animals. The high probability of correctly identifying animals suggests short time interval image differencing could provide a new technique to enumerate wild ungulates occupying grassland ecosystems, especially in isolated or difficult to access areas. To our knowledge, this was the first attempt to use standard change detection techniques to identify and enumerate large ungulates.

  17. Monitoring Active Volcanos Using Aerial Images and the Orthoview Tool

    Directory of Open Access Journals (Sweden)

    Maria Marsella

    2014-12-01

    Full Text Available In volcanic areas, where it can be difficult to perform direct surveys, digital photogrammetry techniques are rarely adopted for routine volcano monitoring. Nevertheless, they have remarkable potentialities for observing active volcanic features (e.g., fissures, lava flows and the connected deformation processes. The ability to obtain accurate quantitative data of definite accuracy in short time spans makes digital photogrammetry a suitable method for controlling the evolution of rapidly changing large-area volcanic phenomena. The systematic acquisition of airborne photogrammetric datasets can be adopted for implementing a more effective procedure aimed at long-term volcano monitoring and hazard assessment. In addition, during the volcanic crisis, the frequent acquisition of oblique digital images from helicopter allows for quasi-real-time monitoring to support mitigation actions by civil protection. These images are commonly used to update existing maps through a photo-interpretation approach that provide data of unknown accuracy. This work presents a scientific tool (Orthoview that implements a straightforward photogrammetric approach to generate digital orthophotos from single-view oblique images provided that at least four Ground Control Points (GCP and current Digital Elevation Models (DEM are available. The influence of the view geometry, of sparse and not-signalized GCP and DEM inaccuracies is analyzed for evaluating the performance of the developed tool in comparison with other remote sensing techniques. Results obtained with datasets from Etna and Stromboli volcanoes demonstrate that 2D features measured on the produced orthophotos can reach sub-meter-level accuracy.

  18. Comparisons of feature extraction algorithm based on unmanned aerial vehicle image

    Science.gov (United States)

    Xi, Wenfei; Shi, Zhengtao; Li, Dongsheng

    2017-07-01

    Feature point extraction technology has become a research hotspot in the photogrammetry and computer vision. The commonly used point feature extraction operators are SIFT operator, Forstner operator, Harris operator and Moravec operator, etc. With the high spatial resolution characteristics, UAV image is different from the traditional aviation image. Based on these characteristics of the unmanned aerial vehicle (UAV), this paper uses several operators referred above to extract feature points from the building images, grassland images, shrubbery images, and vegetable greenhouses images. Through the practical case analysis, the performance, advantages, disadvantages and adaptability of each algorithm are compared and analyzed by considering their speed and accuracy. Finally, the suggestions of how to adapt different algorithms in diverse environment are proposed.

  19. Building Change Detection from Historical Aerial Photographs Using Dense Image Matching and Object-Based Image Analysis

    Directory of Open Access Journals (Sweden)

    Stephan Nebiker

    2014-09-01

    Full Text Available A successful application of dense image matching algorithms to historical aerial photographs would offer a great potential for detailed reconstructions of historical landscapes in three dimensions, allowing for the efficient monitoring of various landscape changes over the last 50+ years. In this paper we propose the combination of image-based dense DSM (digital surface model reconstruction from historical aerial imagery with object-based image analysis for the detection of individual buildings and the subsequent analysis of settlement change. Our proposed methodology is evaluated using historical greyscale and color aerial photographs and numerous reference data sets of Andermatt, a historical town and tourism destination in the Swiss Alps. In our paper, we first investigate the DSM generation performance of different sparse and dense image matching algorithms. They demonstrate the superiority of dense matching algorithms and of the resulting historical DSMs with root mean square error values of 1–1.5 GSD (ground sampling distance and yield point densities comparable to those of recent airborne LiDAR DSMs. In the second part, we present an object-based building detection workflow mainly based on the historical DSMs and the historical imagery itself. Additional inputs are a current digital terrain model and a cadastral building database. For the case of densely matched DSMs, the evaluation yields building detection rates of 92% for grayscale and 94% for color imagery.

  20. Land Use Classification from Vhr Aerial Images Using Invariant Colour Components and Texture

    Science.gov (United States)

    Movia, A.; Beinat, A.; Sandri, T.

    2016-06-01

    Very high resolution (VHR) aerial images can provide detailed analysis about landscape and environment; nowadays, thanks to the rapid growing airborne data acquisition technology an increasing number of high resolution datasets are freely available. In a VHR image the essential information is contained in the red-green-blue colour components (RGB) and in the texture, therefore a preliminary step in image analysis concerns the classification in order to detect pixels having similar characteristics and to group them in distinct classes. Common land use classification approaches use colour at a first stage, followed by texture analysis, particularly for the evaluation of landscape patterns. Unfortunately RGB-based classifications are significantly influenced by image setting, as contrast, saturation, and brightness, and by the presence of shadows in the scene. The classification methods analysed in this work aim to mitigate these effects. The procedures developed considered the use of invariant colour components, image resampling, and the evaluation of a RGB texture parameter for various increasing sizes of a structuring element. To identify the most efficient solution, the classification vectors obtained were then processed by a K-means unsupervised classifier using different metrics, and the results were compared with respect to corresponding user supervised classifications. The experiments performed and discussed in the paper let us evaluate the effective contribution of texture information, and compare the most suitable vector components and metrics for automatic classification of very high resolution RGB aerial images.

  1. Computer vision-based orthorectification and georeferencing of aerial image sets

    Science.gov (United States)

    Faraji, Mohammad Reza; Qi, Xiaojun; Jensen, Austin

    2016-07-01

    Generating a georeferenced mosaic map from unmanned aerial vehicle (UAV) imagery is a challenging task. Direct and indirect georeferencing methods may fail to generate an accurate mosaic map due to the erroneous exterior orientation parameters stored in the inertial measurement unit (IMU), erroneous global positioning system (GPS) data, and difficulty in locating ground control points (GCPs) or having a sufficient number of GCPs. This paper presents a practical framework to orthorectify and georeference aerial images using the robust features-based matching method. The proposed georeferencing process is fully automatic and does not require any GCPs. It is also a near real-time process which can be used to determine whether aerial images taken by UAV cover the entire target area. We also extend this framework to use the inverse georeferencing process to update the IMU/GPS data which can be further used to calibrate the camera of the UAV, reduce IMU/GPS errors, and thus produce more accurate mosaic maps by employing any georeferencing method. Our experiments demonstrate the effectiveness of the proposed framework in producing comparable mosaic maps as commercial software Agisoft and the effectiveness of the extended framework in significantly reducing the errors in the IMU/GPS data.

  2. Moment feature based fast feature extraction algorithm for moving object detection using aerial images.

    Directory of Open Access Journals (Sweden)

    A F M Saifuddin Saif

    Full Text Available Fast and computationally less complex feature extraction for moving object detection using aerial images from unmanned aerial vehicles (UAVs remains as an elusive goal in the field of computer vision research. The types of features used in current studies concerning moving object detection are typically chosen based on improving detection rate rather than on providing fast and computationally less complex feature extraction methods. Because moving object detection using aerial images from UAVs involves motion as seen from a certain altitude, effective and fast feature extraction is a vital issue for optimum detection performance. This research proposes a two-layer bucket approach based on a new feature extraction algorithm referred to as the moment-based feature extraction algorithm (MFEA. Because a moment represents the coherent intensity of pixels and motion estimation is a motion pixel intensity measurement, this research used this relation to develop the proposed algorithm. The experimental results reveal the successful performance of the proposed MFEA algorithm and the proposed methodology.

  3. EVALUATION OF COLOR SETTINGS IN AERIAL IMAGES WITH THE USE OF EYE-TRACKING USER STUDY

    Directory of Open Access Journals (Sweden)

    J. Mirijovsky

    2016-06-01

    Respondents’ answers were evaluated and the most realistic and most preferable color settings were found. The advantage of the eye-tracking evaluation was that also the process of the selection of the answers was analyzed. Areas of Interest were marked around each image in the grid and the sequences of gaze movements were analyzed. Sequence chart was used for visualization and eye-tracking metrics were statistically tested. The presented paper shows the differences in the perception and preferences of aerial images with different color settings.

  4. Satellite image eavesdropping: a multidisciplinary science education project

    Energy Technology Data Exchange (ETDEWEB)

    Friedt, Jean-Michel [Association Projet Aurore, UFR-ST La Bouloie, 16, route de Gray, 25030 Besancon Cedex (France)

    2005-11-01

    Amateur reception of satellite images gathers a wide number of concepts and technologies which makes it attractive as an educational tool. We here introduce the reception of images emitted from NOAA series low-altitude Earth-orbiting satellites. We tackle various issues including the identification and prediction of the pass time of visible satellites, the building of the radio-frequency receiver and antenna after modelling their radiation pattern, and then the demodulation of the resulting audio signal for finally displaying an image of the Earth as seen from space.

  5. Information collecting system based on aerial images obtained by a small UAV for disaster prevention

    Science.gov (United States)

    Suzuki, Taro; Meguro, Jun-ichi; Amano, Yoshiharu; Hashizume, Takumi; Hirokawa, Rui; Tatsumi, Kaoru; Sato, Koichi; Takiguchi, Jun-ichi

    2007-12-01

    This paper presents a disaster prevention system using aerial images obtained by a small unmanned aerial vehicle (UAV). The immediate assessment of damage information and the continuous collection of information for constructing a revival plan are important for damage mitigation when natural disasters such as large earthquakes occur. In this study, we develop a small prototype UAV and onboard software using a GPS navigation system and propose a unique disaster prevention system that employs the small UAV and a geographical information system (GIS). We divide the period that follows a natural disaster on the basis of three processes, and we propose the use of the small UAV for gathering and integrating information into the GIS. We conducted field experiments to evaluate the proposed system employing the small UAV. From the results of our experiments, we concluded that the system is effective in disaster prevention.

  6. Best period for high spatial resolution satellite images for the detection of marks of buried structures

    Directory of Open Access Journals (Sweden)

    Dimitrios Kaimaris

    2012-06-01

    Full Text Available Improvements in sensor technology in recent decades led to the creation of ground, air and space imaging systems, whose data can be used in archaeological studies. Greece is one of the lucky areas that are rich in archaeological heritage. The detection of prehistoric/historic undiscovered constructions on satellite images or aerial photos is a complex and complicated matter. These marks are not visible from the ground, they can, however, be traced on satellite or aerial images, because of the differences in tone and texture. These differences appear as crop, soil and shadow marks. Undoubtedly, the detection of buried structures requires a suitable spatial resolution image, taken under appropriate meteorological conditions and during the best period of the vegetation growing cycle. According to the pertinent literature, detecting covered memorials may be achieved either accidentally or, usually, after a systematic investigation based on historical narratives. The purpose of this study is to determine the factors that facilitate or hinder the detection of buried structures through high spatial resolution satellite imagery. In this study, pan sharpened images from the QuickBird-2 satellite were used, of a spatial resolution of 0.60-0.70 m. This study concerns the detection of marks of the ancient Via Egnatia, from the ancient Amphipolis to Philippi (Eastern Macedonia, Greece. We studied different types of vegetation in the region and their phenological cycle. Taking into account the vegetation phenological cycle of the study area as well as the meteorological data, four pan sharpened QuickBird-2 images of a spatial resolution of 0.60–0.70 m. were used, during four different seasons. By processing the four images, we can determine the one acquired during the most appropriate conditions for the detection of buried structures. The application of this methodology in the study area had positive results, and not only was the main purpose of this

  7. Variable Selection for Road Segmentation in Aerial Images

    Science.gov (United States)

    Warnke, S.; Bulatov, D.

    2017-05-01

    For extraction of road pixels from combined image and elevation data, Wegner et al. (2015) proposed classification of superpixels into road and non-road, after which a refinement of the classification results using minimum cost paths and non-local optimization methods took place. We believed that the variable set used for classification was to a certain extent suboptimal, because many variables were redundant while several features known as useful in Photogrammetry and Remote Sensing are missed. This motivated us to implement a variable selection approach which builds a model for classification using portions of training data and subsets of features, evaluates this model, updates the feature set, and terminates when a stopping criterion is satisfied. The choice of classifier is flexible; however, we tested the approach with Logistic Regression and Random Forests, and taylored the evaluation module to the chosen classifier. To guarantee a fair comparison, we kept the segment-based approach and most of the variables from the related work, but we extended them by additional, mostly higher-level features. Applying these superior features, removing the redundant ones, as well as using more accurately acquired 3D data allowed to keep stable or even to reduce the misclassification error in a challenging dataset.

  8. Blind restoration for nonuniform aerial images using nonlocal Retinex model and shearlet-based higher-order regularization

    Science.gov (United States)

    Chen, Rui; Jia, Huizhu; Xie, Xiaodong; Gao, Wen

    2017-05-01

    Aerial images are often degraded by space-varying motion blurs and simultaneous uneven illumination. To recover a high-quality aerial image from its nonuniform version, we propose a patchwise restoration approach based on a key observation that the degree of blurring is inevitably affected by the illumination conditions. A nonlocal Retinex model is developed to accurately estimate the reflectance component from the degraded aerial image. Thereafter, the uneven illumination is corrected well. Then nonuniform coupled blurring in the enhanced reflectance image is alleviated and transformed toward uniform distribution, which will facilitate the subsequent deblurring. For constructing the multiscale sparsified regularization, the discrete shearlet transform is improved to better represent anisotropic image features in terms of directional sensitivity and selectivity. In addition, a new adaptive variant of total generalized variation is proposed to act as the structure-preserving regularizer. These complementary regularizers are elegantly integrated into an objective function. The final deblurred image with uniform illumination can be obtained by applying a fast alternating direction scheme to solve the derived function. The experimental results demonstrate that our algorithm can not only effectively remove both the space-varying illumination and motion blurs in aerial images, but also recover the abundant details of aerial scenes with top-level objective and subjective quality, and outperforms other state-of-the-art restoration methods.

  9. Accelerating Satellite Image Based Large-Scale Settlement Detection with GPU

    Energy Technology Data Exchange (ETDEWEB)

    Patlolla, Dilip Reddy [ORNL; Cheriyadat, Anil M [ORNL; Weaver, Jeanette E [ORNL; Bright, Eddie A [ORNL

    2012-01-01

    Computer vision algorithms for image analysis are often computationally demanding. Application of such algorithms on large image databases\\---- such as the high-resolution satellite imagery covering the entire land surface, can easily saturate the computational capabilities of conventional CPUs. There is a great demand for vision algorithms running on high performance computing (HPC) architecture capable of processing petascale image data. We exploit the parallel processing capability of GPUs to present a GPU-friendly algorithm for robust and efficient detection of settlements from large-scale high-resolution satellite imagery. Feature descriptor generation is an expensive, but a key step in automated scene analysis. To address this challenge, we present GPU implementations for three different feature descriptors\\-- multiscale Historgram of Oriented Gradients (HOG), Gray Level Co-Occurrence Matrix (GLCM) Contrast and local pixel intensity statistics. We perform extensive experimental evaluations of our implementation using diverse and large image datasets. Our GPU implementation of the feature descriptor algorithms results in speedups of 220 times compared to the CPU version. We present an highly efficient settlement detection system running on a multiGPU architecture capable of extracting human settlement regions from a city-scale sub-meter spatial resolution aerial imagery spanning roughly 1200 sq. kilometers in just 56 seconds with detection accuracy close to 90\\%. This remarkable speedup gained by our vision algorithm maintaining high detection accuracy clearly demonstrates that such computational advancements clearly hold the solution for petascale image analysis challenges.

  10. Velocity estimation of an airplane through a single satellite image

    Institute of Scientific and Technical Information of China (English)

    Zhuxin Zhao; Gongjian Wen; Bingwei Hui; Deren Li

    2012-01-01

    The motion information of a moving target can be recorded in a single image by a push-broom satellite. A push-broom satellite image is composed of many image lines sensed at different time instants. A method to estimate the velocity of a flying airplane from a single image based on the imagery model of the linear push-broom sensor is proposed. Some key points on the high-resolution image of the plane are chosen to determine the velocity (speed and direction). The performance of the method is tested and verified by experiments using a WorldView-1 image.%The motion information of a moving target can be recorded in a single image by a push-broom satellite.A push-broom satellite image is composed of many image lines sensed at different time instants.A method to estimate the velocity of a flying airplane from a single image based on the imagery model of the linear push-broom sensor is proposed.Some key points on the high-resolution image of the plane are chosen to determine the velocity (speed and direction).The performance of the method is tested and verified by experiments using a WorldView-1 image.

  11. Satellite image collection modeling for large area hazard emergency response

    Science.gov (United States)

    Liu, Shufan; Hodgson, Michael E.

    2016-08-01

    Timely collection of critical hazard information is the key to intelligent and effective hazard emergency response decisions. Satellite remote sensing imagery provides an effective way to collect critical information. Natural hazards, however, often have large impact areas - larger than a single satellite scene. Additionally, the hazard impact area may be discontinuous, particularly in flooding or tornado hazard events. In this paper, a spatial optimization model is proposed to solve the large area satellite image acquisition planning problem in the context of hazard emergency response. In the model, a large hazard impact area is represented as multiple polygons and image collection priorities for different portion of impact area are addressed. The optimization problem is solved with an exact algorithm. Application results demonstrate that the proposed method can address the satellite image acquisition planning problem. A spatial decision support system supporting the optimization model was developed. Several examples of image acquisition problems are used to demonstrate the complexity of the problem and derive optimized solutions.

  12. Landsat TM and ETM+ Kansas Satellite Image Database (KSID)

    Data.gov (United States)

    Kansas Data Access and Support Center — The Kansas Satellite Image Database (KSID):2000-2001 consists of terrain-corrected, precision rectified spring, summer, and fall Landsat 5 Thematic Mapper (TM) and...

  13. Kansas Satellite Image Database (KSID) 2004-2005

    Data.gov (United States)

    Kansas Data Access and Support Center — The Kansas Satellite Image Database (KSID) 2004-2005 consists of terrain-corrected, precision rectified spring, summer, and fall Landsat 5 Thematic Mapper (TM)...

  14. Registration of Urban Aerial Image and LiDAR Based on Line Vectors

    Directory of Open Access Journals (Sweden)

    Qinghong Sheng

    2017-09-01

    Full Text Available In a traditional registration of a single aerial image with airborne light detection and ranging (LiDAR data using linear features that regard line direction as a control or linear features as constraints in the solution, lacking the constraint of linear position leads to the error propagation of the adjustment model. To solve this problem, this paper presents a line vector-based registration mode (LVR in which image rays and LiDAR lines are expressed by a line vector that integrates the line direction and the line position. A registration equation of line vector is set up by coplanar imaging rays and corresponding control lines. Three types of datasets consisting of synthetic, theInternational Society for Photogrammetry and Remote Sensing (ISPRS test project, and real aerial data are used. A group of progressive experiments is undertaken to evaluate the robustness of the LVR. Experimental results demonstrate that the integrated line direction and the line position contributes a great deal to the theoretical and real accuracies of the unknowns, as well as the stability of the adjustment model. This paper provides a new suggestion that, for a single image and LiDAR data, registration in urban areas can be accomplished by accommodating rich line features.

  15. GNSS Carrier Phase Integer Ambiguity Resolution with Camera and Satellite images

    Science.gov (United States)

    Henkel, Patrick

    2015-04-01

    Ambiguity Resolution is the key to high precision position and attitude determination with GNSS. However, ambiguity resolution of kinematic receivers becomes challenging in environments with substantial multipath, limited satellite availability and erroneous cycle slip corrections. There is a need for other sensors, e.g. inertial sensors that allow an independent prediction of the position. The change of the predicted position over time can then be used for cycle slip detection and correction. In this paper, we provide a method to improve the initial ambiguity resolution for RTK and PPP with vision-based position information. Camera images are correlated with geo-referenced aerial/ satellite images to obtain an independent absolute position information. This absolute position information is then coupled with the GNSS and INS measurements in an extended Kalman filter to estimate the position, velocity, acceleration, attitude, angular rates, code multipath and biases of the accelerometers and gyroscopes. The camera and satellite images are matched based on some characteristic image points (e.g. corners of street markers). We extract these characteristic image points from the camera images by performing the following steps: An inverse mapping (homogenous projection) is applied to transform the camera images from the driver's perspective to bird view. Subsequently, we detect the street markers by performing (a) a color transformation and reduction with adaptive brightness correction to focus on relevant features, (b) a subsequent morphological operation to enhance the structure recognition, (c) an edge and corner detection to extract feature points, and (d) a point matching of the corner points with a template to recognize the street markers. We verified the proposed method with two low-cost u-blox LEA 6T GPS receivers, the MPU9150 from Invensense, the ASCOS RTK corrections and a PointGrey camera. The results show very precise and seamless position and attitude

  16. Spatial Cloud Detection and Retrieval System for Satellite Images

    Directory of Open Access Journals (Sweden)

    Ayman Nasr

    2013-01-01

    Full Text Available In last the decade we witnessed a large increase in data generated by earth observing satellites. Hence, intelligent processing of the huge amount of data received by hundreds of earth receiving stations, with specific satellite image oriented approaches, presents itself as a pressing need. One of the most important steps in earlier stages of satellite image processing is cloud detection. Satellite images having a large percentage of cloud cannot be used in further analysis. While there are many approaches that deal with different semantic meaning, there are rarely approaches that deal specifically with cloud detection and retrieval. In this paper we introduce a novel approach that spatially detect and retrieve clouds in satellite images using their unique properties .Our approach is developed as spatial cloud detection and retrieval system (SCDRS that introduce a complete framework for specific semantic retrieval system. It uses a Query by polygon (QBP paradigm for the content of interest instead of using the more conventional rectangular query by image approach. First, we extract features from the satellite images using multiple tile sizes using spatial and textural properties of cloud regions. Second, we retrieve our tiles using a parametric statistical approach within a multilevel refinement process. Our approach has been experimentally validated against the conventional ones yielding enhanced precision and recall rates in the same time it gives more precise detection of cloud coverage regions.

  17. Spectral and visual analysis of vegetation and land use with Landsat ETM+ images assested by digital aerial photography, of the Chichinautzin Biological Corridor, Morelos, Mexico

    Directory of Open Access Journals (Sweden)

    José López García

    2009-10-01

    Full Text Available A vegetation and land use map was created through both visual and digital interpretation, of Landsat ETM+ images (March 21st, 2000, of the Chichinautzin Biological Corridor (States of Morelos and Mexico. The Corridor comprises the Chichinautzin Flora and Fauna Protection Area, as well as the Tepozteco and Lagunas de Zempoala National Parks. A supervised classification was validated by field sampling as well as by interpretation of digital aerial photographs (two meters per pixel taken on November 2000. The accuracy of the visual interpretation was determined using a confusion matrix which compares the resulting land use and vegetation classes, with new reference points obtained from digital aerial photographs printed at 1:10 000 and field work. Since the process of visual interpretation of satellite imagery incorporates reference elements and criteria that are inherent to the knowledge and capabilities of the interpreter, it is advisable to perform such a visual interpretation, supported by digital aerial photographs, following an automated classification, because this reduces the error margin and thus enhances the accuracy and reliability of the products.

  18. Change Detection using 75-year Aerial Photo and Satellite Data Sets, Inexpensive Means to Obtain 6 cm Resolution Data, and Developing Opportunities for Community-oriented Remote Sensing through Photography

    Science.gov (United States)

    Rango, A.; Laliberte, A.; Winters, C.; Steele, C. M.; Browning, D. M.

    2010-12-01

    Some governmental research sites have been in existence for as many as 100 years with ground photography used for documentation starting in the early 1900s(e.g., at the USDA Jornada Experimental Range(JER)(783 km2) in south central New Mexico) If ground photography is properly documented when acquired, it can be used as a baseline for later re-photography assessments of change or for providing historical perspective. The JER historical ground photographic data base contains 3148 scenes and is available from a number of photographic archives. For long-term change detection, earth resources satellites (starting in 1972) have been valuable, but their usefulness can be enhanced when combined with data from vertical aerial photography programs begun in the mid 1930s by USDA (and other agency/aerial photo contractors). This historic aerial photography is generally an untapped data resource. Most of these aerial photos have approximately 1-2 m spatial resolution but are scattered in numerous archives around the United States. For the JER, we have been able to locate and obtain 5500 individual aerial photos which have been indispensable in evaluating vegetation change, locating treatment legacies on the landscape, and assessing rangeland treatment effectiveness. All the acquired aerial photos have been digitized and stored in the JER data base. We have also been exploring the use of small Unmanned Aerial Vehicles (UAVs) to provide hyperspatial aerial photos at about 6 cm spatial resolution to complement the 1-2 m resolution aerial photos. The areas covered by these individual UAV photos are only about 215 x 160 m, so in order to cover larger areas we have had to develop our own methods for mosaicing and analysis. In four years of flying the UAV, we have obtained about 15,000 images over JER and other western rangelands. This method is very cost effective because we are able to use an inexpensive 10 megapixel consumer-grade digital camera on the UAV as opposed to more

  19. Point cloud generation from aerial image data acquired by a quadrocopter type micro unmanned aerial vehicle and a digital still camera.

    Science.gov (United States)

    Rosnell, Tomi; Honkavaara, Eija

    2012-01-01

    The objective of this investigation was to develop and investigate methods for point cloud generation by image matching using aerial image data collected by quadrocopter type micro unmanned aerial vehicle (UAV) imaging systems. Automatic generation of high-quality, dense point clouds from digital images by image matching is a recent, cutting-edge step forward in digital photogrammetric technology. The major components of the system for point cloud generation are a UAV imaging system, an image data collection process using high image overlaps, and post-processing with image orientation and point cloud generation. Two post-processing approaches were developed: one of the methods is based on Bae Systems' SOCET SET classical commercial photogrammetric software and another is built using Microsoft(®)'s Photosynth™ service available in the Internet. Empirical testing was carried out in two test areas. Photosynth processing showed that it is possible to orient the images and generate point clouds fully automatically without any a priori orientation information or interactive work. The photogrammetric processing line provided dense and accurate point clouds that followed the theoretical principles of photogrammetry, but also some artifacts were detected. The point clouds from the Photosynth processing were sparser and noisier, which is to a large extent due to the fact that the method is not optimized for dense point cloud generation. Careful photogrammetric processing with self-calibration is required to achieve the highest accuracy. Our results demonstrate the high performance potential of the approach and that with rigorous processing it is possible to reach results that are consistent with theory. We also point out several further research topics. Based on theoretical and empirical results, we give recommendations for properties of imaging sensor, data collection and processing of UAV image data to ensure accurate point cloud generation.

  20. A multi-scale registration of urban aerial image with airborne lidar data

    Science.gov (United States)

    Huang, Shuo; Chen, Siying; Zhang, Yinchao; Guo, Pan; Chen, He

    2015-11-01

    This paper presented a multi-scale progressive registration method of airborne LiDAR data with aerial image. The cores of the proposed method lie in the coarse registration with road networks and the fine registration method using regularized building corners. During the two-stage registration, the exterior orientation parameters (EOP) are continually refined. By validation of the actual flight data of Dunhuang, the experimental result shows that the proposed method can obtain accurate results with low-precision initial EOP, also improve the automatic degree of registration.

  1. ANALYZING SPECTRAL CHARACTERISTICS OF SHADOW AREA FROM ADS-40 HIGH RADIOMETRIC RESOLUTION AERIAL IMAGES

    Directory of Open Access Journals (Sweden)

    Y.-T. Hsieh

    2016-06-01

    Full Text Available The shadows in optical remote sensing images are regarded as image nuisances in numerous applications. The classification and interpretation of shadow area in a remote sensing image are a challenge, because of the reduction or total loss of spectral information in those areas. In recent years, airborne multispectral aerial image devices have been developed 12-bit or higher radiometric resolution data, including Leica ADS-40, Intergraph DMC. The increased radiometric resolution of digital imagery provides more radiometric details of potential use in classification or interpretation of land cover of shadow areas. Therefore, the objectives of this study are to analyze the spectral properties of the land cover in the shadow areas by ADS-40 high radiometric resolution aerial images, and to investigate the spectral and vegetation index differences between the various shadow and non-shadow land covers. According to research findings of spectral analysis of ADS-40 image: (i The DN values in shadow area are much lower than in nonshadow area; (ii DN values received from shadowed areas that will also be affected by different land cover, and it shows the possibility of land cover property retrieval as in nonshadow area; (iii The DN values received from shadowed regions decrease in the visible band from short to long wavelengths due to scattering; (iv The shadow area NIR of vegetation category also shows a strong reflection; (v Generally, vegetation indexes (NDVI still have utility to classify the vegetation and non-vegetation in shadow area. The spectral data of high radiometric resolution images (ADS-40 is potential for the extract land cover information of shadow areas.

  2. Analyzing Spectral Characteristics of Shadow Area from ADS-40 High Radiometric Resolution Aerial Images

    Science.gov (United States)

    Hsieh, Yi-Ta; Wu, Shou-Tsung; Chen, Chaur-Tzuhn; Chen, Jan-Chang

    2016-06-01

    The shadows in optical remote sensing images are regarded as image nuisances in numerous applications. The classification and interpretation of shadow area in a remote sensing image are a challenge, because of the reduction or total loss of spectral information in those areas. In recent years, airborne multispectral aerial image devices have been developed 12-bit or higher radiometric resolution data, including Leica ADS-40, Intergraph DMC. The increased radiometric resolution of digital imagery provides more radiometric details of potential use in classification or interpretation of land cover of shadow areas. Therefore, the objectives of this study are to analyze the spectral properties of the land cover in the shadow areas by ADS-40 high radiometric resolution aerial images, and to investigate the spectral and vegetation index differences between the various shadow and non-shadow land covers. According to research findings of spectral analysis of ADS-40 image: (i) The DN values in shadow area are much lower than in nonshadow area; (ii) DN values received from shadowed areas that will also be affected by different land cover, and it shows the possibility of land cover property retrieval as in nonshadow area; (iii) The DN values received from shadowed regions decrease in the visible band from short to long wavelengths due to scattering; (iv) The shadow area NIR of vegetation category also shows a strong reflection; (v) Generally, vegetation indexes (NDVI) still have utility to classify the vegetation and non-vegetation in shadow area. The spectral data of high radiometric resolution images (ADS-40) is potential for the extract land cover information of shadow areas.

  3. Weed mapping in early-season maize fields using object-based analysis of unmanned aerial vehicle (UAV images.

    Directory of Open Access Journals (Sweden)

    José Manuel Peña

    Full Text Available The use of remote imagery captured by unmanned aerial vehicles (UAV has tremendous potential for designing detailed site-specific weed control treatments in early post-emergence, which have not possible previously with conventional airborne or satellite images. A robust and entirely automatic object-based image analysis (OBIA procedure was developed on a series of UAV images using a six-band multispectral camera (visible and near-infrared range with the ultimate objective of generating a weed map in an experimental maize field in Spain. The OBIA procedure combines several contextual, hierarchical and object-based features and consists of three consecutive phases: 1 classification of crop rows by application of a dynamic and auto-adaptive classification approach, 2 discrimination of crops and weeds on the basis of their relative positions with reference to the crop rows, and 3 generation of a weed infestation map in a grid structure. The estimation of weed coverage from the image analysis yielded satisfactory results. The relationship of estimated versus observed weed densities had a coefficient of determination of r(2=0.89 and a root mean square error of 0.02. A map of three categories of weed coverage was produced with 86% of overall accuracy. In the experimental field, the area free of weeds was 23%, and the area with low weed coverage (<5% weeds was 47%, which indicated a high potential for reducing herbicide application or other weed operations. The OBIA procedure computes multiple data and statistics derived from the classification outputs, which permits calculation of herbicide requirements and estimation of the overall cost of weed management operations in advance.

  4. Weed mapping in early-season maize fields using object-based analysis of unmanned aerial vehicle (UAV) images.

    Science.gov (United States)

    Peña, José Manuel; Torres-Sánchez, Jorge; de Castro, Ana Isabel; Kelly, Maggi; López-Granados, Francisca

    2013-01-01

    The use of remote imagery captured by unmanned aerial vehicles (UAV) has tremendous potential for designing detailed site-specific weed control treatments in early post-emergence, which have not possible previously with conventional airborne or satellite images. A robust and entirely automatic object-based image analysis (OBIA) procedure was developed on a series of UAV images using a six-band multispectral camera (visible and near-infrared range) with the ultimate objective of generating a weed map in an experimental maize field in Spain. The OBIA procedure combines several contextual, hierarchical and object-based features and consists of three consecutive phases: 1) classification of crop rows by application of a dynamic and auto-adaptive classification approach, 2) discrimination of crops and weeds on the basis of their relative positions with reference to the crop rows, and 3) generation of a weed infestation map in a grid structure. The estimation of weed coverage from the image analysis yielded satisfactory results. The relationship of estimated versus observed weed densities had a coefficient of determination of r(2)=0.89 and a root mean square error of 0.02. A map of three categories of weed coverage was produced with 86% of overall accuracy. In the experimental field, the area free of weeds was 23%, and the area with low weed coverage (weeds) was 47%, which indicated a high potential for reducing herbicide application or other weed operations. The OBIA procedure computes multiple data and statistics derived from the classification outputs, which permits calculation of herbicide requirements and estimation of the overall cost of weed management operations in advance.

  5. Weed Mapping in Early-Season Maize Fields Using Object-Based Analysis of Unmanned Aerial Vehicle (UAV) Images

    Science.gov (United States)

    Peña, José Manuel; Torres-Sánchez, Jorge; de Castro, Ana Isabel; Kelly, Maggi; López-Granados, Francisca

    2013-01-01

    The use of remote imagery captured by unmanned aerial vehicles (UAV) has tremendous potential for designing detailed site-specific weed control treatments in early post-emergence, which have not possible previously with conventional airborne or satellite images. A robust and entirely automatic object-based image analysis (OBIA) procedure was developed on a series of UAV images using a six-band multispectral camera (visible and near-infrared range) with the ultimate objective of generating a weed map in an experimental maize field in Spain. The OBIA procedure combines several contextual, hierarchical and object-based features and consists of three consecutive phases: 1) classification of crop rows by application of a dynamic and auto-adaptive classification approach, 2) discrimination of crops and weeds on the basis of their relative positions with reference to the crop rows, and 3) generation of a weed infestation map in a grid structure. The estimation of weed coverage from the image analysis yielded satisfactory results. The relationship of estimated versus observed weed densities had a coefficient of determination of r2=0.89 and a root mean square error of 0.02. A map of three categories of weed coverage was produced with 86% of overall accuracy. In the experimental field, the area free of weeds was 23%, and the area with low weed coverage (weeds) was 47%, which indicated a high potential for reducing herbicide application or other weed operations. The OBIA procedure computes multiple data and statistics derived from the classification outputs, which permits calculation of herbicide requirements and estimation of the overall cost of weed management operations in advance. PMID:24146963

  6. Evolving Neural Network Using Variable String Genetic Algorithm for Color Infrared Aerial Image Classification

    Institute of Scientific and Technical Information of China (English)

    FU Xiaoyang; P E R Dale; ZHANG Shuqing

    2008-01-01

    Coastal wetlands are characterized by complex patterns both in their geomorphic and ecological features.Besides field observations,it is necessary to analyze the land cover of wetlands through the color infrared (CIR) aerial photography or remote sensing image.In this paper,we designed an evolving neural network classifier using variable string genetic algorithm (VGA) for the land cover classification of CIR aerial image.With the VGA,the classifier that we designed is able to evolve automatically the appropriate number of hidden nodes for modeling the neural network topology optimally and to find a near-optimal set of connection weights globally.Then,with backpropagation algorithm (BP),it can find the best connection weights.The VGA-BP classifier,which is derived from hybrid algorithms mentioned above,is demonstrated on CIR images classification effectively.Compared with standard classifiers,such as Bayes maximum-likelihood classifier,VGA classifier and BP-MLP (multi-layer perception) classifier,it has shown that the VGA-BP classifier can have better performance on highly resolution land cover classification.

  7. New Approach for Segmentation and Extraction of Single Tree from Point Clouds Data and Aerial Images

    Science.gov (United States)

    Homainejad, A. S.

    2016-06-01

    This paper addresses a new approach for reconstructing a 3D model from single trees via Airborne Laser Scanners (ALS) data and aerial images. The approach detects and extracts single tree from ALS data and aerial images. The existing approaches are able to provide bulk segmentation from a group of trees; however, some methods focused on detection and extraction of a particular tree from ALS and images. Segmentation of a single tree within a group of trees is mostly a mission impossible since the detection of boundary lines between the trees is a tedious job and basically it is not feasible. In this approach an experimental formula based on the height of the trees was developed and applied in order to define the boundary lines between the trees. As a result, each single tree was segmented and extracted and later a 3D model was created. Extracted trees from this approach have a unique identification and attribute. The output has application in various fields of science and engineering such as forestry, urban planning, and agriculture. For example in forestry, the result can be used for study in ecologically diverse, biodiversity and ecosystem.

  8. Unmanned Aerial Vehicle Systems for Remote Estimation of Flooded Areas Based on Complex Image Processing.

    Science.gov (United States)

    Popescu, Dan; Ichim, Loretta; Stoican, Florin

    2017-02-23

    Floods are natural disasters which cause the most economic damage at the global level. Therefore, flood monitoring and damage estimation are very important for the population, authorities and insurance companies. The paper proposes an original solution, based on a hybrid network and complex image processing, to this problem. As first novelty, a multilevel system, with two components, terrestrial and aerial, was proposed and designed by the authors as support for image acquisition from a delimited region. The terrestrial component contains a Ground Control Station, as a coordinator at distance, which communicates via the internet with more Ground Data Terminals, as a fixed nodes network for data acquisition and communication. The aerial component contains mobile nodes-fixed wing type UAVs. In order to evaluate flood damage, two tasks must be accomplished by the network: area coverage and image processing. The second novelty of the paper consists of texture analysis in a deep neural network, taking into account new criteria for feature selection and patch classification. Color and spatial information extracted from chromatic co-occurrence matrix and mass fractal dimension were used as well. Finally, the experimental results in a real mission demonstrate the validity of the proposed methodologies and the performances of the algorithms.

  9. Extracting Buildings from True Color Stereo Aerial Images Using a Decision Making Strategy

    Directory of Open Access Journals (Sweden)

    Eufemia Tarantino

    2011-07-01

    Full Text Available The automatic extraction of buildings from true color stereo aerial imagery in a dense built-up area is the main focus of this paper. Our approach strategy aimed at reducing the complexity of the image content by means of a three-step procedure combining reliable geospatial image analysis techniques. Even if it is a rudimentary first step towards a more general approach, the method presented proved useful in urban sprawl studies for rapid map production in flat area by retrieving indispensable information on buildings from scanned historic aerial photography. After the preliminary creation of a photogrammetric model to manage Digital Surface Model and orthophotos, five intermediate mask-layers data (Elevation, Slope, Vegetation, Shadow, Canny, Shadow, Edges were processed through the combined use of remote sensing image processing and GIS software environments. Lastly, a rectangular building block model without roof structures (Level of Detail, LoD1 was automatically generated. System performance was evaluated with objective criteria, showing good results in a complex urban area featuring various types of building objects.

  10. Unmanned Aerial Vehicle Systems for Remote Estimation of Flooded Areas Based on Complex Image Processing

    Directory of Open Access Journals (Sweden)

    Dan Popescu

    2017-02-01

    Full Text Available Floods are natural disasters which cause the most economic damage at the global level. Therefore, flood monitoring and damage estimation are very important for the population, authorities and insurance companies. The paper proposes an original solution, based on a hybrid network and complex image processing, to this problem. As first novelty, a multilevel system, with two components, terrestrial and aerial, was proposed and designed by the authors as support for image acquisition from a delimited region. The terrestrial component contains a Ground Control Station, as a coordinator at distance, which communicates via the internet with more Ground Data Terminals, as a fixed nodes network for data acquisition and communication. The aerial component contains mobile nodes—fixed wing type UAVs. In order to evaluate flood damage, two tasks must be accomplished by the network: area coverage and image processing. The second novelty of the paper consists of texture analysis in a deep neural network, taking into account new criteria for feature selection and patch classification. Color and spatial information extracted from chromatic co-occurrence matrix and mass fractal dimension were used as well. Finally, the experimental results in a real mission demonstrate the validity of the proposed methodologies and the performances of the algorithms.

  11. Manhole Cover Localization in Aerial Images with a Deep Learning Approach

    Science.gov (United States)

    Commandre, B.; En-Nejjary, D.; Pibre, L.; Chaumont, M.; Delenne, C.; Chahinian, N.

    2017-05-01

    Urban growth is an ongoing trend and one of its direct consequences is the development of buried utility networks. Locating these networks is becoming a challenging task. While the labeling of large objects in aerial images is extensively studied in Geosciences, the localization of small objects (smaller than a building) is in counter part less studied and very challenging due to the variance of object colors, cluttered neighborhood, non-uniform background, shadows and aspect ratios. In this paper, we put forward a method for the automatic detection and localization of manhole covers in Very High Resolution (VHR) aerial and remotely sensed images using a Convolutional Neural Network (CNN). Compared to other detection/localization methods for small objects, the proposed approach is more comprehensive as the entire image is processed without prior segmentation. The first experiments using the Prades-Le-Lez and Gigean datasets show that our method is indeed effective as more than 49% of the ground truth database is detected with a precision of 75 %. New improvement possibilities are being explored such as using information on the shape of the detected objects and increasing the types of objects to be detected, thus enabling the extraction of more object specific features.

  12. Classification of Defoliated Trees Using Tree-Level Airborne Laser Scanning Data Combined with Aerial Images

    Directory of Open Access Journals (Sweden)

    Juha Hyyppa

    2010-11-01

    Full Text Available Climate change and rising temperatures have been observed to be related to the increase of forest insect damage in the boreal zone. The common pine sawfly (Diprion pini L. (Hymenoptera, Diprionidae is regarded as a significant threat to boreal pine forests. Defoliation by D. pini can cause severe growth loss and tree mortality in Scots pine (Pinus sylvestris L. (Pinaceae. In this study, logistic LASSO regression, Random Forest (RF and Most Similar Neighbor method (MSN were investigated for predicting the defoliation level of individual Scots pines using the features derived from airborne laser scanning (ALS data and aerial images. Classification accuracies from 83.7% (kappa 0.67 to 88.1% (kappa 0.76 were obtained depending on the method. The most accurate result was produced using RF with a combination of data from the two sensors, while the accuracies when using ALS and image features separately were 80.7% and 87.4%, respectively. Evidently, the combination of ALS and aerial images in detecting needle losses is capable of providing satisfactory estimates for individual trees.

  13. Wind Statistics Offshore based on Satellite Images

    DEFF Research Database (Denmark)

    Hasager, Charlotte Bay; Mouche, Alexis; Badger, Merete

    2009-01-01

    Ocean wind maps from satellites are routinely processed both at Risø DTU and CLS based on the European Space Agency Envisat ASAR data. At Risø the a priori wind direction is taken from the atmospheric model NOGAPS (Navel Operational Global Atmospheric Prediction System) provided by the U.S. Navy......’s Master Environmental Library. At CLS the a priori wind direction is taken from the ECMWF (European Centre of Medium-range Weather Forecasting). It is also possible to use other sources of wind direction e.g. the satellite-based ASCAT wind directions as demonstrated by CLS. The wind direction has to known...

  14. Pre-harvest assessment of perennial weeds in cereals based on images from unmanned aerial systems (UAS)

    DEFF Research Database (Denmark)

    Egilsson, Jon; Pedersen, Kim Steenstrup; Olsen, Søren Ingvor;

    2015-01-01

    Unmanned aerial systems (UAS) are able to deliver images of agricultural fields of high spatial and temporal resolution. It is, however, not trivial to extract quantitative information about weed infestations from images. This study contributes to weed research by using state-of-the-art computer ...

  15. Implementation of an IMU Aided Image Stacking Algorithm in a Digital Camera for Unmanned Aerial Vehicles

    Directory of Open Access Journals (Sweden)

    Ahmad Audi

    2017-07-01

    Full Text Available Images acquired with a long exposure time using a camera embedded on UAVs (Unmanned Aerial Vehicles exhibit motion blur due to the erratic movements of the UAV. The aim of the present work is to be able to acquire several images with a short exposure time and use an image processing algorithm to produce a stacked image with an equivalent long exposure time. Our method is based on the feature point image registration technique. The algorithm is implemented on the light-weight IGN (Institut national de l’information géographique camera, which has an IMU (Inertial Measurement Unit sensor and an SoC (System on Chip/FPGA (Field-Programmable Gate Array. To obtain the correct parameters for the resampling of the images, the proposed method accurately estimates the geometrical transformation between the first and the N-th images. Feature points are detected in the first image using the FAST (Features from Accelerated Segment Test detector, then homologous points on other images are obtained by template matching using an initial position benefiting greatly from the presence of the IMU sensor. The SoC/FPGA in the camera is used to speed up some parts of the algorithm in order to achieve real-time performance as our ultimate objective is to exclusively write the resulting image to save bandwidth on the storage device. The paper includes a detailed description of the implemented algorithm, resource usage summary, resulting processing time, resulting images and block diagrams of the described architecture. The resulting stacked image obtained for real surveys does not seem visually impaired. An interesting by-product of this algorithm is the 3D rotation estimated by a photogrammetric method between poses, which can be used to recalibrate in real time the gyrometers of the IMU. Timing results demonstrate that the image resampling part of this algorithm is the most demanding processing task and should also be accelerated in the FPGA in future work.

  16. Implementation of an IMU Aided Image Stacking Algorithm in a Digital Camera for Unmanned Aerial Vehicles.

    Science.gov (United States)

    Audi, Ahmad; Pierrot-Deseilligny, Marc; Meynard, Christophe; Thom, Christian

    2017-07-18

    Images acquired with a long exposure time using a camera embedded on UAVs (Unmanned Aerial Vehicles) exhibit motion blur due to the erratic movements of the UAV. The aim of the present work is to be able to acquire several images with a short exposure time and use an image processing algorithm to produce a stacked image with an equivalent long exposure time. Our method is based on the feature point image registration technique. The algorithm is implemented on the light-weight IGN (Institut national de l'information géographique) camera, which has an IMU (Inertial Measurement Unit) sensor and an SoC (System on Chip)/FPGA (Field-Programmable Gate Array). To obtain the correct parameters for the resampling of the images, the proposed method accurately estimates the geometrical transformation between the first and the N-th images. Feature points are detected in the first image using the FAST (Features from Accelerated Segment Test) detector, then homologous points on other images are obtained by template matching using an initial position benefiting greatly from the presence of the IMU sensor. The SoC/FPGA in the camera is used to speed up some parts of the algorithm in order to achieve real-time performance as our ultimate objective is to exclusively write the resulting image to save bandwidth on the storage device. The paper includes a detailed description of the implemented algorithm, resource usage summary, resulting processing time, resulting images and block diagrams of the described architecture. The resulting stacked image obtained for real surveys does not seem visually impaired. An interesting by-product of this algorithm is the 3D rotation estimated by a photogrammetric method between poses, which can be used to recalibrate in real time the gyrometers of the IMU. Timing results demonstrate that the image resampling part of this algorithm is the most demanding processing task and should also be accelerated in the FPGA in future work.

  17. Automatic aerial image shadow detection through the hybrid analysis of RGB and HIS color space

    Science.gov (United States)

    Wu, Jun; Li, Huilin; Peng, Zhiyong

    2015-12-01

    This paper presents our research on automatic shadow detection from high-resolution aerial image through the hybrid analysis of RGB and HIS color space. To this end, the spectral characteristics of shadow are firstly discussed and three kinds of spectral components including the difference between normalized blue and normalized red component - BR, intensity and saturation components are selected as criterions to obtain initial segmentation of shadow region (called primary segmentation). After that, within the normalized RGB color space and HIS color space, the shadow region is extracted again (called auxiliary segmentation) using the OTSU operation, respectively. Finally, the primary segmentation and auxiliary segmentation are combined through a logical AND-connection operation to obtain reliable shadow region. In this step, small shadow areas are removed from combined shadow region and morphological algorithms are apply to fill small holes as well. The experimental results show that the proposed approach can effectively detect the shadow region from high-resolution aerial image and in high degree of automaton.

  18. Tree detection in orchards from VHR satellite images using scale-space theory

    Science.gov (United States)

    Mahour, Milad; Tolpekin, Valentyn; Stein, Alfred

    2016-10-01

    This study focused on extracting reliable and detailed information from very High Resolution (VHR) satellite images for the detection of individual trees in orchards. The images contain detailed information on spectral and geometrical properties of trees. Their scale level, however, is insufficient for spectral properties of individual trees, because adjacent tree canopies interlock. We modeled trees using a bell shaped spectral profile. Identifying the brightest peak was challenging due to sun illumination effects caused 1 by differences in positions of the sun and the satellite sensor. Crown boundary detection was solved by using the NDVI from the same image. We used Gaussian scale-space methods that search for extrema in the scale-space domain. The procedures were tested on two orchards with different tree types, tree sizes and tree observation patterns in Iran. Validation was done using reference data derived from an UltraCam digital aerial photo. Local extrema of the determinant of the Hessian corresponded well to the geographical coordinates and the size of individual trees. False detections arising from a slight asymmetry of trees were distinguished from multiple detections of the same tree with different extents. Uncertainty assessment was carried out on the presence and spatial extents of individual trees. The study demonstrated how the suggested approach can be used for image segmentation for orchards with different types of trees. We concluded that Gaussian scale-space theory can be applied to extract information from VHR satellite images for individual tree detection. This may lead to improved decision making for irrigation and crop water requirement purposes in future studies.

  19. Feature-based registration of historical aerial images by Area Minimization

    Science.gov (United States)

    Nagarajan, Sudhagar; Schenk, Toni

    2016-06-01

    The registration of historical images plays a significant role in assessing changes in land topography over time. By comparing historical aerial images with recent data, geometric changes that have taken place over the years can be quantified. However, the lack of ground control information and precise camera parameters has limited scientists' ability to reliably incorporate historical images into change detection studies. Other limitations include the methods of determining identical points between recent and historical images, which has proven to be a cumbersome task due to continuous land cover changes. Our research demonstrates a method of registering historical images using Time Invariant Line (TIL) features. TIL features are different representations of the same line features in multi-temporal data without explicit point-to-point or straight line-to-straight line correspondence. We successfully determined the exterior orientation of historical images by minimizing the area formed between corresponding TIL features in recent and historical images. We then tested the feasibility of the approach with synthetic and real data and analyzed the results. Based on our analysis, this method shows promise for long-term 3D change detection studies.

  20. Geospatial Visualization of Global Satellite Images with Vis-EROS

    Energy Technology Data Exchange (ETDEWEB)

    Standart, G. D.; Stulken, K. R.; Zhang, Xuesong; Zong, Ziliang

    2011-04-13

    The Earth Resources Observation and Science (EROS) Center of U.S. Geological Survey is currently managing and maintaining the world largest satellite images distribution system, which provides 24/7 free download service for researchers all over the globe in many areas such as Geology, Hydrology, Climate Modeling, and Earth Sciences. A large amount of geospatial data contained in satellite images maintained by EROS is generated every day. However, this data is not well utilized due to the lack of efficient data visualization tools. This software implements a method for visualizing various characteristics of the global satellite image download requests. More specifically, Keyhole Markup Language (KML) files are generated which can be loaded into an earth browser such as Google Earth. Colored rectangles associated with stored satellite scenes are painted onto the earth browser; and the color and opacity of each rectangle is varied as a function of the popularity of the corresponding satellite image. An analysis of the geospatial information obtained relative to specified time constraints provides an ability to relate image download requests to environmental, political, and social events.

  1. Entropy-Based Block Processing for Satellite Image Registration

    Directory of Open Access Journals (Sweden)

    Ikhyun Lee

    2012-11-01

    Full Text Available Image registration is an important task in many computer vision applications such as fusion systems, 3D shape recovery and earth observation. Particularly, registering satellite images is challenging and time-consuming due to limited resources and large image size. In such scenario, state-of-the-art image registration methods such as scale-invariant feature transform (SIFT may not be suitable due to high processing time. In this paper, we propose an algorithm based on block processing via entropy to register satellite images. The performance of the proposed method is evaluated using different real images. The comparative analysis shows that it not only reduces the processing time but also enhances the accuracy.

  2. Low-altitude aerial imagery obtained with unmanned aerial systems (UAS) flights over Coast Guard Beach, Nauset Spit, Nauset Inlet, and Nauset Marsh, Cape Cod National Seashore, Eastham, Massachusetts on 1 March 2016 (JPEG images)

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — This dataset contains images obtained from unmanned aerial systems (UAS) flown in the Cape Cod National Seashore. The objective of the field work was to evaluate the...

  3. Low-altitude aerial imagery obtained with unmanned aerial systems (UAS) flights over Coast Guard Beach, Nauset Spit, Nauset Inlet, and Nauset Marsh, Cape Cod National Seashore, Eastham, Massachusetts on 1 March 2016 (JPEG images)

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — This dataset contains images obtained from unmanned aerial systems (UAS) flown in the Cape Cod National Seashore. The objective of the field work was to evaluate the...

  4. Optimizing the Attitude Control of Small Satellite Constellations for Rapid Response Imaging

    Science.gov (United States)

    Nag, S.; Li, A.

    2016-12-01

    Distributed Space Missions (DSMs) such as formation flight and constellations, are being recognized as important solutions to increase measurement samples over space and time. Given the increasingly accurate attitude control systems emerging in the commercial market, small spacecraft now have the ability to slew and point within few minutes of notice. In spite of hardware development in CubeSats at the payload (e.g. NASA InVEST) and subsystems (e.g. Blue Canyon Technologies), software development for tradespace analysis in constellation design (e.g. Goddard's TAT-C), planning and scheduling development in single spacecraft (e.g. GEO-CAPE) and aerial flight path optimizations for UAVs (e.g. NASA Sensor Web), there is a gap in open-source, open-access software tools for planning and scheduling distributed satellite operations in terms of pointing and observing targets. This paper will demonstrate results from a tool being developed for scheduling pointing operations of narrow field-of-view (FOV) sensors over mission lifetime to maximize metrics such as global coverage and revisit statistics. Past research has shown the need for at least fourteen satellites to cover the Earth globally everyday using a LandSat-like sensor. Increasing the FOV three times reduces the need to four satellites, however adds image distortion and BRDF complexities to the observed reflectance. If narrow FOV sensors on a small satellite constellation were commanded using robust algorithms to slew their sensor dynamically, they would be able to coordinately cover the global landmass much faster without compensating for spatial resolution or BRDF effects. Our algorithm to optimize constellation satellite pointing is based on a dynamic programming approach under the constraints of orbital mechanics and existing attitude control systems for small satellites. As a case study for our algorithm, we minimize the time required to cover the 17000 Landsat images with maximum signal to noise ratio fall

  5. a computational modeling for image motion velocity on focal plane of aerial & aerospace frame camera

    Science.gov (United States)

    Zhang, X.; Jin, G.; Li, Z. Y.

    As the resolving power and geometric accuracy of aerial aerospace imaging is demanded to be higher the researches in technology of IMC become very important In order to compensate the image motion on focal plane the rule of FPIMV Focal Plane Image Motion Velocity should be grasped while the posture of aircraft and the modes of imaging are under changing In this paper a reasonable computational modeling scheme to the problem is introduced Coordinates transformation method is utilized for calculation of forward FPIMV under different condition of vertical and sloped imaging meanwhile integrated with three axes posture and angle velocity of aircraft Forward FPIMV combine with pitch roll and yaw FPIMV is considered simultaneously and the derivation calculating expressions of frame camera FPIMV under different conditions is presented in detail The solution is applied to computational simulation and has been confirmed to be effective based on the calculation result and it lays the foundation for our farther researches on frame camera IMC technology Key words IMC FPIMV Focal Plane Image Motion Velocity Coordinates transformation method

  6. The best printing methods to print satellite images

    Directory of Open Access Journals (Sweden)

    G.A. Yousif

    2011-12-01

    In this paper different printing systems were used to print an image of SPOT-4 satellite, caver part of Sharm Elshekh area, Sinai, Egypt, on the same type of paper as much as possible, especially in the photography. This step is followed by measuring the experimental data, and analyzed colors to determine the best printing systems for satellite image printing data. The laser system is the more printing system where produce a wider range of color and highest densities of ink and access much color detail. Followed by the offset system which it recorded the best dot gain. Moreover, the study shows that it can use the advantages of each method according to the satellite image color and quantity to be produced.

  7. Registration of Aerial Image with Airborne LiDAR Data Based on Plücker Line

    OpenAIRE

    SHENG Qinghong; Chen, Shuwen; FEI Lijia; Liu, Jianfeng; Wang, Huinan

    2015-01-01

    Registration of aerial image with airborne LiDAR data is a key to feature extraction. A registration model based on Plücker line is proposed. The relative position and attitude relationship between the conjugate lines in LiDAR and image is determined based on Plücker linear equation, which describes line transformation in space, then coplanarity condition equation is established. Finally, coordinate transformation between image point and corresponding LiDAR point is achieved by the ...

  8. Aerial image measurement technique for automated reticle defect disposition (ARDD) in wafer fabs

    Science.gov (United States)

    Zibold, Axel M.; Schmid, Rainer M.; Stegemann, B.; Scheruebl, Thomas; Harnisch, Wolfgang; Kobiyama, Yuji

    2004-08-01

    The Aerial Image Measurement System (AIMS)* for 193 nm lithography emulation has been brought into operation successfully worldwide. A second generation system comprising 193 nm AIMS capability, mini-environment and SMIF, the AIMS fab 193 plus is currently introduced into the market. By adjustment of numerical aperture (NA), illumination type and partial illumination coherence to match the conditions in 193 nm steppers or scanners, it can emulate the exposure tool for any type of reticles like binary, OPC and PSM down to the 65 nm node. The system allows a rapid prediction of wafer printability of defects or defect repairs, and critical features, like dense patterns or contacts on the masks without the need to perform expensive image qualification consisting of test wafer exposures followed by SEM measurements. Therefore, AIMS is a mask quality verification standard for high-end photo masks and established in mask shops worldwide. The progress on the AIMS technology described in this paper will highlight that besides mask shops there will be a very beneficial use of the AIMS in the wafer fab and we propose an Automated Reticle Defect Disposition (ARDD) process. With smaller nodes, where design rules are 65 nm or less, it is expected that smaller defects on reticles will occur in increasing numbers in the wafer fab. These smaller mask defects will matter more and more and become a serious yield limiting factor. With increasing mask prices and increasing number of defects and severability on reticles it will become cost beneficial to perform defect disposition on the reticles in wafer production. Currently ongoing studies demonstrate AIMS benefits for wafer fab applications. An outlook will be given for extension of 193 nm aerial imaging down to the 45 nm node based on emulation of immersion scanners.

  9. Structure-from-Motion Using Historical Aerial Images to Analyse Changes in Glacier Surface Elevation

    Directory of Open Access Journals (Sweden)

    Nico Mölg

    2017-10-01

    Full Text Available The application of structure-from-motion (SfM to generate digital terrain models (DTMs derived from different image sources has strongly increased, the major reason for this being that processing is substantially easier with SfM than with conventional photogrammetry. To test the functionality in a demanding environment, we applied SfM and conventional photogrammetry to archival aerial images from Zmuttgletscher, a mountain glacier in Switzerland, for nine dates between 1946 and 2005 using the most popular software packages, and compared the results regarding bundle adjustment and final DTM quality. The results suggest that by using SfM it is possible to produce DTMs of similar quality as with conventional photogrammetry. Higher point cloud density and less noise allow a higher ground resolution of the final DTM, and the time effort from the user is 3–6 times smaller, while the controls of the commercial software packages Agisoft PhotoScan (Version 1.2; Agisoft, St. Petersburg, Russia and Pix4Dmapper (Version 3.0; Pix4D, Lausanne, Switzerland are limited in comparison to ERDAS photogrammetry. SfM performs less reliably when few images with little overlap are processed. Even though SfM facilitates the largely automated production of high quality DTMs, the user is not exempt from a thorough quality check, at best with reference data where available. The resulting DTM time series revealed an average change in surface elevation at the glacier tongue of −67.0 ± 5.3 m. The spatial pattern of changes over time reflects the influence of flow dynamics and the melt of clean ice and that under debris cover. With continued technological advances, we expect to see an increasing use of SfM in glaciology for a variety of purposes, also in processing archival aerial imagery.

  10. Detection of laurel wilt disease in avocado using low altitude aerial imaging.

    Science.gov (United States)

    de Castro, Ana I; Ehsani, Reza; Ploetz, Randy C; Crane, Jonathan H; Buchanon, Sherrie

    2015-01-01

    Laurel wilt is a lethal disease of plants in the Lauraceae plant family, including avocado (Persea americana). This devastating disease has spread rapidly along the southeastern seaboard of the United States and has begun to affect commercial avocado production in Florida. The main objective of this study was to evaluate the potential to discriminate laurel wilt-affected avocado trees using aerial images taken with a modified camera during helicopter surveys at low-altitude in the commercial avocado production area. The ability to distinguish laurel wilt-affected trees from other factors that produce similar external symptoms was also studied. RmodGB digital values of healthy trees and laurel wilt-affected trees, as well as fruit stress and vines covering trees were used to calculate several vegetation indices (VIs), band ratios, and VI combinations. These indices were subjected to analysis of variance (ANOVA) and an M-statistic was performed in order to quantify the separability of those classes. Significant differences in spectral values among laurel wilt affected and healthy trees were observed in all vegetation indices calculated, although the best results were achieved with Excess Red (ExR), (Red-Green) and Combination 1 (COMB1) in all locations. B/G showed a very good potential for separate the other factors with symptoms similar to laurel wilt-affected trees, such as fruit stress and vines covering trees, from laurel wilt-affected trees. These consistent results prove the usefulness of using a modified camera (RmodGB) to discriminate laurel wilt-affected avocado trees from healthy trees, as well as from other factors that cause the same symptoms and suggest performing the classification in further research. According to our results, ExR and B/G should be utilized to develop an algorithm or decision rules to classify aerial images, since they showed the highest capacity to discriminate laurel wilt-affected trees. This methodology may allow the rapid detection

  11. Detection of laurel wilt disease in avocado using low altitude aerial imaging.

    Directory of Open Access Journals (Sweden)

    Ana I de Castro

    Full Text Available Laurel wilt is a lethal disease of plants in the Lauraceae plant family, including avocado (Persea americana. This devastating disease has spread rapidly along the southeastern seaboard of the United States and has begun to affect commercial avocado production in Florida. The main objective of this study was to evaluate the potential to discriminate laurel wilt-affected avocado trees using aerial images taken with a modified camera during helicopter surveys at low-altitude in the commercial avocado production area. The ability to distinguish laurel wilt-affected trees from other factors that produce similar external symptoms was also studied. RmodGB digital values of healthy trees and laurel wilt-affected trees, as well as fruit stress and vines covering trees were used to calculate several vegetation indices (VIs, band ratios, and VI combinations. These indices were subjected to analysis of variance (ANOVA and an M-statistic was performed in order to quantify the separability of those classes. Significant differences in spectral values among laurel wilt affected and healthy trees were observed in all vegetation indices calculated, although the best results were achieved with Excess Red (ExR, (Red-Green and Combination 1 (COMB1 in all locations. B/G showed a very good potential for separate the other factors with symptoms similar to laurel wilt-affected trees, such as fruit stress and vines covering trees, from laurel wilt-affected trees. These consistent results prove the usefulness of using a modified camera (RmodGB to discriminate laurel wilt-affected avocado trees from healthy trees, as well as from other factors that cause the same symptoms and suggest performing the classification in further research. According to our results, ExR and B/G should be utilized to develop an algorithm or decision rules to classify aerial images, since they showed the highest capacity to discriminate laurel wilt-affected trees. This methodology may allow the

  12. Comparison of Satellite Image Enhancement Techniques in Wavelet Domain

    Directory of Open Access Journals (Sweden)

    K. Narasimhan

    2012-12-01

    Full Text Available In this study, a comparison of various existing satellite image resolution enhancement techniques in wavelet domain is done. Each method is analysed quantitatively and visually. There are various wavelet domain based methods such as Wavelet Zero Padding, Dual Tree-Complex Wavelet Transform, Discrete Wavelet Transform, Cycle Spinning and Undecimated Wavelet Transform. On the basis of analysis, the most efficient method is proposed. The algorithms take the low resolution image as the input image and then wavelet transformation using daubechies (db3 is used to decompose the input image into different sub band images containing high and low frequency component. Then these subband images along with the input image are interpolated followed by combining all these images to generate a new resolution enhanced image by an inverse process.

  13. Quantitative analysis of geomorphic processes using satellite image data at different scales

    Science.gov (United States)

    Williams, R. S., Jr.

    1985-01-01

    When aerial and satellite photographs and images are used in the quantitative analysis of geomorphic processes, either through direct observation of active processes or by analysis of landforms resulting from inferred active or dormant processes, a number of limitations in the use of such data must be considered. Active geomorphic processes work at different scales and rates. Therefore, the capability of imaging an active or dormant process depends primarily on the scale of the process and the spatial-resolution characteristic of the imaging system. Scale is an important factor in recording continuous and discontinuous active geomorphic processes, because what is not recorded will not be considered or even suspected in the analysis of orbital images. If the geomorphic process of landform change caused by the process is less than 200 m in x to y dimension, then it will not be recorded. Although the scale factor is critical, in the recording of discontinuous active geomorphic processes, the repeat interval of orbital-image acquisition of a planetary surface also is a consideration in order to capture a recurring short-lived geomorphic process or to record changes caused by either a continuous or a discontinuous geomorphic process.

  14. Satellite Imaging with Adaptive Optics on a 1 M Telescope

    Science.gov (United States)

    Bennet, F.; Price, I.; Rigaut, F.; Copeland, M.

    2016-09-01

    The Research School of Astronomy and Astrophysics at the Mount Stromlo Observatory in Canberra, Australia, have been developing adaptive optic (AO) systems for space situational awareness applications. We report on the development and demonstration of an AO system for satellite imaging using a 1 m telescope. The system uses the orbiting object as a natural guide star to measure atmospheric turbulence, and a deformable mirror to provide an optical correction. The AO system utilised modern, high speed and low noise EMCCD technology on both the wavefront sensor and imaging camera to achieve high performance, achieving a Strehl ratio in excess of 30% at 870 nm. Images are post processed with lucky imaging algorithms to further improve the final image quality. We demonstrate the AO system on stellar targets and Iridium satellites, achieving a near diffraction limited full width at half maximum. A specialised realtime controller allows our system to achieve a bandwidth above 100 Hz, with the wavefront sensor and control loop running at 2 kHz. The AO systems we are developing show how ground-based optical sensors can be used to manage the space environment. AO imaging systems can be used for satellite surveillance, while laser ranging can be used to determine precise orbital data used in the critical conjunction analysis required to maintain a safe space environment. We have focused on making this system compact, expandable, and versatile. We are continuing to develop this platform for other space situational awareness applications such as geosynchronous satellite astrometry, space debris characterisation, satellite imaging, and ground-to-space laser communication.

  15. Simultaneous Fusion and Denoising of Panchromatic and Multispectral Satellite Images

    Science.gov (United States)

    Ragheb, Amr M.; Osman, Heba; Abbas, Alaa M.; Elkaffas, Saleh M.; El-Tobely, Tarek A.; Khamis, S.; Elhalawany, Mohamed E.; Nasr, Mohamed E.; Dessouky, Moawad I.; Al-Nuaimy, Waleed; Abd El-Samie, Fathi E.

    2012-12-01

    To identify objects in satellite images, multispectral (MS) images with high spectral resolution and low spatial resolution, and panchromatic (Pan) images with high spatial resolution and low spectral resolution need to be fused. Several fusion methods such as the intensity-hue-saturation (IHS), the discrete wavelet transform, the discrete wavelet frame transform (DWFT), and the principal component analysis have been proposed in recent years to obtain images with both high spectral and spatial resolutions. In this paper, a hybrid fusion method for satellite images comprising both the IHS transform and the DWFT is proposed. This method tries to achieve the highest possible spectral and spatial resolutions with as small distortion in the fused image as possible. A comparison study between the proposed hybrid method and the traditional methods is presented in this paper. Different MS and Pan images from Landsat-5, Spot, Landsat-7, and IKONOS satellites are used in this comparison. The effect of noise on the proposed hybrid fusion method as well as the traditional fusion methods is studied. Experimental results show the superiority of the proposed hybrid method to the traditional methods. The results show also that a wavelet denoising step is required when fusion is performed at low signal-to-noise ratios.

  16. Image Dodging Algorithm for GF-1 Satellite WFV Imagery

    Directory of Open Access Journals (Sweden)

    HAN Jie

    2016-12-01

    Full Text Available Image dodging method is one of the important processes that determines whether the mosaicking image can be used for remote sensing quantitative application. GF-1 satellite is the first satellite in CHEOS (Chinese high-resolution earth observation system. WFV multispectral sensor is one of the instruments onboard GF-1 satellite which consist of four cameras to mosaic imaging. According to the characteristics of WFV sensor, this paper proposes an image dodging algorithm based on cross/inter-radiometric calibration method. First, the traditional cross calibration method is applied to obtain the calibration coefficients of one WFV camera. Then statistical analysis and simulation methods are adopted to build the correlation models of DN and TOA (top of atmosphere radiances between adjacent cameras. The proposed method can not only accomplish the radiation performance transfer, but also can fulfill the image dodging. The experimental results show the cross/inter-radiometric calibration coefficients in this paper can effectively eliminate the radiation inconsistency problem of the adjacent camera image which realizes the image dodging. So our proposed dodging method can provide an important reference for other similar sensor in future.

  17. Integrating Spray Plane-Based Remote Sensing and Rapid Image Processing with Variable-Rate Aerial Application.

    Science.gov (United States)

    A remote sensing and variable rate application system was configured for agricultural aircraft. This combination system has the potential of providing a completely integrated solution for all aspects of aerial site-specific application and includes remote sensing, image processing and georegistratio...

  18. Integrated use of satellite images, DEMs, soil and substrate data in studying mountainous lands

    Science.gov (United States)

    Giannetti, Fabio; Montanarella, Luca; Salandin, Roberto

    A method based on the integration into a GIS of satellite images of different spatial resolution (Landsat TM and SPOT), Digital Elevation Models, geo-lithological maps and some soil-landscape data was developed and applied to a test area on a sector of the Italian northwestern Alps in the Piemonte region (Pellice, Po, Varaita and Maira valleys southwest of Torino). The main working steps performed (using GIS software) in this area were: (1) acquisition of geo-lithological and geomorphological maps available and a first definition of homogeneous zones obtained by joining different classes with pedogenic criteria; (2) processing and classification of satellite images to define homogeneous areas with reference to prevailing land cover, land use pattern, relief shape and spectral characters; (3) integration of the previous two layers to obtain a first set of cartographic units showing a distinctive and often repetitive pattern of land form, land cover and parent material; and (4) processing DEMs (slope and aspect), soil or soil-landscape data in order to refine data and characterise the units. The resulting cartographic units were superimposed on a soil-landscape map realised by means of stereoscopic interpretation of aerial photographs by IPLA at the same scale (1:250,000). This comparison was used to verify the correctness of the satellite image processing steps and consistency with the map scale used. A larger scale application was also developed for grassland at 1:50,000 scale to demonstrate the practical use of remote sensing and GIS data in assisting mountainous land development.

  19. Engaging with the Canopy—Multi-Dimensional Vegetation Mark Visualisation Using Archived Aerial Images

    Directory of Open Access Journals (Sweden)

    Geert Verhoeven

    2016-09-01

    Full Text Available Using Montarice in central Adriatic Italy as a case study, this paper focuses on the extraction of the spectral (i.e., plant colour and geometrical (i.e., plant height components of a crop canopy from archived aerial photographs, treating both parameters as proxies for archaeological prospection. After the creation of orthophotographs and a canopy height model using image-based modelling, new archaeological information is extracted from this vegetation model by applying relief-enhancing visualisation techniques. Through interpretation of the resulting data, a combination of the co-registered spectral and geometrical vegetation dimensions clearly add new depth to interpretative mapping, which is typically based solely on colour differences in orthophotographs.

  20. Extraction and Reconstruction of Zebra Crossings from High Resolution Aerial Images

    Directory of Open Access Journals (Sweden)

    Yanbiao Sun

    2016-07-01

    Full Text Available In this paper, an automatic approach for zebra crossing extraction and reconstruction from high-resolution aerial images is proposed. In the extraction procedure, zebra crossings are extracted by the JointBoost classifier based on GLCM (Gray Level Co-occurrence Matrix features and 2D Gabor Features. In the reconstruction procedure, a geometric parameter model based on spatial repeatability relationships is globally fitted to reconstruct the geometric shape of zebra crossings. Additionally, a group of representative experiments is conducted to test the proposed method under interfered conditions, such as zebra crossings covered by pedestrians, shadows and color fading. Furthermore, the performance of the proposed extraction method is compared with the template matching method. Finally, the results show the validation of our proposed method, both in the extraction and reconstruction of zebra crossings.

  1. Evaluation of Different Irrigation Methods for an Apple Orchard Using an Aerial Imaging System

    Directory of Open Access Journals (Sweden)

    Duke M. Bulanon

    2016-06-01

    Full Text Available Regular monitoring and assessment of crops is one of the keys to optimal crop production. This research presents the development of a monitoring system called the Crop Monitoring and Assessment Platform (C-MAP. The C-MAP is composed of an image acquisition unit which is an off-the-shelf unmanned aerial vehicle (UAV equipped with a multispectral camera (near-infrared, green, blue, and an image processing and analysis component. The experimental apple orchard at the Parma Research and Extension Center of the University of Idaho was used as the target for monitoring and evaluation. Five experimental rows of the orchard were randomly treated with five different irrigation methods. An image processing algorithm to detect individual trees was developed to facilitate the analysis of the rows and it was able to detect over 90% of the trees. The image analysis of the experimental rows was based on vegetation indices and results showed that there was a significant difference in the Enhanced Normalized Difference Vegetation Index (ENDVI among the five different irrigation methods. This demonstrates that the C-MAP has very good potential as a monitoring tool for orchard management.

  2. A New Approach to Urban Road Extraction Using High-Resolution Aerial Image

    Directory of Open Access Journals (Sweden)

    Jianhua Wang

    2016-07-01

    Full Text Available Road information is fundamental not only in the military field but also common daily living. Automatic road extraction from a remote sensing images can provide references for city planning as well as transportation database and map updating. However, owing to the spectral similarity between roads and impervious structures, the current methods solely using spectral characteristics are often ineffective. By contrast, the detailed information discernible from the high-resolution aerial images enables road extraction with spatial texture features. In this study, a knowledge-based method is established and proposed; this method incorporates the spatial texture feature into urban road extraction. The spatial texture feature is initially extracted by the local Moran’s I, and the derived texture is added to the spectral bands of image for image segmentation. Subsequently, features like brightness, standard deviation, rectangularity, aspect ratio, and area are selected to form the hypothesis and verification model based on road knowledge. Finally, roads are extracted by applying the hypothesis and verification model and are post-processed based on the mathematical morphology. The newly proposed method is evaluated by conducting two experiments. Results show that the completeness, correctness, and quality of the results could reach approximately 94%, 90% and 86% respectively, indicating that the proposed method is effective for urban road extraction.

  3. Satellite and Aerial Remote Sensing in Support of Disaster Response Operations Conducted by the Texas Division of Emergency Management

    Science.gov (United States)

    Wells, G. L.; Tapley, B. D.; Bettadpur, S. V.; Howard, T.; Porter, B.; Smith, S.; Teng, L.; Tapley, C.

    2014-12-01

    The effective use of remote sensing products as guidance to emergency managers and first responders during field operations requires close coordination and communication with state-level decision makers, incident commanders and the leaders of individual strike teams. Information must be tailored to meet the needs of different emergency support functions and must contain current (ideally near real-time) data delivered in standard formats in time to influence decisions made under rapidly changing conditions. Since 2003, a representative of the University of Texas Center for Space Research (CSR) has served as a member of the Governor's Emergency Management Council and has directed the flow of information from remote sensing observations and high performance computing modeling and simulations to the Texas Division of Emergency Management in the State Operations Center. The CSR team has supported response and recovery missions resulting from hurricanes, tornadoes, flash floods, wildfires, oil spills and other natural and man-made disasters in Texas and surrounding states. Through web mapping services, state emergency managers and field teams have received threat model forecasts, real-time vehicle tracking displays and imagery to support search-and-clear operations before hurricane landfall, search-and-rescue missions following floods, tactical wildfire suppression, pollution monitoring and hazardous materials detection. Data servers provide near real-time satellite imagery collected by CSR's direct broadcast receiving system and post data products delivered during activations of the United Nations International Charter on Space and Major Disasters. In the aftermath of large-scale events, CSR is charged with tasking state aviation resources, including the Air National Guard and Texas Civil Air Patrol, to acquire geolocated aerial photography of the affected region for wide area damage assessment. A data archive for each disaster is available online for years following

  4. Classification of Pansharpened Urban Satellite Images

    DEFF Research Database (Denmark)

    Palsson, Frosti; Sveinsson, Johannes R.; Benediktsson, Jon Atli

    2012-01-01

    The classification of high resolution urban remote sensing imagery is addressed with the focus on classification of imagery that has been pansharpened by a number of different pansharpening methods. The pansharpening process introduces some spectral and spatial distortions in the resulting fused...... multispectral image, the amount of which highly varies depending on which pansharpening technique is used. In the majority of the pansharpening techniques that have been proposed, there is a compromise between the spatial enhancement and the spectral consistency. Here we study the effects of the spectral...... information from the panchromatic data. Random Forests (RF) and Support Vector Machines (SVM) will be used as classifiers. Experiments are done for three different datasets that have been obtained by two different imaging sensors, IKONOS and QuickBird. These sensors deliver multispectral images that have four...

  5. An asymmetric re-weighting method for the precision combined bundle adjustment of aerial oblique images

    Science.gov (United States)

    Xie, Linfu; Hu, Han; Wang, Jingxue; Zhu, Qing; Chen, Min

    2016-07-01

    Combined bundle adjustment is a fundamental step in the processing of massive oblique images. Traditional bundle adjustment designed for nadir images gives identical weights to different parts of image point observations made from different directions, due to the assumption that the errors in the observations follow the same Gaussian distribution. However, because of their large tilt angles, aerial oblique images have trapezoidal footprints on the ground, and their areas correspond to conspicuously different ground sample distances. The errors in different observations no longer conform to the above assumption, which leads to suboptimal bundle adjustment accuracy and restricts subsequent 3D applications. To model the distribution of the errors correctly for the combined bundle adjustment of oblique images, this paper proposes an asymmetric re-weighting method. The scale of each pixel is used to determine a re-weighting factor, and each pixel is subsequently projected onto the ground to identify another anisotropic re-weighting factor using the shape of its quadrangle. Next, these two factors are integrated into the combined bundle adjustment using asymmetric weights for the image point observations; greater weights are assigned to observations with fine resolutions, and those with coarse resolutions are penalized. This paper analyzes urban and rural images captured by three different five-angle camera systems, from both proprietary datasets and the ISPRS/EuroSDR benchmark. The results reveal that the proposed method outperforms the traditional method in both back-projected and triangulated precision by approximately 5-10% in most cases. Furthermore, the misalignments of point clouds generated by the different cameras are significantly alleviated after combined bundle adjustment.

  6. Autonomous Planetary 3-D Reconstruction From Satellite Images

    DEFF Research Database (Denmark)

    Denver, Troelz

    1999-01-01

    is discussed.Based on such features, 3-D representations may be compiled from two or more 2-D satellite images. The main purposes of such a mapping system are extraction of landing sites, objects of scientific interest and general planetary surveying. All data processing is performed autonomously onboard...

  7. Analytical models integrated with satellite images for optimized pest management

    Science.gov (United States)

    The global field protection (GFP) was developed to protect and optimize pest management resources integrating satellite images for precise field demarcation with physical models of controlled release devices of pesticides to protect large fields. The GFP was implemented using a graphical user interf...

  8. Interferometric Imaging of Geostationary Satellites: Signal-to-Noise Considerations

    Science.gov (United States)

    2011-09-01

    and the extent to which they cover the necessary portions of the UV plane . Once the photon counting noise becomes smaller than the UV coverage noise, ad...satellites,” in Proc. SPIE 4091, Imaging Technology and Telescopes, J. W. Bilbro, J. B. Breckinridge, R. A. Carreras , S. R. Czyzak, M. J. Eckart, R. D...SPIE 4091, Imaging Technology and Telescopes, J. W. Bilbro, J. B. Breckinridge, R. A. Carreras , S. R. Czyzak, M. J. Eckart, R. D. Fiete, and P. S

  9. 3-D Reconstruction From Satellite Images

    DEFF Research Database (Denmark)

    Denver, Troelz

    1999-01-01

    The aim of this project has been to implement a software system, that is able to create a 3-D reconstruction from two or more 2-D photographic images made from different positions. The height is determined from the disparity difference of the images. The general purpose of the system is mapping o......, where various methods have been tested in order to optimize the performance. The match results are used in the reconstruction part to establish a 3-D digital representation and finally, different presentation forms are discussed....

  10. The cradle of pyramids in satellite images

    CERN Document Server

    Sparavigna, Amelia Carolina

    2011-01-01

    We propose the use of image processing to enhance the Google Maps of some archaeological areas of Egypt. In particular we analyse that place which is considered the cradle of pyramids, where it was announced the discovery of a new pyramid by means of an infrared remote sensing.

  11. An Image Matching Algorithm Integrating Global SRTM and Image Segmentation for Multi-Source Satellite Imagery

    Directory of Open Access Journals (Sweden)

    Xiao Ling

    2016-08-01

    Full Text Available This paper presents a novel image matching method for multi-source satellite images, which integrates global Shuttle Radar Topography Mission (SRTM data and image segmentation to achieve robust and numerous correspondences. This method first generates the epipolar lines as a geometric constraint assisted by global SRTM data, after which the seed points are selected and matched. To produce more reliable matching results, a region segmentation-based matching propagation is proposed in this paper, whereby the region segmentations are extracted by image segmentation and are considered to be a spatial constraint. Moreover, a similarity measure integrating Distance, Angle and Normalized Cross-Correlation (DANCC, which considers geometric similarity and radiometric similarity, is introduced to find the optimal correspondences. Experiments using typical satellite images acquired from Resources Satellite-3 (ZY-3, Mapping Satellite-1, SPOT-5 and Google Earth demonstrated that the proposed method is able to produce reliable and accurate matching results.

  12. Satellite images analysis for shadow detection and building height estimation

    Science.gov (United States)

    Liasis, Gregoris; Stavrou, Stavros

    2016-09-01

    Satellite images can provide valuable information about the presented urban landscape scenes to remote sensing and telecommunication applications. Obtaining information from satellite images is difficult since all the objects and their surroundings are presented with feature complexity. The shadows cast by buildings in urban scenes can be processed and used for estimating building heights. Thus, a robust and accurate building shadow detection process is important. Region-based active contour models can be used for satellite image segmentation. However, spectral heterogeneity that usually exists in satellite images and the feature similarity representing the shadow and several non-shadow regions makes building shadow detection challenging. In this work, a new automated method for delineating building shadows is proposed. Initially, spectral and spatial features of the satellite image are utilized for designing a custom filter to enhance shadows and reduce intensity heterogeneity. An effective iterative procedure using intensity differences is developed for tuning and subsequently selecting the most appropriate filter settings, able to highlight the building shadows. The response of the filter is then used for automatically estimating the radiometric property of the shadows. The customized filter and the radiometric feature are utilized to form an optimized active contour model where the contours are biased to delineate shadow regions. Post-processing morphological operations are also developed and applied for removing misleading artefacts. Finally, building heights are approximated using shadow length and the predefined or estimated solar elevation angle. Qualitative and quantitative measures are used for evaluating the performance of the proposed method for both shadow detection and building height estimation.

  13. AN AERIAL-IMAGE DENSE MATCHING APPROACH BASED ON OPTICAL FLOW FIELD

    Directory of Open Access Journals (Sweden)

    W. Yuan

    2016-06-01

    Full Text Available Dense matching plays an important role in many fields, such as DEM (digital evaluation model producing, robot navigation and 3D environment reconstruction. Traditional approaches may meet the demand of accuracy. But the calculation time and out puts density is hardly be accepted. Focus on the matching efficiency and complex terrain surface matching feasibility an aerial image dense matching method based on optical flow field is proposed in this paper. First, some high accurate and uniformed control points are extracted by using the feature based matching method. Then the optical flow is calculated by using these control points, so as to determine the similar region between two images. Second, the optical flow field is interpolated by using the multi-level B-spline interpolation in the similar region and accomplished the pixel by pixel coarse matching. Final, the results related to the coarse matching refinement based on the combined constraint, which recognizes the same points between images. The experimental results have shown that our method can achieve per-pixel dense matching points, the matching accuracy achieves sub-pixel level, and fully meet the three-dimensional reconstruction and automatic generation of DSM-intensive matching’s requirements. The comparison experiments demonstrated that our approach’s matching efficiency is higher than semi-global matching (SGM and Patch-based multi-view stereo matching (PMVS which verifies the feasibility and effectiveness of the algorithm.

  14. Relationship analysis between transient thermal control mode and image quality for an aerial camera.

    Science.gov (United States)

    Liu, Weiyi; Xu, Yongsen; Yao, Yuan; Xu, Yulei; Shen, Honghai; Ding, Yalin

    2017-02-01

    Thermal control and temperature uniformity are important factors for aerial cameras. This paper describes the problems with existing systems and introduces modifications. The modifications have improved the temperature uniformity from 12.8°C to 4.5°C, and they enable images to be obtained at atmospheric and low pressures (35.4 KPa). First, thermal optical analysis of the camera is performed by using the finite element analysis method. This modeled the effect of temperature level and temperature gradient on imaging. Based on the results of the analysis, the corresponding improvements to the thermal control measures are implemented to improve the temperature uniformity. The relationship between the temperature control mode and temperature uniformity is analyzed. The improved temperature field corresponding to the thermal optical analysis is studied. Taking into account that the convection will be affected by the low pressure, the paper analyzes the thermal control effect, and imaging results are obtained in low pressure. The experimental results corroborate the analyses.

  15. An Aerial-Image Dense Matching Approach Based on Optical Flow Field

    Science.gov (United States)

    Yuan, Wei; Chen, Shiyu; Zhang, Yong; Gong, Jianya; Shibasaki, Ryosuke

    2016-06-01

    Dense matching plays an important role in many fields, such as DEM (digital evaluation model) producing, robot navigation and 3D environment reconstruction. Traditional approaches may meet the demand of accuracy. But the calculation time and out puts density is hardly be accepted. Focus on the matching efficiency and complex terrain surface matching feasibility an aerial image dense matching method based on optical flow field is proposed in this paper. First, some high accurate and uniformed control points are extracted by using the feature based matching method. Then the optical flow is calculated by using these control points, so as to determine the similar region between two images. Second, the optical flow field is interpolated by using the multi-level B-spline interpolation in the similar region and accomplished the pixel by pixel coarse matching. Final, the results related to the coarse matching refinement based on the combined constraint, which recognizes the same points between images. The experimental results have shown that our method can achieve per-pixel dense matching points, the matching accuracy achieves sub-pixel level, and fully meet the three-dimensional reconstruction and automatic generation of DSM-intensive matching's requirements. The comparison experiments demonstrated that our approach's matching efficiency is higher than semi-global matching (SGM) and Patch-based multi-view stereo matching (PMVS) which verifies the feasibility and effectiveness of the algorithm.

  16. Shoreline-change Rates of the Barrier Islands in Nakdong River Estuary Using Aerial Photography and SPOT-5 Image

    Directory of Open Access Journals (Sweden)

    Sang-Hun Jeong

    2013-03-01

    Full Text Available Shoreline data of the barrier islands in Nakdong River Estuary for the last three decades wereassembled using six sets of aerial photographs and seven sets of satellite images. Canny Algorithm wasapplied to untreated data in order to obtain a wet-dry boundary as a proxy shoreline. Digital ShorelineAnalysis System (DSAS 4.0 was used to estimate the rate of shoreline changes in terms of five statisticalvariables; SCE (Shoreline Change Envelope, NSM (Net Shoreline Movement, EPR(End Point Rate, LRR(Linear Regression Rate, and LMS (Least Median of Squares. The shoreline in Jinwoodo varieddifferently from one place to another during the last three decades; the west tail has advanced (i.e., seawardor southward, the west part has regressed, the south part has advanced, and the east part has regressed.After the 2000s, the rate of shoreline changes (−2.5~6.7 m/yr increased and the east advanced. Theshoreline in Shinjado shows a counterclockwise movement; the west part has advanced, but the east part hasretreated. Since Shinjado was built in its present form, the west part became stable, but the east part hasregressed faster. The rate of shoreline changes (−16.0~12.0 m/yr in Shinjado is greater than that ofJinwoodo. The shoreline in Doyodeung has advanced at a rate of 31.5 m/yr. Since Doyodeung was built inits present form, the south part has regressed at the rate of −18.2 m/yr, but the east and west parts haveadvanced at the rate of 13.5~14.3 m/yr. Based on Digital Shoreline Analysis, shoreline changes in thebarrier islands in the Nakdong River Estuary have varied both temporally and spatially, although the exactreason for the shoreline changes requires more investigation.

  17. FUNCTIONALITY ASSESSMENT OF ALGORITHMS FOR THE COLORING OF IMAGES IN TERMS OF INCREASING RADIOMETRIC VALUES OF AERIAL PHOTOGRAPHS ARCHIVES

    Directory of Open Access Journals (Sweden)

    Ewiak Ireneusz

    2016-12-01

    Full Text Available Available on the commercial market are a number of algorithms that enable assigning to pixels of a monochrome digital image suitable colors according to a strictly defined schedule. These algorithms have been recently used by professional film studios involved in the coloring of archival productions. This article provides an overview on the functionality of coloring algorithms in terms of their use to improve the interpretation quality of historical, black - and - white aerial photographs. The analysis covered intuitive (Recolored programs, as well as more advanced (Adobe After Effect, DaVinci Resolve programs. The use of their full functionality was limited by the too large information capacity of aerial photograph images. Black - and - white historical aerial photographs, which interpretation quality in many cases does not meet the criteria posed on photogrammetric developments, require an increase of their readability. The solution in this regard may be the process of coloring images. The authors of this article conducted studies aimed to determine to what extent the tested coloring algorithms enable an automatic detection of land cover elements on historical aerial photographs and provide color close to the natural. Used in the studies were archival black - and - white aerial photographs of the western part of Warsaw district made available by the Main Centre of Geodetic and Cartographic Documentation , the selection of which was associated with the presence in this area of various elements of land cover, such as water, forests, crops, exposed soils and also anthropogenic objects. In the analysis of different algorithms are included: format and size of the image, degree of automation of the process, degree of compliance of the result and processing time. The accuracy of the coloring process was different for each class of objects mapped on the photograph. The main limitation of the coloring process created shadows of anthropogenic objects

  18. COMPARATIVE ANALYSIS OF SATELLITE IMAGE PRE-PROCESSING TECHNIQUES

    Directory of Open Access Journals (Sweden)

    T. Sree Sharmila

    2013-01-01

    Full Text Available Satellite images are corrupted by noise in its acquisition and transmission. The removal of noise from the image by attenuating the high frequency image components, removes some important details as well. In order to retain the useful information and improve the visual appearance, an effective denoising and resolution enhancement techniques are required. In this research, Hybrid Directional Lifting (HDL technique is proposed to retain the important details of the image and improve the visual appearance. The Discrete Wavelet Transform (DWT based interpolation technique is developed for enhancing the resolution of the denoised image. The performance of the proposed techniques are tested by Land Remote-Sensing Satellite (LANDSAT images, using the quantitative performance measure, Peak Signal to Noise Ratio (PSNR and computation time to show the significance of the proposed techniques. The PSNR of the HDL technique increases 1.02 dB compared to the standard denoising technique and the DWT based interpolation technique increases 3.94 dB. From the experimental results it reveals that newly developed image denoising and resolution enhancement techniques improve the image visual quality with rich textures.

  19. Identification of geostationary satellites using polarization data from unresolved images

    Science.gov (United States)

    Speicher, Andy

    In order to protect critical military and commercial space assets, the United States Space Surveillance Network must have the ability to positively identify and characterize all space objects. Unfortunately, positive identification and characterization of space objects is a manual and labor intensive process today since even large telescopes cannot provide resolved images of most space objects. Since resolved images of geosynchronous satellites are not technically feasible with current technology, another method of distinguishing space objects was explored that exploits the polarization signature from unresolved images. The objective of this study was to collect and analyze visible-spectrum polarization data from unresolved images of geosynchronous satellites taken over various solar phase angles. Different collection geometries were used to evaluate the polarization contribution of solar arrays, thermal control materials, antennas, and the satellite bus as the solar phase angle changed. Since materials on space objects age due to the space environment, it was postulated that their polarization signature may change enough to allow discrimination of identical satellites launched at different times. The instrumentation used in this experiment was a United States Air Force Academy (USAFA) Department of Physics system that consists of a 20-inch Ritchey-Chretien telescope and a dual focal plane optical train fed with a polarizing beam splitter. A rigorous calibration of the system was performed that included corrections for pixel bias, dark current, and response. Additionally, the two channel polarimeter was calibrated by experimentally determining the Mueller matrix for the system and relating image intensity at the two cameras to Stokes parameters S0 and S1. After the system calibration, polarization data was collected during three nights on eight geosynchronous satellites built by various manufacturers and launched several years apart. Three pairs of the eight

  20. Convolutional neural network features based change detection in satellite images

    Science.gov (United States)

    Mohammed El Amin, Arabi; Liu, Qingjie; Wang, Yunhong

    2016-07-01

    With the popular use of high resolution remote sensing (HRRS) satellite images, a huge research efforts have been placed on change detection (CD) problem. An effective feature selection method can significantly boost the final result. While hand-designed features have proven difficulties to design features that effectively capture high and mid-level representations, the recent developments in machine learning (Deep Learning) omit this problem by learning hierarchical representation in an unsupervised manner directly from data without human intervention. In this letter, we propose approaching the change detection problem from a feature learning perspective. A novel deep Convolutional Neural Networks (CNN) features based HR satellite images change detection method is proposed. The main guideline is to produce a change detection map directly from two images using a pretrained CNN. This method can omit the limited performance of hand-crafted features. Firstly, CNN features are extracted through different convolutional layers. Then, a concatenation step is evaluated after an normalization step, resulting in a unique higher dimensional feature map. Finally, a change map was computed using pixel-wise Euclidean distance. Our method has been validated on real bitemporal HRRS satellite images according to qualitative and quantitative analyses. The results obtained confirm the interest of the proposed method.

  1. Building Change Detection Using Old Aerial Images and New LiDAR Data

    Directory of Open Access Journals (Sweden)

    Shouji Du

    2016-12-01

    Full Text Available Building change detection is important for urban area monitoring, disaster assessment and updating geo-database. 3D information derived from image dense matching or airborne light detection and ranging (LiDAR is very effective for building change detection. However, combining 3D data from different sources is challenging, and so far few studies have focused on building change detection using both images and LiDAR data. This study proposes an automatic method to detect building changes in urban areas using aerial images and LiDAR data. First, dense image matching is carried out to obtain dense point clouds and then co-registered LiDAR point clouds using the iterative closest point (ICP algorithm. The registered point clouds are further resampled to a raster DSM (Digital Surface Models. In a second step, height difference and grey-scale similarity are calculated as change indicators and the graph cuts method is employed to determine changes considering the contexture information. Finally, the detected results are refined by removing the non-building changes, in which a novel method based on variance of normal direction of LiDAR points is proposed to remove vegetated areas for positive building changes (newly building or taller and nEGI (normalized Excessive Green Index is used for negative building changes (demolish building or lower. To evaluate the proposed method, a test area covering approximately 2.1 km2 and consisting of many different types of buildings is used for the experiment. Results indicate 93% completeness with correctness of 90.2% for positive changes, while 94% completeness with correctness of 94.1% for negative changes, which demonstrate the promising performance of the proposed method.

  2. High-Resolution Imaging of Asteroids/Satellites with AO

    Science.gov (United States)

    Merline, William

    2012-02-01

    We propose to make high-resolution observations of asteroids using AO, to measure size, shape, and pole position (spin vectors), and/or to search for satellites. We have demonstrated that AO imaging allows determination of the pole/dimensions in 1 or 2 nights on a single target, rather than the years of observations with lightcurve inversion techniques that only yield poles and axial ratios, not true dimensions. Our new technique (KOALA) combines AO imaging with lightcurve and occultation data for optimum size/shape determinations. We request that LGS be available for faint targets, but using NGS AO, we will measure several large and intermediate asteroids that are favorably placed in spring/summer of 2012 for size/shape/pole. Accurately determining the volume from the often-irregular shape allows us to derive densities to much greater precision in cases where the mass is known, e.g., from the presence of a satellite. We will search several d! ozen asteroids for the presence of satellites, particularly in under-studied populations, particularly NEOs (we have recently achieved the first-ever optical image of an NEO binary [Merline et al. 2008b, IAUC 8977]). Satellites provide a real-life lab for testing collisional models. We will search for satellites around special objects at the request of lightcurve observers, and we will make a search for debris in the vicinity of Pluto, in support of the New Horizons mission. Our shape/size work requires observations over most of a full rotation period (typically several hours).

  3. Using interpreted large scale aerial photo data to enhance satellite-based mapping and explore forest land definitions

    Science.gov (United States)

    Tracey S. Frescino; Gretchen G. Moisen

    2009-01-01

    The Interior-West, Forest Inventory and Analysis (FIA), Nevada Photo-Based Inventory Pilot (NPIP), launched in 2004, involved acquisition, processing, and interpretation of large scale aerial photographs on a subset of FIA plots (both forest and nonforest) throughout the state of Nevada. Two objectives of the pilot were to use the interpreted photo data to enhance...

  4. ANALYSIS OF THE EFFECTS OF IMAGE QUALITY ON DIGITAL MAP GENERATION FROM SATELLITE IMAGES

    Directory of Open Access Journals (Sweden)

    H. Kim

    2012-07-01

    Full Text Available High resolution satellite images are widely used to produce and update a digital map since they became widely available. It is well known that the accuracy of digital map produced from satellite images is decided largely by the accuracy of geometric modelling. However digital maps are made by a series of photogrammetric workflow. Therefore the accuracy of digital maps are also affected by the quality of satellite images, such as image interpretability. For satellite images, parameters such as Modulation Transfer Function(MTF, Signal to Noise Ratio(SNR and Ground Sampling Distance(GSD are used to present images quality. Our previous research stressed that such quality parameters may not represent the quality of image products such as digital maps and that parameters for image interpretability such as Ground Resolved Distance(GRD and National Imagery Interpretability Rating Scale(NIIRS need to be considered. In this study, we analyzed the effects of the image quality on accuracy of digital maps produced by satellite images. QuickBird, IKONOS and KOMPSAT-2 imagery were used to analyze as they have similar GSDs. We measured various image quality parameters mentioned above from these images. Then we produced digital maps from the images using a digital photogrammetric workstation. We analyzed the accuracy of the digital maps in terms of their location accuracy and their level of details. Then we compared the correlation between various image quality parameters and the accuracy of digital maps. The results of this study showed that GRD and NIIRS were more critical for map production then GSD, MTF or SNR.

  5. Panchromatic Satellite Image Classification for Flood Hazard Assessment

    Directory of Open Access Journals (Sweden)

    Ahmed Shaker

    2012-11-01

    Full Text Available The study aims to investigate the use of panchromatic (PAN satellite image data for flood hazard assessment with anaid of various digital image processing techniques. Two SPOT PAN satellite images covering part of the Nile River inEgypt were used to delineate the flood extent during the years 1997 and 1998 (before and after a high flood. Threeclassification techniques, including the contextual classifier, maximum likelihood classifier and minimum distanceclassifier, were applied to the following: 1 the original PAN image data, 2 the original PAN image data and grey-levelco-occurrence matrix texture created from the PAN data, and 3 the enhanced PAN image data using an edgesharpeningfilter. The classification results were assessed with reference to the results derived from manualdigitization and random checkpoints. Generally, the results showed improvement of the calculation of flood area whenan edge-sharpening filter was used. In addition, the maximum likelihood classifier yielded the best classificationaccuracy (up to 97% compared to the other two classifiers. The research demonstrates the benefits of using PANsatellite imagery as a potential data source for flood hazard assessment.

  6. Spacecraft design project: High temperature superconducting infrared imaging satellite

    Science.gov (United States)

    1991-01-01

    The High Temperature Superconductor Infrared Imaging Satellite (HTSCIRIS) is designed to perform the space based infrared imaging and surveillance mission. The design of the satellite follows the black box approach. The payload is a stand alone unit, with the spacecraft bus designed to meet the requirements of the payload as listed in the statement of work. Specifications influencing the design of the spacecraft bus were originated by the Naval Research Lab. A description of the following systems is included: spacecraft configuration, orbital dynamics, radio frequency communication subsystem, electrical power system, propulsion, attitude control system, thermal control, and structural design. The issues of testing and cost analysis are also addressed. This design project was part of the course Advanced Spacecraft Design taught at the Naval Postgraduate School.

  7. The SUMO Ship Detector Algorithm for Satellite Radar Images

    Directory of Open Access Journals (Sweden)

    Harm Greidanus

    2017-03-01

    Full Text Available Search for Unidentified Maritime Objects (SUMO is an algorithm for ship detection in satellite Synthetic Aperture Radar (SAR images. It has been developed over the course of more than 15 years, using a large amount of SAR images from almost all available SAR satellites operating in L-, C- and X-band. As validated by benchmark tests, it performs very well on a wide range of SAR image modes (from Spotlight to ScanSAR and resolutions (from 1–100 m and for all types and sizes of ships, within the physical limits imposed by the radar imaging. This paper describes, in detail, the algorithmic approach in all of the steps of the ship detection: land masking, clutter estimation, detection thresholding, target clustering, ship attribute estimation and false alarm suppression. SUMO is a pixel-based CFAR (Constant False Alarm Rate detector for multi-look radar images. It assumes a K distribution for the sea clutter, corrected however for deviations of the actual sea clutter from this distribution, implementing a fast and robust method for the clutter background estimation. The clustering of detected pixels into targets (ships uses several thresholds to deal with the typically irregular distribution of the radar backscatter over a ship. In a multi-polarization image, the different channels are fused. Azimuth ambiguities, a common source of false alarms in ship detection, are removed. A reliability indicator is computed for each target. In post-processing, using the results of a series of images, additional false alarms from recurrent (fixed targets including range ambiguities are also removed. SUMO can run in semi-automatic mode, where an operator can verify each detected target. It can also run in fully automatic mode, where batches of over 10,000 images have successfully been processed in less than two hours. The number of satellite SAR systems keeps increasing, as does their application to maritime surveillance. The open data policy of the EU

  8. METEOROLOGICAL SATELLITE IMAGES IN GEOGRAPHY CLASSES: a didactic possibility

    Directory of Open Access Journals (Sweden)

    Diego Correia Maia

    2016-01-01

    Full Text Available ABSTRACT: The satellite images are still largely unexplored as didactic resource in geography classes, particularly about meteorology. This article aims to contribute to the development of new methodologies of interpretation and understanding, beyond the construction of pedagogical practices involving meteorological satellite images, concepts and issues related to climate issues. The aim of this paper is to present possibilities for the use of meteorological satellite images in the Teaching of Geography, aiming the promoting and the understanding of contents of air masses and fronts and climatic factors. RESUMO: As imagens de satélite ainda são pouco exploradas como recurso didático nas aulas de Geografia, principalmente aquelas relativas à meteorologia. Este artigo visa contribuir com o desenvolvimento de novas metodologias de interpretação e compreensão, além da construção de práticas pedagógicas envolvendo imagens de satélite meteorológico, conceitos e temas ligados às questões climáticas. Seu objetivo é apresentar possibilidades de utilização das imagens de satélite meteorológico no Ensino de Geografia, visando à promoção e ao entendimento dos conteúdos de massas de ar e frentes e de elementos climáticos. Palavras chave

  9. 3D mapping from high resolution satellite images

    Science.gov (United States)

    Goulas, D.; Georgopoulos, A.; Sarakenos, A.; Paraschou, Ch.

    2013-08-01

    In recent years 3D information has become more easily available. Users' needs are constantly increasing, adapting to this reality and 3D maps are in more demand. 3D models of the terrain in CAD or other environments have already been common practice; however one is bound by the computer screen. This is why contemporary digital methods have been developed in order to produce portable and, hence, handier 3D maps of various forms. This paper deals with the implementation of the necessary procedures to produce holographic 3D maps and three dimensionally printed maps. The main objective is the production of three dimensional maps from high resolution aerial and/or satellite imagery with the use of holography and but also 3D printing methods. As study area the island of Antiparos was chosen, as there were readily available suitable data. These data were two stereo pairs of Geoeye-1 and a high resolution DTM of the island. Firstly the theoretical bases of holography and 3D printing are described, and the two methods are analyzed and there implementation is explained. In practice a x-axis parallax holographic map of the Antiparos Island is created and a full parallax (x-axis and y-axis) holographic map is created and printed, using the holographic method. Moreover a three dimensional printed map of the study area has been created using 3dp (3d printing) method. The results are evaluated for their usefulness and efficiency.

  10. Assessment of Satellite Images for Soil Salinity Studies

    Directory of Open Access Journals (Sweden)

    S.H. Sanaeinejad

    2012-04-01

    Full Text Available Soil salinity is one of the main environmental problems affecting extensive area in the world. There are some problems with traditional data collection methods for soil studies. Using the new methods and techniques such as remote sensing could overcome most of these problems. However using these data in areas with uncommon usages needed some researches to find the best calibration between the data and real situations in soil. This research was carried out using Landsat satellite images in Neyshabour area, North East of Iran. In order to prepare suitable learning samples for the image processing in this study, 300 locations were randomly selected in the area, among which 273 locations were finally selected as suitable surface soil samples. All samples were moved to laboratory and their electrical conductivity was measured. Six reflective bands of ETM+ satellite images taken from the study area in 2002 were used for the image processing analysis. Classification of different soil salinities was carried out using common algorithms of image classification based on the best composition bands and using statistical methods between soil salinity variables and digital numbers of the images to represent a suitable method. the research results showed that the reflective bands 7, 3, 4 and 1 are the best band composition for preparing the color composite images and for the classification of the salinity in this area. The highest coefficient of determination was R2=0.311 and R2=0.44 for saline and non-saline soil respectively using band 2 and 3 of the images at 5% significant level. Based on the results, it can be concluded that the potential of ETM+ images for delineation and identification of different soil salinity are limited.

  11. Three-dimensional imaging applications in Earth Sciences using video data acquired from an unmanned aerial vehicle

    Science.gov (United States)

    McLeod, Tara

    For three dimensional (3D) aerial images, unmanned aerial vehicles (UAVs) are cheaper to operate and easier to fly than the typical manned craft mounted with a laser scanner. This project explores the feasibility of using 2D video images acquired with a UAV and transforming them into 3D point clouds. The Aeryon Scout -- a quad-copter micro UAV -- flew two missions: the first at York University Keele campus and the second at the Canadian Wollastonite Mine Property. Neptec's ViDAR software was used to extract 3D information from the 2D video using structure from motion. The resulting point clouds were sparsely populated, yet captured vegetation well. They were used successfully to measure fracture orientation in rock walls. Any improvement in the video resolution would cascade through the processing and improve the overall results.

  12. Comparison of satellite imagery and infrared aerial photography as vegetation mapping methods in an arctic study area: Jameson Land, East Greenland

    DEFF Research Database (Denmark)

    Hansen, Birger Ulf; Mosbech, Anders

    1994-01-01

    Remote Sensing, vegetation mapping, SPOT, Landsat TM, aerial photography, Jameson Land, East Greenland......Remote Sensing, vegetation mapping, SPOT, Landsat TM, aerial photography, Jameson Land, East Greenland...

  13. SAR and Oblique Aerial Optical Image Fusion for Urban Area Image Segmentation

    Science.gov (United States)

    Fagir, J.; Schubert, A.; Frioud, M.; Henke, D.

    2017-05-01

    The fusion of synthetic aperture radar (SAR) and optical data is a dynamic research area, but image segmentation is rarely treated. While a few studies use low-resolution nadir-view optical images, we approached the segmentation of SAR and optical images acquired from the same airborne platform - leading to an oblique view with high resolution and thus increased complexity. To overcome the geometric differences, we generated a digital surface model (DSM) from adjacent optical images and used it to project both the DSM and SAR data into the optical camera frame, followed by segmentation with each channel. The fused segmentation algorithm was found to out-perform the single-channel version.

  14. Authentication Scheme Based on Principal Component Analysis for Satellite Images

    Directory of Open Access Journals (Sweden)

    Ashraf. K. Helmy

    2009-09-01

    Full Text Available This paper presents a multi-band wavelet image content authentication scheme for satellite images by incorporating the principal component analysis (PCA. The proposed schemeachieves higher perceptual transparency and stronger robustness. Specifically, the developed watermarking scheme can successfully resist common signal processing such as JPEG compression and geometric distortions such as cropping. In addition, the proposed scheme can be parameterized, thus resulting in more security. That is, an attacker may not be able to extract the embedded watermark if the attacker does not know the parameter.In an order to meet these requirements, the host image is transformed to YIQ to decrease the correlation between different bands, Then Multi-band Wavelet transform (M-WT is applied to each channel separately obtaining one approximate sub band and fifteen detail sub bands. PCA is then applied to the coefficients corresponding to the same spatial location in all detail sub bands. The last principle component band represents an excellent domain forinserting the water mark since it represents lowest correlated features in high frequency area of host image.One of the most important aspects of satellite images is spectral signature, the behavior of different features in different spectral bands, the results of proposed algorithm shows that the spectral stamp for different features doesn't tainted after inserting the watermark.

  15. Aerial ungulate surveys with a combination of infrared and high–resolution natural colour images

    Directory of Open Access Journals (Sweden)

    Franke, U.

    2012-01-01

    Full Text Available Information on animal population sizes is crucial for wildlife management. In aerial surveys, we used a silent light aircraft (microlight and a combination of a computer–linked thermal infrared camera (640 x 480 pixels to detect ungulates and high–resolution visual images (5,616 x 3,744 pixels to identify specific species. From winter 2008/2009 to winter 2010/2011, we flew 48 missions over three German national parks and a German/ French biosphere reserve. Within each study area, we followed non–overlapping linear transects with a flying altitude ~450 m above ground level and scanned 1,500–2,000 ha every two hours of flight time. Animals best detected and identified were red deer and fallow deer. Detection rates with respect to the type and density of vegetation cover ranged from 0% (young spruce to 75% (young defoliated beech to 100% (open land. This non–invasive method is cost–effective and suitable for many landscapes.

  16. The Use of a Satellite Communications System for Command and Control of the National Aeronautics and Space Administration Surrogate Unmanned Aerial System Research Aircraft

    Science.gov (United States)

    Howell, Charles T.; Jones, Frank; Hutchinson, Brian; Joyce, Claude; Nelson, Skip; Melum, Mike

    2017-01-01

    The NASA Langley Research Center has transformed a Cirrus Design SR22 general aviation (GA) aircraft into an Unmanned Aerial Systems (UAS) Surrogate research aircraft which has served for several years as a platform for unmanned systems research and development. The aircraft is manned with a Safety Pilot and a Research Systems Operator (RSO) that allows for flight operations almost any-where in the national airspace system (NAS) without the need for a Federal Aviation Administration (FAA) Certificate of Authorization (COA). The UAS Surrogate can be remotely controlled from a modular, transportable ground control station (GCS) like a true UAS. Ground control of the aircraft is accomplished by the use of data links that allow the two-way passage of the required data to control the aircraft and provide the GCS with situational awareness. The original UAS Surrogate data-link system was composed of redundant very high frequency (VHF) data radio modems with a maximum range of approximately 40 nautical miles. A new requirement was developed to extend this range beyond visual range (BVR). This new requirement led to the development of a satellite communications system that provided the means to command and control the UAS Surrogate at ranges beyond the limits of the VHF data links. The system makes use of the Globalstar low earth orbit (LEO) satellite communications system. This paper will provide details of the development, implementation, and flight testing of the satellite data communications system on the UAS Surrogate research aircraft.

  17. 基于北斗系统的对空情报传送方式%Aerial Intelligence Transmission Based on Beidou Satellite Navigation System

    Institute of Scientific and Technical Information of China (English)

    康望东; 覃远超; 唐静

    2012-01-01

    At present, the radar station aerial intelligence transmission is performed in the cable network through the fixed stations. Aimed at the situations of the damaged cable networks or without optical fiber for the mobile radar station, the method for the short message service of Beidou satellite navigation system is proposed for the transmission of aerial radar intelligence. The transmission schemes are designed and discussed.%目前雷达站对空情报传送经由有线网络通过固定台站传输。针对有线网络破坏或机动雷达站未敷设有线光缆的情况,可利用北斗卫星系统的短报文通信进行雷达对空情报传送,并对利用北斗卫星系统进行对空情报传送的方案进行了设计和讨论。

  18. Aerial Image over Ouray National Wildlife Refuge, Acquired on September 4, 1963 (Frame 121)

    Data.gov (United States)

    US Fish and Wildlife Service, Department of the Interior — This data set is a georeferenced version of an original black and white aerial photograph downloaded from Earth Explorer (USGS; www.earthexplorer.usgs.gov). The...

  19. Aerial Image over Ouray National Wildlife Refuge, Acquired on September 4, 1963 (Frame 135)

    Data.gov (United States)

    US Fish and Wildlife Service, Department of the Interior — This data set is a georeferenced version of an original black and white aerial photograph downloaded from Earth Explorer (USGS; www.earthexplorer.usgs.gov). The...

  20. Aerial Image over Ouray National Wildlife Refuge, Acquired on September 4, 1963 (Frame 134)

    Data.gov (United States)

    US Fish and Wildlife Service, Department of the Interior — This data set is a georeferenced version of an original black and white aerial photograph downloaded from Earth Explorer (USGS; www.earthexplorer.usgs.gov). The...

  1. Aerial Image over Ouray National Wildlife Refuge, Acquired on September 4, 1963 (Frame 88)

    Data.gov (United States)

    US Fish and Wildlife Service, Department of the Interior — This data set is a georeferenced version of an original black and white aerial photograph downloaded from Earth Explorer (USGS; www.earthexplorer.usgs.gov). The...

  2. Aerial Image over Ouray National Wildlife Refuge, Acquired on August 11, 1953

    Data.gov (United States)

    US Fish and Wildlife Service, Department of the Interior — This data set is a georeferenced version of an original black and white aerial photograph downloaded from Earth Explorer (USGS; www.earthexplorer.usgs.gov). The...

  3. Series of aerial images over Marais des Cygnes National Wildlife Refuge, acquired February, 1950

    Data.gov (United States)

    US Fish and Wildlife Service, Department of the Interior — This data set is a georeferenced version of an original black and white aerial photograph downloaded from Earth Explorer (USGS). The original photo was georeferenced...

  4. Series of aerial images over Monte Vista National Wildlife Refuge, acquired November 1941

    Data.gov (United States)

    US Fish and Wildlife Service, Department of the Interior — This data set includes 31 georeferenced versions of original black and white aerial photographs acquired in digital form from the National Archives and Records...

  5. Series of aerial images over Baca National Wildlife Refuge, acquired in 1953

    Data.gov (United States)

    US Fish and Wildlife Service, Department of the Interior — This data set includes 23 georeferenced and clipped versions of aerial photographs acquired September 29th and October 1st, 1953, over Baca National Wildlife...

  6. Series of aerial images over Baca National Wildlife Refuge, acquired July, 1988

    Data.gov (United States)

    US Fish and Wildlife Service, Department of the Interior — This data set includes 21 georeferenced and clipped versions of aerial photographs from July 22nd, 1988, taken over Baca National Wildlife Refuge. A georeferenced...

  7. Series of aerial images over Alamosa National Wildlife Refuge, acquired in 1953

    Data.gov (United States)

    US Fish and Wildlife Service, Department of the Interior — This data set includes 16 georeferenced and clipped versions of aerial photographs from September 29th and October 1st, 1953, taken over Alamosa National Wildlife...

  8. Series of aerial images over Monte Vista National Wildlife Refuge, acquired in 1960

    Data.gov (United States)

    US Fish and Wildlife Service, Department of the Interior — This data set includes 7 georeferenced and clipped versions of aerial photographs from August 1960, taken over Monte Vista National Wildlife Refuge. A georeferenced...

  9. Series of aerial images over Alamosa National Wildlife Refuge, acquired July, 1988

    Data.gov (United States)

    US Fish and Wildlife Service, Department of the Interior — This data set includes seven georeferenced and clipped versions of aerial photographs from July 22nd, 1988, taken over Alamosa National Wildlife Refuge. A...

  10. Series of aerial images over Monte Vista National Wildlife Refuge, acquired August 26th, 1989

    Data.gov (United States)

    US Fish and Wildlife Service, Department of the Interior — This data set includes five georeferenced versions of color aerial photographs from August 26th, 1989, taken over Monte Vista National Wildlife Refuge. Like other...

  11. Series of aerial images over Monte Vista National Wildlife Refuge, acquired July 13th, 1965

    Data.gov (United States)

    US Fish and Wildlife Service, Department of the Interior — This data set includes 31 georeferenced versions of black and white aerial photographs from July 13th, 1965, taken over Monte Vista National Wildlife Refuge. A...

  12. Series of aerial images over Monte Vista National Wildlife Refuge, acquired September 8, 1985

    Data.gov (United States)

    US Fish and Wildlife Service, Department of the Interior — This data set includes two georeferenced versions of color aerial photographs from September 8, 1985, taken over Monte Vista National Wildlife Refuge. A...

  13. Aerial Images from AN Uav System: 3d Modeling and Tree Species Classification in a Park Area

    Science.gov (United States)

    Gini, R.; Passoni, D.; Pinto, L.; Sona, G.

    2012-07-01

    The use of aerial imagery acquired by Unmanned Aerial Vehicles (UAVs) is scheduled within the FoGLIE project (Fruition of Goods Landscape in Interactive Environment): it starts from the need to enhance the natural, artistic and cultural heritage, to produce a better usability of it by employing audiovisual movable systems of 3D reconstruction and to improve monitoring procedures, by using new media for integrating the fruition phase with the preservation ones. The pilot project focus on a test area, Parco Adda Nord, which encloses various goods' types (small buildings, agricultural fields and different tree species and bushes). Multispectral high resolution images were taken by two digital compact cameras: a Pentax Optio A40 for RGB photos and a Sigma DP1 modified to acquire the NIR band. Then, some tests were performed in order to analyze the UAV images' quality with both photogrammetric and photo-interpretation purposes, to validate the vector-sensor system, the image block geometry and to study the feasibility of tree species classification. Many pre-signalized Control Points were surveyed through GPS to allow accuracy analysis. Aerial Triangulations (ATs) were carried out with photogrammetric commercial software, Leica Photogrammetry Suite (LPS) and PhotoModeler, with manual or automatic selection of Tie Points, to pick out pros and cons of each package in managing non conventional aerial imagery as well as the differences in the modeling approach. Further analysis were done on the differences between the EO parameters and the corresponding data coming from the on board UAV navigation system.

  14. Chemiluminescent methods and instruments for monitoring of the atmosphere and satellite validation on board of research aircrafts and unmanned aerial vehicles

    Science.gov (United States)

    Sitnikov, Nikolay; Borisov, Yuriy; Akmulin, Dimitry; Chekulaev, Igor; Sitnikova, Vera; Ulanovsky, Alexey; Sokolov, Alexey

    The results of development of instruments based on heterophase chemiluminescence for measurements of space distribution of ozone and nitrogen oxides concentrations on board of research aircrafts and unmanned aerial vehicles carried out in Central Aerological Observatory are presented. Some results of atmospheric investigations on board of research aircrafts M55 “Geophysica” (Russia) and “Falcon” (Germany) carried out using developed instruments in frame of international projects are demonstrated. Small and low power instruments based on chemiluminescent principle for UAV are developed. The results of measurements on board of UAV are shown. The development can be used for satellite data validation, as well as operative environmental monitoring of contaminated areas in particular, chemical plants, natural and industrial disasters territories, areas and facilities for space purposes etc.

  15. Analysis of Galileo Style Geostationary Satellite Imaging: Image Reconstruction

    Science.gov (United States)

    2012-09-01

    obtained using only baselines longer than 8 m does not sample the short spacial frequencies, and the image reconstruction is not able to recover the...the long spacial frequencies sampled in a shorter baseline overlap the short spacial frequencies sampled in a longer baseline. This technique will

  16. Digital image mosaics of the nearshore coastal waters of selected areas on the Hawaiian Islands of Hawai‘i, Maui, Moloka‘i, and O‘ahu generated using aerial photographs and SHOALS airborne lidar bathymetry data

    Science.gov (United States)

    Chavez, P. S.; Isbrecht, JoAnn; Velasco, Miguel G.; Cochran, Susan

    2016-01-01

    The lack of geographic and thematic maps of coral reefs limits our understanding of reefs and our ability to assess change. The U.S. Geological Survey (USGS) has the capability to compile digital image mosaics that are useful for creating detailed map products. Image maps covering the shallow near-shore coastal waters have been produced for several of the main Hawaiian Islands, including Hawai‘i, Maui, Moloka‘i, and O‘ahu and are presented in JPEG2000 (.jp2) format. The digital-image mosaics were generated by first scanning historical aerial photographs. At the time, available satellite image resolutions were not acceptable and the aerial photographs used were the best option. The individually scanned digital images were tone-and color-matched and then digitally mosaicked together using spatial matching. Separately, black and white digital orthophoto quads (DOQs) or digital raster graphics (DRGs) of the same areas were merged with shaded-relief images generated from lidar bathymetry data. The resulting black and white images covering both near-shore coastal waters and on-land areas became the geometric ‘masters’ for the mosaics generated from the aerial photographs. The aerial-photograph mosaics were geometrically corrected to overlay the master data set by using hundreds of image-to-image geometric control points and ‘slaving’ the mosaic onto the master. The USGS has been investigating the use of remotely sensed image and spatial data to help map and study coral reef environments. Interpretation of these data is corroborated by extensive field mapping and correlation with field measured distribution and density of coral cover and other coral reef cover types. An immediate result of this effort was the generation of very-high spatial resolution georeferenced image maps of critical coral reef habitat areas being studied by the USGS and others.

  17. Path planning on satellite images for unmanned surface vehicles

    Directory of Open Access Journals (Sweden)

    Joe-Ming Yang

    2015-01-01

    Full Text Available In recent years, the development of autonomous surface vehicles has been a field of increasing research interest. There are two major areas in this field: control theory and path planning. This study focuses on path planning, and two objectives are discussed: path planning for Unmanned Surface Vehicles (USVs and implementation of path planning in a real map. In this paper, satellite thermal images are converted into binary images which are used as the maps for the Finite Angle A * algorithm (FAA *, an advanced A * algorithm that is used to determine safer and suboptimal paths for USVs. To plan a collision-free path, the algorithm proposed in this article considers the dimensions of surface vehicles. Furthermore, the turning ability of a surface vehicle is also considered, and a constraint condition is introduced to improve the quality of the path planning algorithm, which makes the traveled path smoother. This study also shows a path planning experiment performed on a real satellite thermal image, and the path planning results can be used by an USV

  18. NNIC—neural network image compressor for satellite positioning system

    Science.gov (United States)

    Danchenko, Pavel; Lifshits, Feodor; Orion, Itzhak; Koren, Sion; Solomon, Alan D.; Mark, Shlomo

    2007-04-01

    We have developed an algorithm, based on novel techniques of data compression and neural networks for the optimal positioning of a satellite. The algorithm is described in detail, and examples of its application are given. The heart of this algorithm is the program NNIC—neural network image compressor. This program was developed for compression color and grayscale images with artificial neural networks (ANNs). NNIC applies three different methods for compression. Two of them are based on neural networks architectures—multilayer perceptron and kohonen network. The third is based on a widely used method of discrete cosine transform, the basis for the JPEG standard. The program also serves as a tool for determining numerical and visual quality parameters of compression and comparison between different methods. A number of advantages and disadvantages of the compression using ANNs were discovered in the course of the present research, some of them presented in this report. The thrust of the report is the discussion of ANNs implementation problems for modern platforms, such as a satellite positioning system that include intensive image flowing and processing.

  19. DIGITAL CAMERA CALIBRATION USING IMAGES TAKEN FROM AN UNMANNED AERIAL VEHICLE

    Directory of Open Access Journals (Sweden)

    M. Pérez

    2012-09-01

    Full Text Available For calibrating the camera, an accurate determination of the interior orientation parameters is needed. For more accurate results, the calibration images should be taken under conditions that are similar to the field samples. The aim of this work is the establishment of an efficient and accurate digital camera calibration method to be used in particular working conditions, as it can be found with our UAV (Unmanned Aerial Vehicle photogrammetric projects. The UAV used in this work was md4-200 modelled by Microdrones. The microdrone is also equipped with a standard digital non- metric camera, the Pentax Optio A40 camera. To find out the interior orientation parameters of the digital camera, two calibration methods were done. A lab calibration based on a flat pattern and a field calibration were fulfilled. To carry out the calibration, Photomodeler Scanner software was used in both cases. The lab calibration process was completely automatic using a calibration grid. The focal length was fixed at widest angle and the network included a total of twelve images with± 90º roll angles. In order to develop the field calibration, a flight plan was programmed including a total of twelve images. In the same way as in the lab calibration, the focal length was fixed at widest angle. The field test used in the study was a flat surface located on the University of Almería campus and a set of 67 target points were placed. The calibration field area was 25 × 25 m approximately and the altitude flight over ground was 50 m. After the software processing, the camera calibration parameter values were obtained. The paper presents the process, the results and the accuracy of these calibration methods. The field calibration method reduced the final total error obtained in the previous lab calibration. Furthermore the overall RMSs obtained from both methods are similar. Therefore we will apply the field calibration results to all our photogrammetric projects in which

  20. Application of high resolution images from unmanned aerial vehicles for hydrology and rangeland science

    Science.gov (United States)

    Rango, A.; Vivoni, E. R.; Anderson, C. A.; Perini, N. A.; Saripalli, S.; Laliberte, A.

    2012-12-01

    A common problem in many natural resource disciplines is the lack of high-enough spatial resolution images that can be used for monitoring and modeling purposes. Advances have been made in the utilization of Unmanned Aerial Vehicles (UAVs) in hydrology and rangeland science. By utilizing low flight altitudes and velocities, UAVs are able to produce high resolution (5 cm) images as well as stereo coverage (with 75% forward overlap and 40% sidelap) to extract digital elevation models (DEM). Another advantage of flying at low altitude is that the potential problems of atmospheric haze obscuration are eliminated. Both small fixed-wing and rotary-wing aircraft have been used in our experiments over two rangeland areas in the Jornada Experimental Range in southern New Mexico and the Santa Rita Experimental Range in southern Arizona. The fixed-wing UAV has a digital camera in the wing and six-band multispectral camera in the nose, while the rotary-wing UAV carries a digital camera as payload. Because we have been acquiring imagery for several years, there are now > 31,000 photos at one of the study sites, and 177 mosaics over rangeland areas have been constructed. Using the DEM obtained from the imagery we have determined the actual catchment areas of three watersheds and compared these to previous estimates. At one site, the UAV-derived watershed area is 4.67 ha which is 22% smaller compared to a manual survey using a GPS unit obtained several years ago. This difference can be significant in constructing a watershed model of the site. From a vegetation species classification, we also determined that two of the shrub types in this small watershed(mesquite and creosote with 6.47 % and 5.82% cover, respectively) grow in similar locations(flat upland areas with deep soils), whereas the most predominant shrub(mariola with 11.9% cover) inhabits hillslopes near stream channels(with steep shallow soils). The positioning of these individual shrubs throughout the catchment using

  1. Automated hotspot analysis with aerial image CD metrology for advanced logic devices

    Science.gov (United States)

    Buttgereit, Ute; Trautzsch, Thomas; Kim, Min-ho; Seo, Jung-Uk; Yoon, Young-Keun; Han, Hak-Seung; Chung, Dong Hoon; Jeon, Chan-Uk; Meyers, Gary

    2014-09-01

    Continuously shrinking designs by further extension of 193nm technology lead to a much higher probability of hotspots especially for the manufacturing of advanced logic devices. The CD of these potential hotspots needs to be precisely controlled and measured on the mask. On top of that, the feature complexity increases due to high OPC load in the logic mask design which is an additional challenge for CD metrology. Therefore the hotspot measurements have been performed on WLCD from ZEISS, which provides the benefit of reduced complexity by measuring the CD in the aerial image and qualifying the printing relevant CD. This is especially of advantage for complex 2D feature measurements. Additionally, the data preparation for CD measurement becomes more critical due to the larger amount of CD measurements and the increasing feature diversity. For the data preparation this means to identify these hotspots and mark them automatically with the correct marker required to make the feature specific CD measurement successful. Currently available methods can address generic pattern but cannot deal with the pattern diversity of the hotspots. The paper will explore a method how to overcome those limitations and to enhance the time-to-result in the marking process dramatically. For the marking process the Synopsys WLCD Output Module was utilized, which is an interface between the CATS mask data prep software and the WLCD metrology tool. It translates the CATS marking directly into an executable WLCD measurement job including CD analysis. The paper will describe the utilized method and flow for the hotspot measurement. Additionally, the achieved results on hotspot measurements utilizing this method will be presented.

  2. An entropy-based approach to automatic image segmentation of satellite images

    CERN Document Server

    Barbieri, A L; Rodrigues, F A; Bruno, O M; Costa, L da F

    2009-01-01

    An entropy-based image segmentation approach is introduced and applied to color images obtained from Google Earth. Segmentation refers to the process of partitioning a digital image in order to locate different objects and regions of interest. The application to satellite images paves the way to automated monitoring of ecological catastrophes, urban growth, agricultural activity, maritime pollution, climate changing and general surveillance. Regions representing aquatic, rural and urban areas are identified and the accuracy of the proposed segmentation methodology is evaluated. The comparison with gray level images revealed that the color information is fundamental to obtain an accurate segmentation.

  3. Image Processing Technique for Automatic Detection of Satellite Streaks

    Science.gov (United States)

    2007-02-01

    satellites actifs et d’autres débris doivent être contrôlées. Dans ces cas, les paramètres orbitaux sont connus, mais après un certain temps cette...artéfacts de capteur (tel que des pixels morts, gradient de fond, bruit) et dégradation du signal (coulage, éblouissement, saturation, etc...Cette étude explique comment les artéfacts du capteur peuvent être corrigés, le fond de l’image enlevé et le bruit partiellement effacé. Puis, elle

  4. A Multi-View Dense Image Matching Method for High-Resolution Aerial Imagery Based on a Graph Network

    Directory of Open Access Journals (Sweden)

    Li Yan

    2016-09-01

    Full Text Available Multi-view dense matching is a crucial process in automatic 3D reconstruction and mapping applications. In this paper, we present a robust and effective multi-view dense matching algorithm for high-resolution aerial images based on a graph network. The overlap ratio and intersection angle between image pairs are used to find candidate stereo pairs and build the graph network. A Coarse-to-Fine strategy based on an improved Semi-Global Matching algorithm is applied for disparity computation across stereo pairs. Based on the constructed graph, point clouds of base views are generated by triangulating all connected image nodes, followed by a fusion process with the average reprojection error as a priority measure. The proposed method was successfully applied in experiments on aerial image test dataset provided by the ISPRS of Vaihingen, Germany and an oblique nadir image block of Zürich, Switzerland, using three kinds of matching configurations. The proposed method was compared to other state-of-art methods, SURE and PhotoScan. The results demonstrate that the proposed method delivers matches at higher completeness, efficiency, and accuracy than the other methods tested; the RMS for average reprojection error reached the sub pixel level and the actual positioning deviation was better than 1.5 GSD.

  5. Landsat TM and ETM+ 2002-2003 Kansas Satellite Image Database (KSID)

    Data.gov (United States)

    Kansas Data Access and Support Center — The Kansas Satellite Image Database (KSID):2002-2003 consists of image data gathered by three sensors. The first image data are terrain-corrected, precision...

  6. Sea Ice Mapping using Unmanned Aerial Systems

    Science.gov (United States)

    Solbø, S.; Storvold, R.

    2011-12-01

    Mapping of sea ice extent and sea ice features is an important task in climate research. Since the arctic coastal and oceanic areas have a high probability of cloud coverage, aerial platforms are superior to satellite measurements for high-resolution optical measurements. However, routine observations of sea ice conditions present a variety of problems using conventional piloted aircrafts. Specially, the availability of suitable aircrafts for lease does not cover the demand in major parts of the arctic. With the recent advances in unmanned aerial systems (UAS), there is a high possibility of establishing routine, cost effective aerial observations of sea ice conditions in the near future. Unmanned aerial systems can carry a wide variety of sensors useful for characterizing sea-ice features. For instance, the CryoWing UAS, a system initially designed for measurements of the cryosphere, can be equipped with digital cameras, surface thermometers and laser altimeters for measuring freeboard of ice flows. In this work we will present results from recent CryoWing sea ice flights on Svalbard, Norway. The emphasis will be on data processing for stitching together images acquired with the non-stabilized camera payload, to form high-resolution mosaics covering large spatial areas. These data are being employed to map ice conditions; including ice and lead features and melt ponds. These high-resolution mosaics are also well suited for sea-ice mechanics, classification studies and for validation of satellite sea-ice products.

  7. A low cost thermal infrared hyperspectral imager for small satellites

    Science.gov (United States)

    Crites, S. T.; Lucey, P. G.; Wright, R.; Garbeil, H.; Horton, K. A.

    2011-06-01

    The traditional model for space-based earth observations involves long mission times, high cost, and long development time. Because of the significant time and monetary investment required, riskier instrument development missions or those with very specific scientific goals are unlikely to successfully obtain funding. However, a niche for earth observations exploiting new technologies in focused, short lifetime missions is opening with the growth of the small satellite market and launch opportunities for these satellites. These low-cost, short-lived missions provide an experimental platform for testing new sensor technologies that may transition to larger, more long-lived platforms. The low costs and short lifetimes also increase acceptable risk to sensors, enabling large decreases in cost using commercial off the shelf (COTS) parts and allowing early-career scientists and engineers to gain experience with these projects. We are building a low-cost long-wave infrared spectral sensor, funded by the NASA Experimental Project to Stimulate Competitive Research program (EPSCOR), to demonstrate the ways in which a university's scientific and instrument development programs can fit into this niche. The sensor is a low-mass, power efficient thermal hyperspectral imager with electronics contained in a pressure vessel to enable the use of COTS electronics, and will be compatible with small satellite platforms. The sensor, called Thermal Hyperspectral Imager (THI), is based on a Sagnac interferometer and uses an uncooled 320x256 microbolometer array. The sensor will collect calibrated radiance data at long-wave infrared (LWIR, 8-14 microns) wavelengths in 230-meter pixels with 20 wavenumber spectral resolution from a 400-km orbit.

  8. A low cost thermal infrared hyperspectral imager for small satellites

    Science.gov (United States)

    Crites, S. T.; Lucey, P. G.; Wright, R.; Garbeil, H.; Horton, K. A.; Wood, M.

    2012-06-01

    The growth of the small satellite market and launch opportunities for these satellites is creating a new niche for earth observations that contrasts with the long mission durations, high costs, and long development times associated with traditional space-based earth observations. Low-cost, short-lived missions made possible by this new approach provide an experimental platform for testing new sensor technologies that may transition to larger, more long-lived platforms. The low costs and short lifetimes also increase acceptable risk to sensors, enabling large decreases in cost using commercial off-the-shelf (COTS) parts and allowing early-career scientists and engineers to gain experience with these projects. We are building a low-cost long-wave infrared spectral sensor, funded by the NASA Experimental Project to Stimulate Competitive Research program (EPSCoR), to demonstrate ways in which a university's scientific and instrument development programs can fit into this niche. The sensor is a low-mass, power-efficient thermal hyperspectral imager with electronics contained in a pressure vessel to enable use of COTS electronics and will be compatible with small satellite platforms. The sensor, called Thermal Hyperspectral Imager (THI), is based on a Sagnac interferometer and uses an uncooled 320x256 microbolometer array. The sensor will collect calibrated radiance data at long-wave infrared (LWIR, 8-14 microns) wavelengths in 230 meter pixels with 20 wavenumber spectral resolution from a 400 km orbit. We are currently in the laboratory and airborne testing stage in order to demonstrate the spectro-radiometric quality of data that the instrument provides.

  9. Satellite image based methods for fuels maps updating

    Science.gov (United States)

    Alonso-Benito, Alfonso; Hernandez-Leal, Pedro A.; Arbelo, Manuel; Gonzalez-Calvo, Alejandro; Moreno-Ruiz, Jose A.; Garcia-Lazaro, Jose R.

    2016-10-01

    Regular updating of fuels maps is important for forest fire management. Nevertheless complex and time consuming field work is usually necessary for this purpose, which prevents a more frequent update. That is why the assessment of the usefulness of satellite data and the development of remote sensing techniques that enable the automatic updating of these maps, is of vital interest. In this work, we have tested the use of the spectral bands of OLI (Operational Land Imager) sensor on board Landsat 8 satellite, for updating the fuels map of El Hierro Island (Spain). From previously digitized map, a set of 200 reference plots for different fuel types was created. A 50% of the plots were randomly used as a training set and the rest were considered for validation. Six supervised and 2 unsupervised classification methods were applied, considering two levels of detail. A first level with only 5 classes (Meadow, Brushwood, Undergrowth canopy cover >50%, Undergrowth canopy cover <15%, and Xeric formations), and the second one containing 19 fuel types. The level 1 classification methods yielded an overall accuracy ranging from 44% for Parellelepided to an 84% for Maximun Likelihood. Meanwhile, level 2 results showed at best, an unacceptable overall accuracy of 34%, which prevents the use of this data for such a detailed characterization. Anyway it has been demonstrated that in some conditions, images of medium spatial resolution, like Landsat 8-OLI, could be a valid tool for an automatic upgrade of fuels maps, minimizing costs and complementing traditional methodologies.

  10. Application of Geostatistical Simulation to Enhance Satellite Image Products

    Science.gov (United States)

    Hlavka, Christine A.; Dungan, Jennifer L.; Thirulanambi, Rajkumar; Roy, David

    2004-01-01

    With the deployment of Earth Observing System (EOS) satellites that provide daily, global imagery, there is increasing interest in defining the limitations of the data and derived products due to its coarse spatial resolution. Much of the detail, i.e. small fragments and notches in boundaries, is lost with coarse resolution imagery such as the EOS MODerate-Resolution Imaging Spectroradiometer (MODIS) data. Higher spatial resolution data such as the EOS Advanced Spaceborn Thermal Emission and Reflection Radiometer (ASTER), Landsat and airborne sensor imagery provide more detailed information but are less frequently available. There are, however, both theoretical and analytical evidence that burn scars and other fragmented types of land covers form self-similar or self-affine patterns, that is, patterns that look similar when viewed at widely differing spatial scales. Therefore small features of the patterns should be predictable, at least in a statistical sense, with knowledge about the large features. Recent developments in fractal modeling for characterizing the spatial distribution of undiscovered petroleum deposits are thus applicable to generating simulations of finer resolution satellite image products. We will present example EOS products, analysis to investigate self-similarity, and simulation results.

  11. Airport runway detection in satellite images by Adaboost learning

    Science.gov (United States)

    Zongur, Ugur; Halici, Ugur; Aytekin, Orsan; Ulusoy, Ilkay

    2009-09-01

    Advances in hardware and pattern recognition techniques, along with the widespread utilization of remote sensing satellites, have urged the development of automatic target detection systems in satellite images. Automatic detection of airports is particularly essential, due to the strategic importance of these targets. In this paper, a runway detection method using a segmentation process based on textural properties is proposed for the detection of airport runways, which is the most distinguishing element of an airport. Several local textural features are extracted including not only low level features such as mean, standard deviation of image intensity and gradient, but also Zernike Moments, Circular-Mellin Features, Haralick Features, as well as features involving Gabor Filters, Wavelets and Fourier Power Spectrum Analysis. Since the subset of the mentioned features, which have a role in the discrimination of airport runways from other structures and landforms, cannot be predicted trivially, Adaboost learning algorithm is employed for both classification and determining the feature subset, due to its feature selector nature. By means of the features chosen in this way, a coarse representation of possible runway locations is obtained. Promising experimental results are achieved and given.

  12. Registration of Aerial Image with Airborne LiDAR Data Based on Plücker Line

    Directory of Open Access Journals (Sweden)

    SHENG Qinghong

    2015-07-01

    Full Text Available Registration of aerial image with airborne LiDAR data is a key to feature extraction. A registration model based on Plücker line is proposed. The relative position and attitude relationship between the conjugate lines in LiDAR and image is determined based on Plücker linear equation, which describes line transformation in space, then coplanarity condition equation is established. Finally, coordinate transformation between image point and corresponding LiDAR point is achieved by the spiral movement of Plücker lines in the image. The registration model of Plücker linear coplanarity condition equation is simple, and jointly describes the rotation and translation to avoid coupling error between them, so the accuracy is approved. This research provides technical support for high-quality earth spatial information acquisition.

  13. Use of satellite images for the monitoring of water systems

    Science.gov (United States)

    Hillebrand, Gudrun; Winterscheid, Axel; Baschek, Björn; Wolf, Thomas

    2015-04-01

    Satellite images are a proven source of information for monitoring ecological indicators in coastal waters and inland river systems. This potential of remote sensing products was demonstrated by recent research projects (e.g. EU-funded project Freshmon - www.freshmon.eu) and other activities by national institutions. Among indicators for water quality, a particular focus was set on the temporal and spatial dynamics of suspended particulate matter (SPM) and Chlorophyll-a (Chl-a). The German Federal Institute of Hydrology (BfG) was using the Weser and Elbe estuaries as test cases to compare in-situ measurements with results obtained from a temporal series of automatically generated maps of SPM distributions based on remote sensing data. Maps of SPM and Chl-a distributions in European inland rivers and alpine lakes were generated by the Freshmon Project. Earth observation based products are a valuable source for additional data that can well supplement in-situ monitoring. For 2015, the BfG and the Institute for Lake Research of the State Institute for the Environment, Measurements and Nature Conservation of Baden-Wuerttemberg, Germany (LUBW) are in the process to start implementing an operational service for monitoring SPM and Chl-a based on satellite images (Landsat 7 & 8, Sentinel 2, and if required other systems with higher spatial resolution, e.g. Rapid Eye). In this 2-years project, which is part of the European Copernicus Programme, the operational service will be set up for - the inland rivers of Rhine and Elbe - the North Sea estuaries of Elbe, Weser and Ems. Furthermore - Lake Constance and other lakes located within the Federal State of Baden-Wuerttemberg. In future, the service can be implemented for other rivers and lakes as well. Key feature of the project is a data base that holds the stock of geo-referenced maps of SPM and Chl-a distributions. Via web-based portals (e.g. GGInA - geo-portal of the BfG; UIS - environmental information system of the

  14. Thermal Imaging of Subsurface Coal Fires by means of an Unmanned Aerial Vehicle (UAV) in the Autonomous Province Xinjiang, PRC

    Science.gov (United States)

    Vasterling, Margarete; Schloemer, Stefan; Fischer, Christian; Ehrler, Christoph

    2010-05-01

    Spontaneous combustion of coal and resulting coal fires lead to very high temperatures in the subsurface. To a large amount the heat is transferred to the surface by convective and conductive transport inducing a more or less pronounced thermal anomaly. During the past decade satellite-based infrared-imaging (ASTER, MODIS) was the method of choice for coal fire detection on a local and regional scale. However, the resolution is by far too low for a detailed analysis of single coal fires which is essential prerequisite for corrective measures (i.e. fire fighting) and calculation of carbon dioxide emission based on a complex correlation between energy release and CO2 generation. Consequently, within the framework of the Sino-German research project "Innovative Technologies for Exploration, Extinction and Monitoring of Coal Fires in Northern China", a new concept was developed and successfully tested. An unmanned aerial vehicle (UAV) was equipped with a lightweight camera for thermografic (resolution 160 by 120 pixel, dynamic range -20 to 250°C) and for visual imaging. The UAV designed as an octocopter is able to hover at GPS controlled waypoints during predefined flight missions. The application of a UAV has several advantages. Compared to point measurements on the ground the thermal imagery quickly provides the spatial distribution of the temperature anomaly with a much better resolution. Areas otherwise not accessible (due to topography, fire induced cracks, etc.) can easily be investigated. The results of areal surveys on two coal fires in Xinjiang are presented. Georeferenced thermal and visual images were mosaicked together and analyzed. UAV-born data do well compared to temperatures measured directly on the ground and cover large areas in detail. However, measuring surface temperature alone is not sufficient. Simultaneous measurements made at the surface and in roughly 15cm depth proved substantial temperature gradients in the upper soil. Thus the temperature

  15. Spatial, Temporal and Spectral Satellite Image Fusion via Sparse Representation

    Science.gov (United States)

    Song, Huihui

    Remote sensing provides good measurements for monitoring and further analyzing the climate change, dynamics of ecosystem, and human activities in global or regional scales. Over the past two decades, the number of launched satellite sensors has been increasing with the development of aerospace technologies and the growing requirements on remote sensing data in a vast amount of application fields. However, a key technological challenge confronting these sensors is that they tradeoff between spatial resolution and other properties, including temporal resolution, spectral resolution, swath width, etc., due to the limitations of hardware technology and budget constraints. To increase the spatial resolution of data with other good properties, one possible cost-effective solution is to explore data integration methods that can fuse multi-resolution data from multiple sensors, thereby enhancing the application capabilities of available remote sensing data. In this thesis, we propose to fuse the spatial resolution with temporal resolution and spectral resolution, respectively, based on sparse representation theory. Taking the study case of Landsat ETM+ (with spatial resolution of 30m and temporal resolution of 16 days) and MODIS (with spatial resolution of 250m ~ 1km and daily temporal resolution) reflectance, we propose two spatial-temporal fusion methods to combine the fine spatial information of Landsat image and the daily temporal resolution of MODIS image. Motivated by that the images from these two sensors are comparable on corresponding bands, we propose to link their spatial information on available Landsat- MODIS image pair (captured on prior date) and then predict the Landsat image from the MODIS counterpart on prediction date. To well-learn the spatial details from the prior images, we use a redundant dictionary to extract the basic representation atoms for both Landsat and MODIS images based on sparse representation. Under the scenario of two prior Landsat

  16. CLOUD DETECTION OF OPTICAL SATELLITE IMAGES USING SUPPORT VECTOR MACHINE

    Directory of Open Access Journals (Sweden)

    K.-Y. Lee

    2016-06-01

    Full Text Available Cloud covers are generally present in optical remote-sensing images, which limit the usage of acquired images and increase the difficulty of data analysis, such as image compositing, correction of atmosphere effects, calculations of vegetation induces, land cover classification, and land cover change detection. In previous studies, thresholding is a common and useful method in cloud detection. However, a selected threshold is usually suitable for certain cases or local study areas, and it may be failed in other cases. In other words, thresholding-based methods are data-sensitive. Besides, there are many exceptions to control, and the environment is changed dynamically. Using the same threshold value on various data is not effective. In this study, a threshold-free method based on Support Vector Machine (SVM is proposed, which can avoid the abovementioned problems. A statistical model is adopted to detect clouds instead of a subjective thresholding-based method, which is the main idea of this study. The features used in a classifier is the key to a successful classification. As a result, Automatic Cloud Cover Assessment (ACCA algorithm, which is based on physical characteristics of clouds, is used to distinguish the clouds and other objects. In the same way, the algorithm called Fmask (Zhu et al., 2012 uses a lot of thresholds and criteria to screen clouds, cloud shadows, and snow. Therefore, the algorithm of feature extraction is based on the ACCA algorithm and Fmask. Spatial and temporal information are also important for satellite images. Consequently, co-occurrence matrix and temporal variance with uniformity of the major principal axis are used in proposed method. We aim to classify images into three groups: cloud, non-cloud and the others. In experiments, images acquired by the Landsat 7 Enhanced Thematic Mapper Plus (ETM+ and images containing the landscapes of agriculture, snow area, and island are tested. Experiment results demonstrate

  17. Cloud Detection of Optical Satellite Images Using Support Vector Machine

    Science.gov (United States)

    Lee, Kuan-Yi; Lin, Chao-Hung

    2016-06-01

    Cloud covers are generally present in optical remote-sensing images, which limit the usage of acquired images and increase the difficulty of data analysis, such as image compositing, correction of atmosphere effects, calculations of vegetation induces, land cover classification, and land cover change detection. In previous studies, thresholding is a common and useful method in cloud detection. However, a selected threshold is usually suitable for certain cases or local study areas, and it may be failed in other cases. In other words, thresholding-based methods are data-sensitive. Besides, there are many exceptions to control, and the environment is changed dynamically. Using the same threshold value on various data is not effective. In this study, a threshold-free method based on Support Vector Machine (SVM) is proposed, which can avoid the abovementioned problems. A statistical model is adopted to detect clouds instead of a subjective thresholding-based method, which is the main idea of this study. The features used in a classifier is the key to a successful classification. As a result, Automatic Cloud Cover Assessment (ACCA) algorithm, which is based on physical characteristics of clouds, is used to distinguish the clouds and other objects. In the same way, the algorithm called Fmask (Zhu et al., 2012) uses a lot of thresholds and criteria to screen clouds, cloud shadows, and snow. Therefore, the algorithm of feature extraction is based on the ACCA algorithm and Fmask. Spatial and temporal information are also important for satellite images. Consequently, co-occurrence matrix and temporal variance with uniformity of the major principal axis are used in proposed method. We aim to classify images into three groups: cloud, non-cloud and the others. In experiments, images acquired by the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and images containing the landscapes of agriculture, snow area, and island are tested. Experiment results demonstrate the detection

  18. Satellite image time series simulation for environmental monitoring

    Science.gov (United States)

    Guo, Tao

    2014-11-01

    The performance of environmental monitoring heavily depends on the availability of consecutive observation data and it turns out an increasing demand in remote sensing community for satellite image data in the sufficient resolution with respect to both spatial and temporal requirements, which appear to be conflictive and hard to tune tradeoffs. Multiple constellations could be a solution if without concerning cost, and thus it is so far interesting but very challenging to develop a method which can simultaneously improve both spatial and temporal details. There are some research efforts to deal with the problem from various aspects, a type of approaches is to enhance the spatial resolution using techniques of super resolution, pan-sharpen etc. which can produce good visual effects, but mostly cannot preserve spectral signatures and result in losing analytical value. Another type is to fill temporal frequency gaps by adopting time interpolation, which actually doesn't increase informative context at all. In this paper we presented a novel method to generate satellite images in higher spatial and temporal details, which further enables satellite image time series simulation. Our method starts with a pair of high-low resolution data set, and then a spatial registration is done by introducing LDA model to map high and low resolution pixels correspondingly. Afterwards, temporal change information is captured through a comparison of low resolution time series data, and the temporal change is then projected onto high resolution data plane and assigned to each high resolution pixel referring the predefined temporal change patterns of each type of ground objects to generate a simulated high resolution data. A preliminary experiment shows that our method can simulate a high resolution data with a good accuracy. We consider the contribution of our method is to enable timely monitoring of temporal changes through analysis of low resolution images time series only, and usage of

  19. ISSUES IN DIGITAL IMAGE PROCESSING OF AERIAL PHOTOGRAPHY FOR MAPPING SUBMERSED AQUATIC VEGETATION

    Science.gov (United States)

    The paper discusses the numerous issues that needed to be addressed when developing a methodology for mapping Submersed Aquatic Vegetation (SAV) from digital aerial photography. Specifically, we discuss 1) choice of film; 2) consideration of tide and weather constraints; 3) in-s...

  20. Collecting Inexpensive High Resolution Aerial and Stereo Images of Small- to Mid-Scale Geomorphic and Tectonic Features

    Science.gov (United States)

    Wheelwright, R. J.; White, W. S.; Willis, J. B.

    2010-12-01

    Methods for collecting accurate, mm- to cm-scale stereoscopic aerial imagery of both small- and mid-scale geomorphic features are developed for a one-time cost of under $1500. High resolution aerial images are valuable for documenting and analyzing small- to mid-scale geomorphic and tectonic features. However, collecting images of mid-scale features such as landslides, rock glaciers, fault scarps, and cinder cones is expensive and makes studies that rely on high resolution repeat imagery prohibitive for undergraduate geology departments with limited budgets. In addition to cost, collecting images of smaller scale geomorphic features such as gravel bars is often impeded by overhanging vegetation or other features in the immediate environment that make impractical the collection of aerial images using standard airborne techniques. The methods provide high resolution stereo photos suitable for image processing and stereographic analysis; the images are potentially suitable for change analyses, velocity tracking, and construction of lidar-resolution digital elevation models. We developed two techniques. The technique suitable for small-scale features (such as gravel bars) utilizes two Nikon D3000 digital single-lens reflex (DSLR) cameras attached to a system of poles that suspends the cameras at a height of 4 meters with a variable camera separation of 0.6 to 0.9 m. The poles are oriented such that they do not appear in the photographs. The cameras are simultaneously remotely activated to collect stereo pairs at a resolution of 64 pixels/cm2 (pixel length is 1.2 mm). Ground control on the images is provided by pegs placed 5 meters apart, GPS positioning, and a meter-stick included in each photograph. Initial photo data gathered of a gravel bar on the Henry’s Fork of the Snake River, north of Rexburg, Idaho is sharp and readily segmented using the MatLab-based CLASTS image processing algorithm. The technique developed for imaging mid-scale features (such as cinder

  1. Heuristic Scheduling Algorithm Oriented Dynamic Tasks for Imaging Satellites

    Directory of Open Access Journals (Sweden)

    Maocai Wang

    2014-01-01

    Full Text Available Imaging satellite scheduling is an NP-hard problem with many complex constraints. This paper researches the scheduling problem for dynamic tasks oriented to some emergency cases. After the dynamic properties of satellite scheduling were analyzed, the optimization model is proposed in this paper. Based on the model, two heuristic algorithms are proposed to solve the problem. The first heuristic algorithm arranges new tasks by inserting or deleting them, then inserting them repeatedly according to the priority from low to high, which is named IDI algorithm. The second one called ISDR adopts four steps: insert directly, insert by shifting, insert by deleting, and reinsert the tasks deleted. Moreover, two heuristic factors, congestion degree of a time window and the overlapping degree of a task, are employed to improve the algorithm’s performance. Finally, a case is given to test the algorithms. The results show that the IDI algorithm is better than ISDR from the running time point of view while ISDR algorithm with heuristic factors is more effective with regard to algorithm performance. Moreover, the results also show that our method has good performance for the larger size of the dynamic tasks in comparison with the other two methods.

  2. Urban Land Use Change Detection Using Multisensor Satellite Images

    Institute of Scientific and Technical Information of China (English)

    DENG Jin-Song; WANG Ke; LI Jun; DENG Yan-Hua

    2009-01-01

    Due to inappropriate planning and management, accelerated urban growth and tremendous loss in land, especially cropland, have become a great challenge for sustainable urban development in China, especially in developed urban area in the coastal regions; therefore, there is an urgent need to effectively detect and monitor the land use changes and provide accurate and timely information for planning and management. In this study a method combining principal component analysis (PCA) of multiseusor satellite images from SPOT (systeme pour l'observation de la terre or earth observation satellite)-5 muttispectral (XS) and Landsat-7 enhanced thematic mapper (ETM) panchromatic (PAN) data, and supervised classification was used to detect and analyze the dynamics of land use changes in the city proper of Hangzhou. The overall accuracy of the land use change detection was 90.67% and Kappa index was 0.89. The results indicated that there was a considerable land use change (10.03% of the total area) in the study area from 2001 to 2003, with three major types of land use conversions: from cropland into bnilt-up land, construction site, and water area (fish pond). Changes from orchard land into built-up land were also detected. The method described in this study is feasible and useful for detecting rapid land use change in the urban area.

  3. Images of war: using satellite images for human rights monitoring in Turkish Kurdistan.

    Science.gov (United States)

    de Vos, Hugo; Jongerden, Joost; van Etten, Jacob

    2008-09-01

    In areas of war and armed conflict it is difficult to get trustworthy and coherent information. Civil society and human rights groups often face problems of dealing with fragmented witness reports, disinformation of war propaganda, and difficult direct access to these areas. Turkish Kurdistan was used as a case study of armed conflict to evaluate the potential use of satellite images for verification of witness reports collected by human rights groups. The Turkish army was reported to be burning forests, fields and villages as a strategy in the conflict against guerrilla uprising. This paper concludes that satellite images are useful to validate witness reports of forest fires. Even though the use of this technology for human rights groups will depend on some feasibility factors such as prices, access and expertise, the images proved to be key for analysis of spatial aspects of conflict and valuable for reconstructing a more trustworthy picture.

  4. TIRCIS: thermal infrared compact imaging spectrometer for small satellite applications

    Science.gov (United States)

    Wright, Robert; Lucey, Paul; Crites, Sarah; Garbeil, Harold; Wood, Mark; Pilger, Eric; Gabrieli, Andrea; Honniball, Casey

    2016-10-01

    Measurements of reflectance or emittance in tens of narrow, contiguous wavebands, allow for the derivation of laboratory quality spectra remotely, from which the chemical composition and physical properties of targets can be determined. Although spaceborne (e.g. EO-1 Hyperion) hyperspectral data in the 0.4-2.5 micron (VSWIR) region are available, the provision of equivalent data in the log-wave infrared has lagged behind, there being no currently operational high spatial resolution LWIR imaging spectrometer on orbit. TIRCIS (Thermal Infra-Red Compact Imaging Spectrometer), uses a Fabry-Perot interferometer, an uncooled microbolometer array, and push-broom scanning to acquire hyperspectral image data. Radiometric calibration is provided by blackbody targets while spectral calibration is achieved using monochromatic light sources. The instrument has a mass of <15 kg and dimensions of 53 cm × 25 cm ♢ 22 cm, and has been designed to be compatible with integration into a micro-satellite platform. (A precursor to this instrument was launched onboard a 55 kg microsatellite in October 2015). The optical design yields a 120 m ground sample size given an orbit of 500 km. Over the wavelength interval of 7.5 to 14 microns up to 50 spectral samples are possible. Measured signal-to-noise ratios range from peak values of 500:1 to 1500:1, for source temperature of 10 to 100°C.

  5. A System to Detect Residential Area in Multispectral Satellite Images

    Directory of Open Access Journals (Sweden)

    Seyfallah Bouraoui

    2011-11-01

    Full Text Available In this paper, we propose a new solution to extract complex structures from High-Resolution (HR remote-sensing images. We propose to represent shapes and there relations by using region adjacency graphs. They are generated automatically from the segmented images. Thus, the nodes of the graph represent shape like houses, streets or trees, while arcs describe the adjacency relation between them. In order to be invariant to transformations such as rotation and scaling, the extraction of objects of interest is done by combining two techniques: one based on roof color to detect the bounding boxes of houses, and one based on mathematical morphology notions to detect streets. To recognize residential areas, a model described by a regular language is built. The detection is achieved by looking for a path in the region adjacency graph, which can be recognized as a word belonging to the description language. Our algorithm was tested with success on images from the French satellite SPOT 5 representing the urban area of Strasbourg (France at different spatial resolution.

  6. Dsm Based Orientation of Large Stereo Satellite Image Blocks

    Science.gov (United States)

    d'Angelo, P.; Reinartz, P.

    2012-07-01

    High resolution stereo satellite imagery is well suited for the creation of digital surface models (DSM). A system for highly automated and operational DSM and orthoimage generation based on CARTOSAT-1 imagery is presented, with emphasis on fully automated georeferencing. The proposed system processes level-1 stereo scenes using the rational polynomial coefficients (RPC) universal sensor model. The RPC are derived from orbit and attitude information and have a much lower accuracy than the ground resolution of approximately 2.5 m. In order to use the images for orthorectification or DSM generation, an affine RPC correction is required. In this paper, GCP are automatically derived from lower resolution reference datasets (Landsat ETM+ Geocover and SRTM DSM). The traditional method of collecting the lateral position from a reference image and interpolating the corresponding height from the DEM ignores the higher lateral accuracy of the SRTM dataset. Our method avoids this drawback by using a RPC correction based on DSM alignment, resulting in improved geolocation of both DSM and ortho images. Scene based method and a bundle block adjustment based correction are developed and evaluated for a test site covering the nothern part of Italy, for which 405 Cartosat-1 Stereopairs are available. Both methods are tested against independent ground truth. Checks against this ground truth indicate a lateral error of 10 meters.

  7. Identifying potential solar power generation sites using satellite apt images

    Science.gov (United States)

    Fawz-Ul-Haq, K. R.; Siddiqui, Z. R.

    1994-01-01

    In this paper, satellite APT images have been used to study cloud-cover over Pakistan, so as to determine those areas which have the least frequency of cloudiness. Such areas are likely to receive maximum insolation, and have been shown on a contour map of Pakistan. It is observed that more than half of Pakistan is highly sunny, and has many promising areas for establishing large scale solar electric power generation stations. It is further observed that, some of the productive mineral-rich areas in remote parts, such as Chagai, are highly cloud-free, therefore, it may be possible to meet their mining needs through solar energy. Thus Pakistan, on the threshold of industrialization, has high prospects of obtaining `clean' energy, free of greenhouse gases.

  8. Galileo's first images of Jupiter and the Galilean satellites

    Science.gov (United States)

    Belton, M.J.S.; Head, J. W.; Ingersoll, A.P.; Greeley, R.; McEwen, A.S.; Klaasen, K.P.; Senske, D.; Pappalardo, R.; Collins, G.; Vasavada, A.R.; Sullivan, R.; Simonelli, D.; Geissler, P.; Carr, M.H.; Davies, M.E.; Veverka, J.; Gierasch, P.J.; Banfield, D.; Bell, M.; Chapman, C.R.; Anger, C.; Greenberg, R.; Neukum, G.; Pilcher, C.B.; Beebe, R.F.; Burns, J.A.; Fanale, F.; Ip, W.; Johnson, T.V.; Morrison, D.; Moore, J.; Orton, G.S.; Thomas, P.; West, R.A.

    1996-01-01

    The first images of Jupiter, Io, Europa, and Ganymede from the Galileo spacecraft reveal new information about Jupiter's Great Red Spot (GRS) and the surfaces of the Galilean satellites. Features similar to clusters of thunderstorms were found in the GRS. Nearby wave structures suggest that the GRS may be a shallow atmospheric feature. Changes in surface color and plume distribution indicate differences in resurfacing processes near hot spots on lo. Patchy emissions were seen while Io was in eclipse by Jupiter. The outer margins of prominent linear markings (triple bands) on Europa are diffuse, suggesting that material has been vented from fractures. Numerous small circular craters indicate localized areas of relatively old surface. Pervasive brittle deformation of an ice layer appears to have formed grooves on Ganymede. Dark terrain unexpectedly shows distinctive albedo variations to the limit of resolution.

  9. A new technique for the detection of large scale landslides in glacio-lacustrine deposits using image correlation based upon aerial imagery: A case study from the French Alps

    Science.gov (United States)

    Fernandez, Paz; Whitworth, Malcolm

    2016-10-01

    Landslide monitoring has benefited from recent advances in the use of image correlation of high resolution optical imagery. However, this approach has typically involved satellite imagery that may not be available for all landslides depending on their time of movement and location. This study has investigated the application of image correlation techniques applied to a sequence of aerial imagery to an active landslide in the French Alps. We apply an indirect landslide monitoring technique (COSI-Corr) based upon the cross-correlation between aerial photographs, to obtain horizontal displacement rates. Results for the 2001-2003 time interval are presented, providing a spatial model of landslide activity and motion across the landslide, which is consistent with previous studies. The study has identified areas of new landslide activity in addition to known areas and through image decorrelation has identified and mapped two new lateral landslides within the main landslide complex. This new approach for landslide monitoring is likely to be of wide applicability to other areas characterised by complex ground displacements.

  10. Satellite Image Security Improvement by Combining DWT-DCT Watermarking and AES Encryption

    Directory of Open Access Journals (Sweden)

    Naida.H.Nazmudeen

    2014-06-01

    Full Text Available With the large-scale research in space sciences and technologies, there is a great demand of satellite image security system for providing secure storage and transmission of satellite images. As the demand to protect the sensitive and valuable data from satellites has increased and hence proposed a new method for satellite image security by combining DWT-DCT watermarking and AES encryption. Watermarking techniques developed for multimedia data cannot be directly applied to the satellite images because here the analytic integrity of the data, rather than perceptual quality, is of primary importance. To improve performance, combine discrete wavelet transform (DWT with another equally powerful transform; the discrete cosine transform (DCT. The combined DWT-DCT watermarking algorithm’s imperceptibility was better than the performance of the DWT approach. Modified decision based unsymmetrical trimmed median filter (MDBUTMF algorithm is proposed for the restoration of satellite images that are highly corrupted by salt and pepper noise. Satellite images desire not only the watermarking for copyright protection but also encryption during storage and transmission for preventing information leakage. Hence this paper investigates the security and performance level of joint DWT-DCT watermarking and Advanced Encryption Standard (AES for satellite imagery. Theoretical analysis can be done by calculating PSNR and MSE. The experimental results demonstrate the efficiency of the proposed scheme, which fulfils the strict requirements concerning alterations of satellite images.

  11. Smoothing of Fused Spectral Consistent Satellite Images with TV-based Edge Detection

    DEFF Research Database (Denmark)

    Sveinsson, Johannes; Aanæs, Henrik; Benediktsson, Jon Atli

    2007-01-01

    Several widely used methods have been proposed for fusing high resolution panchromatic data and lower resolution multi-channel data. However, many of these methods fail to maintain the spectral consistency of the fused high resolution image, which is of high importance to many of the applications...... based on satellite data. Additionally, most conventional methods are loosely connected to the image forming physics of the satellite image, giving these methods an ad hoc feel. Vesteinsson et al. [1] proposed a method of fusion of satellite images that is based on the properties of imaging physics...... in a statistically meaningful way and was called spectral consistent panshapening (SCP). In this paper we improve this framework for satellite image fusion by introducing a better image prior, via data-dependent image smoothing. The dependency is obtained via total variation edge detection method....

  12. MORPHOLOGICAL PROFILE AND GRANULOMETRIC MAPS IN EXTRACTION OF BUILDINGS IN VHR SATELLITE IMAGES

    National Research Council Canada - National Science Library

    Kupidura Przemysław; Skulimowska Monika

    2015-01-01

    ...: the morphological profile, and granulometric maps in detecting buildings on satellite images. It briefly explains the theoretical basis for granulometric analysis of image and compares two methods used in research...

  13. [Building Change Detection Based on Multi-Level Rules Classification with Airborne LiDAR Data and Aerial Images].

    Science.gov (United States)

    Gong, Yi-long; Yan, Li

    2015-05-01

    The present paper proposes a new building change detection method combining Lidar point cloud with aerial image, using multi-level rules classification algorithm, to solve building change detection problem between these two kinds of heterogeneous data. Then, a morphological post-processing method combined with area threshold is proposed. Thus, a complete building change detection processing flow that can be applied to actual production is proposed. Finally, the effectiveness of the building change detection method is evaluated, processing the 2010 airborne LiDAR point cloud data and 2009 high resolution aerial image of Changchun City, Jilin province, China; in addition, compared with the object-oriented building change detection method based on support vector machine (SVM) classification, more analysis and evaluation of the suggested method is given. Experiment results show that the performance of the proposed building change detection method is ideal. Its Kappa index is 0. 90, and correctness is 0. 87, which is higher than the object-oriented building change detection method based on SVM classification.

  14. Using GPS/INS data to enhance image matching for real-time aerial triangulation

    Science.gov (United States)

    Tanathong, Supannee; Lee, Impyeong

    2014-11-01

    Direct georeferencing is a promising technique for determining the exterior orientation parameters (EO) of a camera in real-time through the integration of GPS/INS sensors. Instead of using expensive devices, we improve the accuracy of the directly measured EOs through aerial triangulation (AT) and rely on tie-points. In this work, using GPS/INS data, we enhance the KLT tracker to achieve accuracy and speed that is compatible with real-time aerial triangulation. Given GPS/INS data from medium-grade sensors, the proposed system is 48% faster than the original work and tie-points extracted by our system are 6.33% more accurate and more evenly distributed than tie-points extracted by the original work. The AT processing results show that tie-points from the proposed work can reduce the RMSE of the directly measured EOs by 17.87% for position and 23.37% for attitude. Thus, we conclude that our proposed system can be integrated with real-time aerial triangulation.

  15. Mapping soil heterogeneity using RapidEye satellite images

    Science.gov (United States)

    Piccard, Isabelle; Eerens, Herman; Dong, Qinghan; Gobin, Anne; Goffart, Jean-Pierre; Curnel, Yannick; Planchon, Viviane

    2016-04-01

    In the frame of BELCAM, a project funded by the Belgian Science Policy Office (BELSPO), researchers from UCL, ULg, CRA-W and VITO aim to set up a collaborative system to develop and deliver relevant information for agricultural monitoring in Belgium. The main objective is to develop remote sensing methods and processing chains able to ingest crowd sourcing data, provided by farmers or associated partners, and to deliver in return relevant and up-to-date information for crop monitoring at the field and district level based on Sentinel-1 and -2 satellite imagery. One of the developments within BELCAM concerns an automatic procedure to detect soil heterogeneity within a parcel using optical high resolution images. Such heterogeneity maps can be used to adjust farming practices according to the detected heterogeneity. This heterogeneity may for instance be caused by differences in mineral composition of the soil, organic matter content, soil moisture or soil texture. Local differences in plant growth may be indicative for differences in soil characteristics. As such remote sensing derived vegetation indices may be used to reveal soil heterogeneity. VITO started to delineate homogeneous zones within parcels by analyzing a series of RapidEye images acquired in 2015 (as a precursor for Sentinel-2). Both unsupervised classification (ISODATA, K-means) and segmentation techniques were tested. Heterogeneity maps were generated from images acquired at different moments during the season (13 May, 30 June, 17 July, 31 August, 11 September and 1 November 2015). Tests were performed using blue, green, red, red edge and NIR reflectances separately and using derived indices such as NDVI, fAPAR, CIrededge, NDRE2. The results for selected winter wheat, maize and potato fields were evaluated together with experts from the collaborating agricultural research centers. For a few fields UAV images and/or yield measurements were available for comparison.

  16. Object-based illumination normalization for multi-temporal satellite images in urban area

    Science.gov (United States)

    Su, Nan; Zhang, Ye; Tian, Shu; Yan, Yiming

    2016-09-01

    Multi-temporal satellite images acquisition with different illumination conditions cause radiometric difference to have a huge effect on image quality during remote sensing image processing. In particular, image matching of satellite stereo images with great difference between acquisition dates is very difficult for the high-precision DSM generation in the field of satellite photogrammetry. Therefore, illumination normalization is one of the greatest application technology to eliminate radiometric difference for image matching and other image applications. In this paper, we proposed a novel method of object-based illumination normalization to improve image matching of different temporal satellite stereo images in urban area. Our proposed method include two main steps: 1) the object extraction 2) multi-level illumination normalization. Firstly, we proposed a object extraction method for the same objects extraction among the multi-temporal satellite images, which can keep the object structural attribute. Moreover, the multi-level illumination normalization is proposed by combining gradient domain method and singular value decomposition (SVD) according to characteristic information of relevant objects. Our proposed method has great improvement for the illumination of object area to be benefit for image matching in urban area with multiple objects. And the histogram similarity parameter and matching rate are used for illumination consistency quantitative evaluation. The experiments have been conducted on different satellite images with different acquisition dates in the same urban area to verify the effectiveness of our proposed method. The experimental results demonstrate a good performance by comparing other methods.

  17. MaNIAC-UAV - a methodology for automatic pavement defects detection using images obtained by Unmanned Aerial Vehicles

    Science.gov (United States)

    Henrique Castelo Branco, Luiz; César Lima Segantine, Paulo

    2015-09-01

    Intelligent Transportation Systems - ITS is a set of integrated technologies (Remote Sensing, Image Processing, Communications Systems and others) that aim to offer services and advanced traffic management for the several transportation modes (road, air and rail). Collect data on the characteristics and conditions of the road surface and keep them update is an important and difficult task that needs to be currently managed in order to reduce accidents and vehicle maintenance costs. Nowadays several roads and highways are paved, but usually there is insufficient updated data about current condition and status. There are different types of pavement defects on the roads and to keep them in good condition they should be constantly monitored and maintained according to pavement management strategy. This paper presents a methodology to obtain, automatically, information about the conditions of the highway asphalt pavement. Data collection was done through remote sensing using an UAV (Unmanned Aerial Vehicle) and the image processing and pattern recognition techniques through Geographic Information System.

  18. Satellite Image Time Series Decomposition Based on EEMD

    Directory of Open Access Journals (Sweden)

    Yun-long Kong

    2015-11-01

    Full Text Available Satellite Image Time Series (SITS have recently been of great interest due to the emerging remote sensing capabilities for Earth observation. Trend and seasonal components are two crucial elements of SITS. In this paper, a novel framework of SITS decomposition based on Ensemble Empirical Mode Decomposition (EEMD is proposed. EEMD is achieved by sifting an ensemble of adaptive orthogonal components called Intrinsic Mode Functions (IMFs. EEMD is noise-assisted and overcomes the drawback of mode mixing in conventional Empirical Mode Decomposition (EMD. Inspired by these advantages, the aim of this work is to employ EEMD to decompose SITS into IMFs and to choose relevant IMFs for the separation of seasonal and trend components. In a series of simulations, IMFs extracted by EEMD achieved a clear representation with physical meaning. The experimental results of 16-day compositions of Moderate Resolution Imaging Spectroradiometer (MODIS, Normalized Difference Vegetation Index (NDVI, and Global Environment Monitoring Index (GEMI time series with disturbance illustrated the effectiveness and stability of the proposed approach to monitoring tasks, such as applications for the detection of abrupt changes.

  19. The Advanced X-ray Imaging Satellite (AXIS)

    Science.gov (United States)

    Reynolds, Christopher S.; Mushotzky, Richard

    2017-08-01

    The Advanced X-ray Imaging Satellite (AXIS) will follow in the footsteps of the spectacularly successful Chandra X-ray Observatory with similar or higher angular resolution and an order of magnitude more collecting area in the 0.3-10keV band. These capabilities will enable major advances in many of the most active areas of astrophysics, including (i) mapping event horizon scale structure in AGN accretion disks and the determination of supermassive black hole (SMBH) spins through monitoring of gravitationally-microlensed quasars; (ii) dramatically deepening our understanding of AGN feedback in galaxies and galaxy clusters out to high-z through the direct imaging of AGN winds and the interaction of jets with the hot interstellar/intracluster medium; (iii) understanding the fueling of AGN by probing hot flows inside of the SMBH sphere of influence; (iv) obtaining geometric distance measurements using dust scattering halos. With a nominal 2028 launch, AXIS will be enormously synergistic with LSST, ALMA, WFIRST and ATHENA, and will be a valuable precursor to Lynx. AXIS is enabled by breakthroughs in the construction of light-weight X-ray optics from mono-crystalline silicon blocks, building on recent developments in the semiconductor industry. Here, we describe the straw-man concept for AXIS, some of the high profile science that this observatory will address, and how you can become involved.

  20. High Resolution Imaging of Satellites with Ground-Based 10-m Astronomical Telescopes

    Energy Technology Data Exchange (ETDEWEB)

    Marois, C

    2007-01-04

    High resolution imaging of artificial satellites can play an important role in current and future space endeavors. One such use is acquiring detailed images that can be used to identify or confirm damage and aid repair plans. It is shown that a 10-m astronomical telescope equipped with an adaptive optics system (AO) to correct for atmospheric turbulence using a natural guide star can acquire high resolution images of satellites in low-orbits using a fast shutter and a near-infrared camera even if the telescope is not capable of tracking satellites. With the telescope pointing towards the satellite projected orbit and less than 30 arcsec away from a guide star, multiple images of the satellite are acquired on the detector using the fast shutter. Images can then be shifted and coadded by post processing to increase the satellite signal to noise ratio. Using the Keck telescope typical Strehl ratio and anisoplanatism angle as well as a simple diffusion/reflection model for a satellite 400 km away observed near Zenith at sunset or sunrise, it is expected that such system will produced > 10{sigma} K-band images at a resolution of 10 cm inside a 60 arcsec diameter field of view. If implemented, such camera could deliver the highest resolution satellite images ever acquired from the ground.

  1. High Resolution Imaging of Satellites with Ground-Based 10-m Astronomical Telescopes

    Energy Technology Data Exchange (ETDEWEB)

    Marois, C

    2007-01-04

    High resolution imaging of artificial satellites can play an important role in current and future space endeavors. One such use is acquiring detailed images that can be used to identify or confirm damage and aid repair plans. It is shown that a 10-m astronomical telescope equipped with an adaptive optics system (AO) to correct for atmospheric turbulence using a natural guide star can acquire high resolution images of satellites in low-orbits using a fast shutter and a near-infrared camera even if the telescope is not capable of tracking satellites. With the telescope pointing towards the satellite projected orbit and less than 30 arcsec away from a guide star, multiple images of the satellite are acquired on the detector using the fast shutter. Images can then be shifted and coadded by post processing to increase the satellite signal to noise ratio. Using the Keck telescope typical Strehl ratio and anisoplanatism angle as well as a simple diffusion/reflection model for a satellite 400 km away observed near Zenith at sunset or sunrise, it is expected that such system will produced > 10{sigma} K-band images at a resolution of 10 cm inside a 60 arcsec diameter field of view. If implemented, such camera could deliver the highest resolution satellite images ever acquired from the ground.

  2. First Satellite Imaging of Auroral Pulsations by the Fast Auroral Imager on e-POP

    Science.gov (United States)

    Lui, A.; Cogger, L.; Howarth, A. D.; Yau, A. W.

    2015-12-01

    We report the first satellite imaging of auroral pulsations by the Fast Auroral Imager (FAI) onboard the Enhanced Polar Outflow Probe (e-POP) satellite. The near-infrared camera of FAI is capable of providing up to two auroral images per second, ideal for investigation of pulsating auroras. The auroral pulsations were observed within the auroral bulge formed during a substorm interval on 2014 February 19. This first satellite view of these pulsations from FAI reveals that (1) several pulsating auroral channels (PACs) occur within the auroral bulge, (2) periods of the intensity pulsations span over one decade within the auroral bulge, and (3) there is no apparent trend of longer pulsation periods associated with higher latitudes for these PACs. Although PACs resemble in some respect stable pulsating auroras reported previously but they have several important differences in characteristics.PACs are not embedded in or emerging from omega bands or torches and are located at significant distances from the equatorward boundary of the auroral oval, unlike the characteristics of stable pulsating auroras.

  3. Estimate Landslide Volume with Genetic Algorithms and Image Similarity Method from Single Satellite Image

    Science.gov (United States)

    Yu, Ting-To

    2013-04-01

    It is important to acquire the volume of landslide in short period of time. For hazard mitigation and also emergency response purpose, the traditional method takes much longer time than expected. Due to the weather limit, traffic accessibility and many regulations of law, it take months to handle these process before the actual carry out of filed work. Remote sensing imagery can get the data as long as the visibility allowed, which happened only few day after the event. While traditional photometry requires a stereo pairs images to produce the post event DEM for calculating the change of volume. Usually have to wait weeks or even months for gathering such data, LiDAR or ground GPS measurement might take even longer period of time with much higher cost. In this study we use one post event satellite image and pre-event DTM to compare the similarity between these by alter the DTM with genetic algorithms. The outcome of smartest guess from GAs shall remove or add exact values of height at each location, which been converted into shadow relief viewgraph to compare with satellite image. Once the similarity threshold been make then the guessing work stop. It takes only few hours to finish the entire task, the computed accuracy is around 70% by comparing to the high resolution LiDAR survey at a landslide, southern Taiwan. With extra GCPs, the estimate accuracy can improve to 85% and also within few hours after the receiving of satellite image. Data of this demonstration case is a 5 m DTM at 2005, 2M resolution FormoSat optical image at 2009 and 5M LiDAR at 2010. The GAs and image similarity code is developed on Matlab at windows PC.

  4. Detection of Tree Crowns Based on Reclassification Using Aerial Images and LIDAR Data

    Science.gov (United States)

    Talebi, S.; Zarea, A.; Sadeghian, S.; Arefi, H.

    2013-09-01

    Tree detection using aerial sensors in early decades was focused by many researchers in different fields including Remote Sensing and Photogrammetry. This paper is intended to detect trees in complex city areas using aerial imagery and laser scanning data. Our methodology is a hierarchal unsupervised method consists of some primitive operations. This method could be divided into three sections, in which, first section uses aerial imagery and both second and third sections use laser scanners data. In the first section a vegetation cover mask is created in both sunny and shadowed areas. In the second section Rate of Slope Change (RSC) is used to eliminate grasses. In the third section a Digital Terrain Model (DTM) is obtained from LiDAR data. By using DTM and Digital Surface Model (DSM) we would get to Normalized Digital Surface Model (nDSM). Then objects which are lower than a specific height are eliminated. Now there are three result layers from three sections. At the end multiplication operation is used to get final result layer. This layer will be smoothed by morphological operations. The result layer is sent to WG III/4 to evaluate. The evaluation result shows that our method has a good rank in comparing to other participants' methods in ISPRS WG III/4, when assessed in terms of 5 indices including area base completeness, area base correctness, object base completeness, object base correctness and boundary RMS. With regarding of being unsupervised and automatic, this method is improvable and could be integrate with other methods to get best results.

  5. Semi-automted analysis of high-resolution aerial images to quantify docks in Upper Midwest glacial lakes

    Science.gov (United States)

    Beck, Marcus W.; Vondracek, Bruce C.; Hatch, Lorin K.; Vinje, Jason

    2013-01-01

    Lake resources can be negatively affected by environmental stressors originating from multiple sources and different spatial scales. Shoreline development, in particular, can negatively affect lake resources through decline in habitat quality, physical disturbance, and impacts on fisheries. The development of remote sensing techniques that efficiently characterize shoreline development in a regional context could greatly improve management approaches for protecting and restoring lake resources. The goal of this study was to develop an approach using high-resolution aerial photographs to quantify and assess docks as indicators of shoreline development. First, we describe a dock analysis workflow that can be used to quantify the spatial extent of docks using aerial images. Our approach incorporates pixel-based classifiers with object-based techniques to effectively analyze high-resolution digital imagery. Second, we apply the analysis workflow to quantify docks for 4261 lakes managed by the Minnesota Department of Natural Resources. Overall accuracy of the analysis results was 98.4% (87.7% based on ) after manual post-processing. The analysis workflow was also 74% more efficient than the time required for manual digitization of docks. These analyses have immediate relevance for resource planning in Minnesota, whereas the dock analysis workflow could be used to quantify shoreline development in other regions with comparable imagery. These data can also be used to better understand the effects of shoreline development on aquatic resources and to evaluate the effects of shoreline development relative to other stressors.

  6. The Application of the Technology of 3D Satellite Cloud Imaging in Virtual Reality Simulation

    Directory of Open Access Journals (Sweden)

    Xiao-fang Xie

    2007-05-01

    Full Text Available Using satellite cloud images to simulate clouds is one of the new visual simulation technologies in Virtual Reality (VR. Taking the original data of satellite cloud images as the source, this paper depicts specifically the technology of 3D satellite cloud imaging through the transforming of coordinates and projection, creating a DEM (Digital Elevation Model of cloud imaging and 3D simulation. A Mercator projection was introduced to create a cloud image DEM, while solutions for geodetic problems were introduced to calculate distances, and the outer-trajectory science of rockets was introduced to obtain the elevation of clouds. For demonstration, we report on a computer program to simulate the 3D satellite cloud images.

  7. OBJECT BASED AGRICULTURAL LAND COVER CLASSIFICATION MAP OF SHADOWED AREAS FROM AERIAL IMAGE AND LIDAR DATA USING SUPPORT VECTOR MACHINE

    Directory of Open Access Journals (Sweden)

    R. T. Alberto

    2016-06-01

    Full Text Available Aerial image and LiDAR data offers a great possibility for agricultural land cover mapping. Unfortunately, these images leads to shadowy pixels. Management of shadowed areas for classification without image enhancement were investigated. Image segmentation approach using three different segmentation scales were used and tested to segment the image for ground features since only the ground features are affected by shadow caused by tall features. The RGB band and intensity were the layers used for the segmentation having an equal weights. A segmentation scale of 25 was found to be the optimal scale that will best fit for the shadowed and non-shadowed area classification. The SVM using Radial Basis Function kernel was then applied to extract classes based on properties extracted from the Lidar data and orthophoto. Training points for different classes including shadowed areas were selected homogeneously from the orthophoto. Separate training points for shadowed areas were made to create additional classes to reduced misclassification. Texture classification and object-oriented classifiers have been examined to reduced heterogeneity problem. The accuracy of the land cover classification using 25 scale segmentation after accounting for the shadow detection and classification was significantly higher compared to higher scale of segmentation.

  8. Feature Learning Based Approach for Weed Classification Using High Resolution Aerial Images from a Digital Camera Mounted on a UAV

    Directory of Open Access Journals (Sweden)

    Calvin Hung

    2014-12-01

    Full Text Available The development of low-cost unmanned aerial vehicles (UAVs and light weight imaging sensors has resulted in significant interest in their use for remote sensing applications. While significant attention has been paid to the collection, calibration, registration and mosaicking of data collected from small UAVs, the interpretation of these data into semantically meaningful information can still be a laborious task. A standard data collection and classification work-flow requires significant manual effort for segment size tuning, feature selection and rule-based classifier design. In this paper, we propose an alternative learning-based approach using feature learning to minimise the manual effort required. We apply this system to the classification of invasive weed species. Small UAVs are suited to this application, as they can collect data at high spatial resolutions, which is essential for the classification of small or localised weed outbreaks. In this paper, we apply feature learning to generate a bank of image filters that allows for the extraction of features that discriminate between the weeds of interest and background objects. These features are pooled to summarise the image statistics and form the input to a texton-based linear classifier that classifies an image patch as weed or background. We evaluated our approach to weed classification on three weeds of significance in Australia: water hyacinth, tropical soda apple and serrated tussock. Our results showed that collecting images at 5–10 m resulted in the highest classifier accuracy, indicated by F1 scores of up to 94%.

  9. Comprehensive Comparison of Two Image-Based Point Clouds from Aerial Photos with Airborne LIDAR for Large-Scale Mapping

    Science.gov (United States)

    Widyaningrum, E.; Gorte, B. G. H.

    2017-09-01

    The integration of computer vision and photogrammetry to generate three-dimensional (3D) information from images has contributed to a wider use of point clouds, for mapping purposes. Large-scale topographic map production requires 3D data with high precision and accuracy to represent the real conditions of the earth surface. Apart from LiDAR point clouds, the image-based matching is also believed to have the ability to generate reliable and detailed point clouds from multiple-view images. In order to examine and analyze possible fusion of LiDAR and image-based matching for large-scale detailed mapping purposes, point clouds are generated by Semi Global Matching (SGM) and by Structure from Motion (SfM). In order to conduct comprehensive and fair comparison, this study uses aerial photos and LiDAR data that were acquired at the same time. Qualitative and quantitative assessments have been applied to evaluate LiDAR and image-matching point clouds data in terms of visualization, geometric accuracy, and classification result. The comparison results conclude that LiDAR is the best data for large-scale mapping.

  10. Unmanned aerial vehicle: A unique platform for low-altitude remote sensing for crop management

    Science.gov (United States)

    Unmanned aerial vehicles (UAV) provide a unique platform for remote sensing to monitor crop fields that complements remote sensing from satellite, aircraft and ground-based platforms. The UAV-based remote sensing is versatile at ultra-low altitude to be able to provide an ultra-high-resolution imag...

  11. Spatio-temporal multi-modality ontology for indexing and retrieving satellite images

    OpenAIRE

    MESSOUDI, Wassim; FARAH, Imed Riadh; SAHEB ETTABAA, Karim; Ben Ghezala, Henda; SOLAIMAN, Basel

    2009-01-01

    International audience; This paper presents spatio-temporal multi-modality ontology for indexing and retrieving satellite images in the high level to improve the quality of the system retrieval and to perform semantic in the retrieval process.Our approach is based on three modules: (1) regions and features extraction, (2) ontological indexing and (3) semantic image retrieval. The first module allows extracting regions from the satellite image using the fuzzy c-means FCM) segmentation algorith...

  12. Revealing glacier flow and surge dynamics from animated satellite image sequences: examples from the Karakoram

    OpenAIRE

    Paul, F

    2015-01-01

    Although animated images are very popular on the internet, they have so far found only limited use for glaciological applications. With long time series of satellite images becoming increasingly available and glaciers being well recognized for their rapid changes and variable flow dynamics, animated sequences of multiple satellite images reveal glacier dynamics in a time-lapse mode, making the otherwise slow changes of glacier movement visible and understandable to the wider...

  13. Region of Interest Detection Based on Histogram Segmentation for Satellite Image

    Science.gov (United States)

    Kiadtikornthaweeyot, Warinthorn; Tatnall, Adrian R. L.

    2016-06-01

    High resolution satellite imaging is considered as the outstanding applicant to extract the Earth's surface information. Extraction of a feature of an image is very difficult due to having to find the appropriate image segmentation techniques and combine different methods to detect the Region of Interest (ROI) most effectively. This paper proposes techniques to classify objects in the satellite image by using image processing methods on high-resolution satellite images. The systems to identify the ROI focus on forests, urban and agriculture areas. The proposed system is based on histograms of the image to classify objects using thresholding. The thresholding is performed by considering the behaviour of the histogram mapping to a particular region in the satellite image. The proposed model is based on histogram segmentation and morphology techniques. There are five main steps supporting each other; Histogram classification, Histogram segmentation, Morphological dilation, Morphological fill image area and holes and ROI management. The methods to detect the ROI of the satellite images based on histogram classification have been studied, implemented and tested. The algorithm is be able to detect the area of forests, urban and agriculture separately. The image segmentation methods can detect the ROI and reduce the size of the original image by discarding the unnecessary parts.

  14. Detecting Anomaly Regions in Satellite Image Time Series Based on Sesaonal Autocorrelation Analysis

    Science.gov (United States)

    Zhou, Z.-G.; Tang, P.; Zhou, M.

    2016-06-01

    Anomaly regions in satellite images can reflect unexpected changes of land cover caused by flood, fire, landslide, etc. Detecting anomaly regions in satellite image time series is important for studying the dynamic processes of land cover changes as well as for disaster monitoring. Although several methods have been developed to detect land cover changes using satellite image time series, they are generally designed for detecting inter-annual or abrupt land cover changes, but are not focusing on detecting spatial-temporal changes in continuous images. In order to identify spatial-temporal dynamic processes of unexpected changes of land cover, this study proposes a method for detecting anomaly regions in each image of satellite image time series based on seasonal autocorrelation analysis. The method was validated with a case study to detect spatial-temporal processes of a severe flooding using Terra/MODIS image time series. Experiments demonstrated the advantages of the method that (1) it can effectively detect anomaly regions in each of satellite image time series, showing spatial-temporal varying process of anomaly regions, (2) it is flexible to meet some requirement (e.g., z-value or significance level) of detection accuracies with overall accuracy being up to 89% and precision above than 90%, and (3) it does not need time series smoothing and can detect anomaly regions in noisy satellite images with a high reliability.

  15. DETECTING ANOMALY REGIONS IN SATELLITE IMAGE TIME SERIES BASED ON SESAONAL AUTOCORRELATION ANALYSIS

    Directory of Open Access Journals (Sweden)

    Z.-G. Zhou

    2016-06-01

    Full Text Available Anomaly regions in satellite images can reflect unexpected changes of land cover caused by flood, fire, landslide, etc. Detecting anomaly regions in satellite image time series is important for studying the dynamic processes of land cover changes as well as for disaster monitoring. Although several methods have been developed to detect land cover changes using satellite image time series, they are generally designed for detecting inter-annual or abrupt land cover changes, but are not focusing on detecting spatial-temporal changes in continuous images. In order to identify spatial-temporal dynamic processes of unexpected changes of land cover, this study proposes a method for detecting anomaly regions in each image of satellite image time series based on seasonal autocorrelation analysis. The method was validated with a case study to detect spatial-temporal processes of a severe flooding using Terra/MODIS image time series. Experiments demonstrated the advantages of the method that (1 it can effectively detect anomaly regions in each of satellite image time series, showing spatial-temporal varying process of anomaly regions, (2 it is flexible to meet some requirement (e.g., z-value or significance level of detection accuracies with overall accuracy being up to 89% and precision above than 90%, and (3 it does not need time series smoothing and can detect anomaly regions in noisy satellite images with a high reliability.

  16. Super-Resolution Reconstruction of High-Resolution Satellite ZY-3 TLC Images.

    Science.gov (United States)

    Li, Lin; Wang, Wei; Luo, Heng; Ying, Shen

    2017-05-07

    Super-resolution (SR) image reconstruction is a technique used to recover a high-resolution image using the cumulative information provided by several low-resolution images. With the help of SR techniques, satellite remotely sensed images can be combined to achieve a higher-resolution image, which is especially useful for a two- or three-line camera satellite, e.g., the ZY-3 high-resolution Three Line Camera (TLC) satellite. In this paper, we introduce the application of the SR reconstruction method, including motion estimation and the robust super-resolution technique, to ZY-3 TLC images. The results show that SR reconstruction can significantly improve both the resolution and image quality of ZY-3 TLC images.

  17. High resolution Doppler imager on the Upper Atmosphere Research Satellite

    Energy Technology Data Exchange (ETDEWEB)

    Skinner, W.R.; Hays, P.B.; Grassl, H.J.; Gell, D.A.; Burrage, M.D.; Marshall, A.R.; Ortland, D.A. [Univ. of Michigan, Ann Arbor, MI (United States)

    1994-12-31

    The High Resolution Doppler Imager (HRDI) on the Upper Atmosphere Research Satellite has been providing measurements of the wind field in the stratosphere, mesosphere and lower thermosphere since November 1991. Examination of various calibration data indicates the instrument has remained remarkably stable since launch. The instrument has a thermal drift of about 30 m/s/{degree}C (slightly dependent on wavelength) and a long-term temporal drift that has amounted to about 80 m/s since launch. These effects are removed in the data processing leaving an uncertainty in the instrument stability of {minus}2 nVs. The temperature control of the instrument has improved significantly since launch as a new method was implemented. The initial temperature control held the instrument temperature at about {+-}1{degree}C. The improved method, which holds constant the temperature of the optical bench instead of the radiator, keeps the instrument temperature at about 0.2{degree}C. The calibrations indicate very little change in the sensitivity of the instrument. The detector response has shown no degradation and the optics have not changed their transmittance.

  18. Improving multispectral satellite image compression using onboard subpixel registration

    Science.gov (United States)

    Albinet, Mathieu; Camarero, Roberto; Isnard, Maxime; Poulet, Christophe; Perret, Jokin

    2013-09-01

    Future CNES earth observation missions will have to deal with an ever increasing telemetry data rate due to improvements in resolution and addition of spectral bands. Current CNES image compressors implement a discrete wavelet transform (DWT) followed by a bit plane encoding (BPE) but only on a mono spectral basis and do not profit from the multispectral redundancy of the observed scenes. Recent CNES studies have proven a substantial gain on the achievable compression ratio, +20% to +40% on selected scenarios, by implementing a multispectral compression scheme based on a Karhunen Loeve transform (KLT) followed by the classical DWT+BPE. But such results can be achieved only on perfectly registered bands; a default of registration as low as 0.5 pixel ruins all the benefits of multispectral compression. In this work, we first study the possibility to implement a multi-bands subpixel onboard registration based on registration grids generated on-the-fly by the satellite attitude control system and simplified resampling and interpolation techniques. Indeed bands registration is usually performed on ground using sophisticated techniques too computationally intensive for onboard use. This fully quantized algorithm is tuned to meet acceptable registration performances within stringent image quality criteria, with the objective of onboard real-time processing. In a second part, we describe a FPGA implementation developed to evaluate the design complexity and, by extrapolation, the data rate achievable on a spacequalified ASIC. Finally, we present the impact of this approach on the processing chain not only onboard but also on ground and the impacts on the design of the instrument.

  19. Satellite Image Processing for Land Use and Land Cover Mapping

    Directory of Open Access Journals (Sweden)

    Ashoka Vanjare

    2014-09-01

    Full Text Available In this paper, urban growth of Bangalore region is analyzed and discussed by using multi-temporal and multi-spectral Landsat satellite images. Urban growth analysis helps in understanding the change detection of Bangalore region. The change detection is studied over a period of 39 years and the region of interest covers an area of 2182 km2. The main cause for urban growth is the increase in population. In India, rapid urbanization is witnessed due to an increase in the population, continuous development has affected the existence of natural resources. Therefore observing and monitoring the natural resources (land use plays an important role. To analyze changed detection, researcher’s use remote sensing data. Continuous use of remote sensing data helps researchers to analyze the change detection. The main objective of this study is to monitor land cover changes of Bangalore district which covers rural and urban regions using multi-temporal and multi-sensor Landsat - multi-spectral scanner (MSS, thematic mapper (TM, Enhanced Thematic mapper plus (ETM+ MSS, TM and ETM+ images captured in the years 1973, 1992, 1999, 2002, 2005, 2008 and 2011. Temporal changes were determined by using maximum likelihood classification method. The classification results contain four land cover classes namely, built-up, vegetation, water and barren land. The results indicate that the region is densely developed which has resulted in decrease of water and vegetation regions. The continuous transformation of barren land to built-up region has affected water and vegetation regions. Generally, from 1973 to 2011 the percentage of urban region has increased from 4.6% to 25.43%, mainly due to urbanization.

  20. Applying object-based image analysis and knowledge-based classification to ADS-40 digital aerial photographs to facilitate complex forest land cover classification

    Science.gov (United States)

    Hsieh, Yi-Ta; Chen, Chaur-Tzuhn; Chen, Jan-Chang

    2017-01-01

    In general, considerable human and material resources are required for performing a forest inventory survey. Using remote sensing technologies to save forest inventory costs has thus become an important topic in forest inventory-related studies. Leica ADS-40 digital aerial photographs feature advantages such as high spatial resolution, high radiometric resolution, and a wealth of spectral information. As a result, they have been widely used to perform forest inventories. We classified ADS-40 digital aerial photographs according to the complex forest land cover types listed in the Fourth Forest Resource Survey in an effort to establish a classification method for categorizing ADS-40 digital aerial photographs. Subsequently, we classified the images using the knowledge-based classification method in combination with object-based analysis techniques, decision tree classification techniques, classification parameters such as object texture, shape, and spectral characteristics, a class-based classification method, and geographic information system mapping information. Finally, the results were compared with manually interpreted aerial photographs. Images were classified using a hierarchical classification method comprised of four classification levels (levels 1 to 4). The classification overall accuracy (OA) of levels 1 to 4 is within a range of 64.29% to 98.50%. The final result comparisons showed that the proposed classification method achieved an OA of 78.20% and a kappa coefficient of 0.7597. On the basis of the image classification results, classification errors occurred mostly in images of sunlit crowns because the image values for individual trees varied. Such a variance was caused by the crown structure and the incident angle of the sun. These errors lowered image classification accuracy and warrant further studies. This study corroborates the high feasibility for mapping complex forest land cover types using ADS-40 digital aerial photographs.

  1. Analysis of Unmanned Aerial System-Based CIR Images in Forestry—A New Perspective to Monitor Pest Infestation Levels

    Directory of Open Access Journals (Sweden)

    Jan Rudolf Karl Lehmann

    2015-03-01

    Full Text Available The detection of pest infestation is an important aspect of forest management. In the case of the oak splendour beetle (Agrilus biguttatus infestation, the affected oaks (Quercus sp. show high levels of defoliation and altered canopy reflection signature. These critical features can be identified in high-resolution colour infrared (CIR images of the tree crown and branches level captured by Unmanned Aerial Systems (UAS. In this study, we used a small UAS equipped with a compact digital camera which has been calibrated and modified to record not only the visual but also the near infrared reflection (NIR of possibly infested oaks. The flight campaigns were realized in August 2013, covering two study sites which are located in a rural area in western Germany. Both locations represent small-scale, privately managed commercial forests in which oaks are economically valuable species. Our workflow includes the CIR/NIR image acquisition, mosaicking, georeferencing and pixel-based image enhancement followed by object-based image classification techniques. A modified Normalized Difference Vegetation Index (NDVImod derived classification was used to distinguish between five vegetation health classes, i.e., infested, healthy or dead branches, other vegetation and canopy gaps. We achieved an overall Kappa Index of Agreement (KIA   of 0.81 and 0.77 for each study site, respectively. This approach offers a low-cost alternative to private forest owners who pursue a sustainable management strategy.

  2. Development of Fast Error Compensation Algorithm for Integrated Inertial-Satellite Navigation System of Small-size Unmanned Aerial Vehicles in Complex Environment

    Directory of Open Access Journals (Sweden)

    A. V. Fomichev

    2015-01-01

    Full Text Available In accordance with the structural features of small-size unmanned aerial vehicle (UAV, and considering the feasibility of this project, the article studies an integrated inertial-satellite navigation system (INS. The INS algorithm development is based on the method of indirect filtration and principle of loosely coupled combination of output data on UAV positions and velocity. Data on position and velocity are provided from the strapdown inertial navigation system (SINS and satellite navigation system (GPS. A difference between the output flows of measuring data on position and velocity provided from the SINS and GPS is used to evaluate SINS errors by means of the basic algorithm of Kalman filtering. Then the outputs of SINS are revised. The INS possesses the following advantages: a simpler mathematical model of Kalman filtering, high reliability, two independently operating navigation systems, and high redundancy of available navigation information.But in case of loosely coupled scheme, INS can meet the challenge of high precision and reliability of navigation only when the SINS and GPS operating conditions are normal all the time. The proposed INS is used with UAV moving in complex environment due to obstacles available, severe natural climatic conditions, etc. This case expects that it is impossible for UAV to receive successful GPS-signals frequently. In order to solve this problem, was developed an algorithm for rapid compensation for errors of INS information, which could effectively solve the problem of failure of the navigation system in case there are no GPS-signals .Since it is almost impossible to obtain the data of the real trajectory in practice, in the course of simulation in accordance with the kinematic model of the UAV and the complex environment of the terrain, the flight path generator is used to produce the flight path. The errors of positions and velocities are considered as an indicator of the INS effectiveness. The results

  3. Imaging-Duration Embedded Dynamic Scheduling of Earth Observation Satellites for Emergent Events

    Directory of Open Access Journals (Sweden)

    Xiaonan Niu

    2015-01-01

    Full Text Available We present novel two-stage dynamic scheduling of earth observation satellites to provide emergency response by making full use of the duration of the imaging task execution. In the first stage, the multiobjective genetic algorithm NSGA-II is used to produce an optimal satellite imaging schedule schema, which is robust to dynamic adjustment as possible emergent events occur in the future. In the second stage, when certain emergent events do occur, a dynamic adjusting heuristic algorithm (CTM-DAHA is applied to arrange new tasks into the robust imaging schedule. Different from the existing dynamic scheduling methods, the imaging duration is embedded in the two stages to make full use of current satellite resources. In the stage of robust satellite scheduling, total task execution time is used as a robust indicator to obtain a satellite schedule with less imaging time. In other words, more imaging time is preserved for future emergent events. In the stage of dynamic adjustment, a compact task merging strategy is applied to combine both of existing tasks and emergency tasks into a composite task with least imaging time. Simulated experiments indicate that the proposed method can produce a more robust and effective satellite imaging schedule.

  4. Two Algorithms for the Detection and Tracking of Moving Vehicle Targets in Aerial Infrared Image Sequences

    Directory of Open Access Journals (Sweden)

    Yutian Cao

    2015-12-01

    Full Text Available In this paper, by analyzing the characteristics of infrared moving targets, a Symmetric Frame Differencing Target Detection algorithm based on local clustering segmentation is proposed. In consideration of the high real-time performance and accuracy of traditional symmetric differencing, this novel algorithm uses local grayscale clustering to accomplish target detection after carrying out symmetric frame differencing to locate the regions of change. In addition, the mean shift tracking algorithm is also improved to solve the problem of missed targets caused by error convergence. As a result, a kernel-based mean shift target tracking algorithm based on detection updates is also proposed. This tracking algorithm makes use of the interaction between detection and tracking to correct the tracking errors in real time and to realize robust target tracking in complex scenes. In addition, the validity, robustness and stability of the proposed algorithms are all verified by experiments on mid-infrared aerial sequences with vehicles as targets.

  5. REGION-BASED 3D SURFACE RECONSTRUCTION USING IMAGES ACQUIRED BY LOW-COST UNMANNED AERIAL SYSTEMS

    Directory of Open Access Journals (Sweden)

    Z. Lari

    2015-08-01

    Full Text Available Accurate 3D surface reconstruction of our environment has become essential for an unlimited number of emerging applications. In the past few years, Unmanned Aerial Systems (UAS are evolving as low-cost and flexible platforms for geospatial data collection that could meet the needs of aforementioned application and overcome limitations of traditional airborne and terrestrial mobile mapping systems. Due to their payload restrictions, these systems usually include consumer-grade imaging and positioning sensor which will negatively impact the quality of the collected geospatial data and reconstructed surfaces. Therefore, new surface reconstruction surfaces are needed to mitigate the impact of using low-cost sensors on the final products. To date, different approaches have been proposed to for 3D surface construction using overlapping images collected by imaging sensor mounted on moving platforms. In these approaches, 3D surfaces are mainly reconstructed based on dense matching techniques. However, generated 3D point clouds might not accurately represent the scanned surfaces due to point density variations and edge preservation problems. In order to resolve these problems, a new region-based 3D surface renostruction trchnique is introduced in this paper. This approach aims to generate a 3D photo-realistic model of individually scanned surfaces within the captured images. This approach is initiated by a Semi-Global dense Matching procedure is carried out to generate a 3D point cloud from the scanned area within the collected images. The generated point cloud is then segmented to extract individual planar surfaces. Finally, a novel region-based texturing technique is implemented for photorealistic reconstruction of the extracted planar surfaces. Experimental results using images collected by a camera mounted on a low-cost UAS demonstrate the feasibility of the proposed approach for photorealistic 3D surface reconstruction.

  6. Region-Based 3d Surface Reconstruction Using Images Acquired by Low-Cost Unmanned Aerial Systems

    Science.gov (United States)

    Lari, Z.; Al-Rawabdeh, A.; He, F.; Habib, A.; El-Sheimy, N.

    2015-08-01

    Accurate 3D surface reconstruction of our environment has become essential for an unlimited number of emerging applications. In the past few years, Unmanned Aerial Systems (UAS) are evolving as low-cost and flexible platforms for geospatial data collection that could meet the needs of aforementioned application and overcome limitations of traditional airborne and terrestrial mobile mapping systems. Due to their payload restrictions, these systems usually include consumer-grade imaging and positioning sensor which will negatively impact the quality of the collected geospatial data and reconstructed surfaces. Therefore, new surface reconstruction surfaces are needed to mitigate the impact of using low-cost sensors on the final products. To date, different approaches have been proposed to for 3D surface construction using overlapping images collected by imaging sensor mounted on moving platforms. In these approaches, 3D surfaces are mainly reconstructed based on dense matching techniques. However, generated 3D point clouds might not accurately represent the scanned surfaces due to point density variations and edge preservation problems. In order to resolve these problems, a new region-based 3D surface renostruction trchnique is introduced in this paper. This approach aims to generate a 3D photo-realistic model of individually scanned surfaces within the captured images. This approach is initiated by a Semi-Global dense Matching procedure is carried out to generate a 3D point cloud from the scanned area within the collected images. The generated point cloud is then segmented to extract individual planar surfaces. Finally, a novel region-based texturing technique is implemented for photorealistic reconstruction of the extracted planar surfaces. Experimental results using images collected by a camera mounted on a low-cost UAS demonstrate the feasibility of the proposed approach for photorealistic 3D surface reconstruction.

  7. Shadow imaging of geosynchronous satellites: simulation, image reconstruction, and shadow prediction

    Science.gov (United States)

    Douglas, Dennis M.; Hunt, Bobby R.; Sheppard, David G.

    2016-09-01

    Shadow imaging is a technique to obtain highly resolved silhouettes of resident space objects (RSOs) which would otherwise be unattainable using conventional terrestrial based imaging approaches. This is done by post processing the measured irradiance pattern (shadow) cast onto the Earth as the RSO occults a star. The research presented here focuses on shadow imaging of geosynchronous (GEO) satellites with near stationary orbits approximately 36,000 km from the Earth. Shadows pertaining to a set of diverse observing scenarios are simulated and used as inputs to a Fresnel based phase retrieval algorithm. Spatial resolution limits are evaluated and correlated to signal to noise (SNR) metrics. Resolvable feature sizes of less than 1 m are shown to be readily achievable using foreseeable observing scenarios. Initial output from a shadow prediction tool indicates that there are, on average, over 1000 shadows on the Earth on any given time from a single GEO satellite for stars brighter than mv=10. Shadow ground track uncertainties are correlated to stellar astrometric errors. Global and localized shadow track maps are presented demonstrating a high feasibility for future shadow collections.

  8. Aerial Image over Flint Hills National Wildlife Refuge, Acquired on March 22, 1950 (Frame 1656)

    Data.gov (United States)

    US Fish and Wildlife Service, Department of the Interior — Georeferenced image, acquired on March 22, 1950, over a portion of the Flint Hills National Wildlife Refuge. Image covers the eastern portion of the refuge including...

  9. Aerial Image over Ouray National Wildlife Refuge, Acquired on August 27, 1965 (Frame 131)

    Data.gov (United States)

    US Fish and Wildlife Service, Department of the Interior — Georeferenced image, acquired on August 27, 1965 over a portion of the Ouray National Wildlife Refuge. Image covers the northern portion of the refuge including...

  10. Aerial Image over Flint Hills National Wildlife Refuge, Acquired on April 14, 1948 (Frame 1155)

    Data.gov (United States)

    US Fish and Wildlife Service, Department of the Interior — Georeferenced image, acquired on April 14, 1948, over a portion of the Flint Hills National Wildlife Refuge. Image covers the eastern portion of the refuge including...

  11. Application of Technical Measures and Software in Constructing Photorealistic 3D Models of Historical Building Using Ground-Based and Aerial (UAV Digital Images

    Directory of Open Access Journals (Sweden)

    Zarnowski Aleksander

    2015-12-01

    Full Text Available Preparing digital documentation of historical buildings is a form of protecting cultural heritage. Recently there have been several intensive studies using non-metric digital images to construct realistic 3D models of historical buildings. Increasingly often, non-metric digital images are obtained with unmanned aerial vehicles (UAV. Technologies and methods of UAV flights are quite different from traditional photogrammetric approaches. The lack of technical guidelines for using drones inhibits the process of implementing new methods of data acquisition.

  12. Combining Human Computing and Machine Learning to Make Sense of Big (Aerial) Data for Disaster Response.

    Science.gov (United States)

    Ofli, Ferda; Meier, Patrick; Imran, Muhammad; Castillo, Carlos; Tuia, Devis; Rey, Nicolas; Briant, Julien; Millet, Pauline; Reinhard, Friedrich; Parkan, Matthew; Joost, Stéphane

    2016-03-01

    Aerial imagery captured via unmanned aerial vehicles (UAVs) is playing an increasingly important role in disaster response. Unlike satellite imagery, aerial imagery can be captured and processed within hours rather than days. In addition, the spatial resolution of aerial imagery is an order of magnitude higher than the imagery produced by the most sophisticated commercial satellites today. Both the United States Federal Emergency Management Agency (FEMA) and the European Commission's Joint Research Center (JRC) have noted that aerial imagery will inevitably present a big data challenge. The purpose of this article is to get ahead of this future challenge by proposing a hybrid crowdsourcing and real-time machine learning solution to rapidly process large volumes of aerial data for disaster response in a time-sensitive manner. Crowdsourcing can be used to annotate features of interest in aerial images (such as damaged shelters and roads blocked by debris). These human-annotated features can then be used to train a supervised machine learning system to learn to recognize such features in new unseen images. In this article, we describe how this hybrid solution for image analysis can be implemented as a module (i.e., Aerial Clicker) to extend an existing platform called Artificial Intelligence for Disaster Response (AIDR), which has already been deployed to classify microblog messages during disasters using its Text Clicker module and in response to Cyclone Pam, a category 5 cyclone that devastated Vanuatu in March 2015. The hybrid solution we present can be applied to both aerial and satellite imagery and has applications beyond disaster response such as wildlife protection, human rights, and archeological exploration. As a proof of concept, we recently piloted this solution using very high-resolution aerial photographs of a wildlife reserve in Namibia to support rangers with their wildlife conservation efforts (SAVMAP project, http://lasig.epfl.ch/savmap ). The

  13. Small Whiskbroom Imager for atmospheric compositioN monitorinG (SWING) from an Unmanned Aerial Vehicle (UAV): Results from the 2014 AROMAT campaign

    Science.gov (United States)

    Merlaud, Alexis; Tack, Frederik; Constantin, Daniel; Fayt, Caroline; Maes, Jeroen; Mingireanu, Florin; Mocanu, Ionut; Georgescu, Lucian; Van Roozendael, Michel

    2015-04-01

    The Small Whiskbroom Imager for atmospheric compositioN monitorinG (SWING) is an instrument dedicated to atmospheric trace gas retrieval from an Unmanned Aerial Vehicle (UAV). The payload is based on a compact visible spectrometer and a scanning mirror to collect scattered sunlight. Its weight, size, and power consumption are respectively 920 g, 27x12x12 cm3, and 6 W. The custom-built 2.5 m flying wing UAV is electrically powered, has a typical airspeed of 100 km/h, and can operate at a maximum altitude of 3 km. Both the payload and the UAV were developed in the framework of a collaboration between the Belgian Institute for Space Aeronomy (BIRA-IASB) and the Dunarea de Jos University of Galati, Romania. We present here SWING-UAV test flights dedicated to NO2 measurements and performed in Romania on 10 and 11 September 2014, during the Airborne ROmanian Measurements of Aerosols and Trace gases (AROMAT) campaign. The UAV performed 5 flights in the vicinity of the large thermal power station of Turceni (44.67° N, 23.4° E). The UAV was operated in visual range during the campaign, up to 900 m AGL , downwind of the plant and crossing its exhaust plume. The spectra recorded on flight are analyzed with the Differential Optical Absorption Spectroscopy (DOAS) method. The retrieved NO2 Differential Slant Column Densities (DSCDs) are up to 1.5e17 molec/cm2 and reveal the horizontal gradients around the plant. The DSCDs are converted to vertical columns and compared with coincident car-based DOAS measurements. We also present the near-future perspective of the SWING-UAV observation system, which includes flights in 2015 above the Black Sea to quantify ship emissions, the addition of SO2 as a target species, and autopilot flights at higher altitudes to cover a typical satellite pixel extent (10x10 km2).

  14. Satellite image blind restoration based on surface fitting and multivariate model

    Institute of Scientific and Technical Information of China (English)

    CHEN Xin-bing; YANG Shi-zhi; WANG Xian-hua; QIAO Yan-li

    2009-01-01

    Owing to the blurring effect from atmosphere and camera system in the satellite imaging a blind image restoration algo-rithm is proposed which includes the modulation transfer function (MTF) estimation and the image restoration. In the MTF estimation stage, based on every degradation process of satellite imaging-chain, a combined parametric model of MTF is given and used to fit the surface of normalized logarithmic amplitude spectrum of degraded image. In the image restoration stage, a maximum a posteriori (MAP) based edge-preserving image restoration method is presented which introduces multivariate Laplacian model to characterize the prior distribution of wavelet coefficients of original image. During the image restoration, in order to avoid solving high nonlinear equations, optimization transfer algorithm is adopted to decom-pose the image restoration procedure into two simple steps: Landweber iteration and wavelet thresholding denoising. In the numerical experiment, the satellite image restoration results from SPOT-5 and high resolution camera (HR) of China & Brazil earth resource satellite (CBERS-02B) ane compared, and the proposed algorithm is superior in the image edge preservation and noise inhibition.

  15. A method of using commercial virtual satellite image to check the pattern painting spot effect

    Science.gov (United States)

    Wang, Zheng-gang; Kang, Qing; Shen, Zhi-qiang; Cui, Chang-bin

    2014-02-01

    A method of using commercial virtual satellite image to check the pattern painting spot effect contrast with the satellite images before painting and after painting have been discussed. Using a housetop as the testing platform analyses and discusses the factors' influence such as resolution of satellite image, spot size and color of pattern painting spot and pattern painting camouflage method choosing to the plan implement. The pattern painting design and spot size used in the testing has been ensured, and housetop pattern painting has been painted. Finally, the small spot pattern painting camouflage effect of engineering using upon painting pattern size, color and texture have been checked, contrasting with the satellite image before painting and after painting.

  16. A study on quality and availability of COCTS images of HY- 1 satellite by simulation

    Institute of Scientific and Technical Information of China (English)

    李淑菁; 毛天明; 潘德炉

    2002-01-01

    Hy-1 is a first China's ocean color satellite which will be launched as a piggyback satellite on FY- 1 satellite using Long March rocket. On the satellite there are two sensors: one is the China's ocean color and temperature scanner (COCTS), the other is CCD coastal zone imager (CZI).The COCTS is considered to be a main sensor to play a key role. In order to understand the characteristics of future ocean color images observed, a simulation and evaluation study on the quality and availability of the COCTS image has been done. First, the simulation models are introduced briefly, and typical simulated cases of radiance images at visible bands are introduced, in which the radiance distribution is based on geographic location, the satellite orbital parameters and sensor properties, the simulated method to evaluate the image quality and availability is developed by using the characteristics of image called the complex signal noise ratio ( CSNR ). Meanwhile, a series of the CSNR images are generated from the simulated radiance components for different cases, which can be used to evaluate the quality and availability of the COCTS images before the HY - 1 is placed in orbit. Finally, the quality and availability of the COCTS images are quantitatively analyzed with the simulated CSNR data. The results will be beneficial to all scientists who are in charge of the COCTS mission and to those who plan to use the data from the COCTS.

  17. Research on UAV aerial image registration algorithm%无人机航拍图像配准算法研究

    Institute of Scientific and Technical Information of China (English)

    史俊莉

    2015-01-01

    针对无人机航拍图像对尺度变化不明显的问题,在经典SIFT特征匹配算法的基础上,提出了一种改进的CS-SIFT特征匹配算法.该算法通过建立S层金字塔,达到降低多尺度空间和减少特征点数量的目的.在特征向量的匹配中,利用准欧氏距离替代常用欧氏距离,并通过极限几何约束,消除部分错误配准点对,进一步提高特征匹配效率.Matlab仿真结果表明,改进后的算法具有较高的匹配精度和较少的匹配时间,适用于对实时性要求较高的无人机航拍系统.%Aiming at the problem that UAV aerial image was not obvious to scale change,an improved CS-SIFT feature matching algorithm was presented based on the classical feature matching algorithm of SIFT (scale invariant feature transform ).The algorithm established S-layer pyramid to reduce the multi-scale space and the number of feature points.In the matching feature vectors,quasi-Euclidean distance substitu-ted commonly used Euclidean distance and geometric constraints limitd by eliminating part of the registra-tion error points to further improve the efficiency of feature matching.The simulation results using Matlab language indicated that the improved algorithm had higher matching accuracy and needed less matching time and it was quite suitable for the UAV aerial system of high real-time demand.

  18. Feature Extraction in Sequential Multimedia Images: with Applications in Satellite Images and On-line Videos

    Science.gov (United States)

    Liang, Yu-Li

    Multimedia data is increasingly important in scientific discovery and people's daily lives. Content of massive multimedia is often diverse and noisy, and motion between frames is sometimes crucial in analyzing those data. Among all, still images and videos are commonly used formats. Images are compact in size but do not contain motion information. Videos record motion but are sometimes too big to be analyzed. Sequential images, which are a set of continuous images with low frame rate, stand out because they are smaller than videos and still maintain motion information. This thesis investigates features in different types of noisy sequential images, and the proposed solutions that intelligently combined multiple features to successfully retrieve visual information from on-line videos and cloudy satellite images. The first task is detecting supraglacial lakes above ice sheet in sequential satellite images. The dynamics of supraglacial lakes on the Greenland ice sheet deeply affect glacier movement, which is directly related to sea level rise and global environment change. Detecting lakes above ice is suffering from diverse image qualities and unexpected clouds. A new method is proposed to efficiently extract prominent lake candidates with irregular shapes, heterogeneous backgrounds, and in cloudy images. The proposed system fully automatize the procedure that track lakes with high accuracy. We further cooperated with geoscientists to examine the tracked lakes and found new scientific findings. The second one is detecting obscene content in on-line video chat services, such as Chatroulette, that randomly match pairs of users in video chat sessions. A big problem encountered in such systems is the presence of flashers and obscene content. Because of various obscene content and unstable qualities of videos capture by home web-camera, detecting misbehaving users is a highly challenging task. We propose SafeVchat, which is the first solution that achieves satisfactory

  19. NOAA Geostationary Operational Environmental Satellite (GOES) Imager Data

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The NOAA Geostationary Operational Environmental Satellite (GOES) series provides continuous measurements of the atmosphere and surface over the Western Hemisphere....

  20. NEPR World View 2 Satellite Mosaic - NOAA TIFF Image

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This GeoTiff is a mosaic of World View 2 panchromatic satellite imagery of Northeast Puerto Rico that contains the shallow water area (0-35m deep) surrounding...

  1. Experimental analysis on classification of unmanned aerial vehicle images using the probabilistic latent semantic analysis

    Science.gov (United States)

    Yi, Wenbin; Tang, Hong

    2009-10-01

    In this paper, we present a novel algorithm to classify UAV images through the image annotation which is a semi-supervised method. During the annotation process, we first divide whole image into different sizes of blocks and generate suitable visual words which are the K-means clustering centers or just pixels in small size image block. Then, given a set of image blocks for each semantic concept as training data, learning is based on the Probabilistic Latent Semantic Analysis (PLSA). The probability distributions of visual words in every document can be learned through the PLSA model. The labeling of every document (image block) is done by computing the similarity of its feature distribution to the distribution of the training documents with the Kullback-Leibler (K-L) divergence. Finally, the classification of the UAV images will be done by combining all the image blocks in every block size. The UAV images using in our experiments was acquired during Sichuan earthquake in 2008. The results show that smaller size block image will get better classification results.

  2. ARCHIMEDES (Aerial Robot Carrying High-resolution Imaging, a Magnetometric Experiment and Direct Environmental Sensing instruments)

    Science.gov (United States)

    Griebel, Hannes S.

    2003-11-01

    ARCHIMEDES is a small interplanetary space mission with the goal to deploy a balloon within the atmosphere of planet Mars. It is a super pressure balloon equipped with a high resolution planetary camera provided by DLR Berlin, a magnetometer provided by TU Braunschweig and a meteorology package provided by the Finnish Meteorological Institute. These sensors are suspended beneath a 14 m diameter balloon in a small gondola of 4 kg. The aerobot itself has a mass of 16 kg and floats at approximately 2 km altitude. Eventually, the aerial platform itself becomes a scientific experiment owing to the fact that it represents a test particle that can be traced to investigate atmospheric movements on a planetary scale. The aerobot is suggested to AMSAT for transportation out to Mars on board their P5-A Mars mission, slated to launch in 2007 or 2009. Once in orbit around the planet, the ARCHIMEDES spacecraft would be released and autonomously de-orbit itself. AMSAT P5-A would also provide for a data communication link with planet earth, and track the aerobot by means of radio ranging.

  3. An Image-Based Sensor System for Autonomous Rendez-Vous with Uncooperative Satellites

    CERN Document Server

    Miravet, Carlos; Krouch, Eloise; del Cura, Juan Manuel

    2008-01-01

    In this paper are described the image processing algorithms developed by SENER, Ingenieria y Sistemas to cope with the problem of image-based, autonomous rendez-vous (RV) with an orbiting satellite. The methods developed have a direct application in the OLEV (Orbital Life Extension Extension Vehicle) mission. OLEV is a commercial mission under development by a consortium formed by Swedish Space Corporation, Kayser-Threde and SENER, aimed to extend the operational life of geostationary telecommunication satellites by supplying them control, navigation and guidance services. OLEV is planned to use a set of cameras to determine the angular position and distance to the client satellite during the complete phases of rendez-vous and docking, thus enabling the operation with satellites not equipped with any specific navigational aid to provide support during the approach. The ability to operate with un-equipped client satellites significantly expands the range of applicability of the system under development, compar...

  4. SEGMENTATION AND CLASSIFICATION OF POINT CLOUDS FROM DENSE AERIAL IMAGE MATCHING

    Directory of Open Access Journals (Sweden)

    Mohammad Omidalizarandi

    2013-08-01

    Full Text Available In the recent years, 3D city reconstruction is one of the active researches in the field of photogrammetry. The goal of this work is to improve and extend surface growing based segmentation in the X-Y-Z image in the form of 3D structured data with combination of spectral information of RGB and grayscale image to extract building roofs, streets and vegetation. In order to process 3D point clouds, hybrid segmentation is carried out in both object space and image space. Our experiments on three case studies verify that updating plane parameters and robust least squares plane fitting improves the results of building extraction especially in case of low accurate point clouds. In addition, region growing in image space has been derived to the fact that grayscale image is more flexible than RGB image and results in more realistic building roofs.

  5. Aerial Photography and Imagery, Ortho-Corrected, 2004 Satellite Imagery, Published in 2004, 1:12000 (1in=1000ft) scale, Anne Arundel County, OIT GIS.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:12000 (1in=1000ft) scale, was produced all or in part from Orthoimagery information as...

  6. Cassini Imaging of Auroral Emissions on the Galilean Satellites

    Science.gov (United States)

    Geissler, P.; McEwen, A.; Porco, C.

    2001-05-01

    Cassini captured several sequences of images showing Io, Europa and Ganymede while the moons were eclipsed by Jupiter. Io was the best studied of the satellites, with 4 eclipses successfully recorded. Earlier eclipse imaging by Galileo (Geissler et al., Science 295, 870-874) had shown colorful atmospheric emissions from Io and raised questions concerning their temporal variability and the identity of the emitting species. With its high data rate and numerous filter combinations, Cassini was able to fill some of the gaps in our knowledge of Io's visible aurorae. Io's bright equatorial glows were detected at previously unknown wavelengths and were also seen in motion. One eclipse took place on 12/29/2000 while Io was far from the plasma torus center. The pair of equatorial glows near the sub-Jupiter and anti-Jupiter points appeared about equal in brightness and changed little in location or intensity over a two hour period. Io crossed the plasma torus center during the next eclipse on 1/01/2001, as it passed through System III magnetic longitudes from 250 to 303 degrees. The equatorial glows were seen to shift in latitude during this eclipse, tracking the tangent points of the jovian magnetic field lines. This behaviour is similar to that observed for ultraviolet and other atomic emissions, and confirms that these visible glows are powered by Birkeland currents connecting Io and Jupiter. The eclipse on 1/05/2001 provided the best spectral measurements of the aurorae. The equatorial glows were detected at near ultraviolet wavelengths, consistent with their interpretation as molecular SO2 emissions. More than 100 kR were recorded in the ISS UV3 filter (300-380 nm) along with a similar intensity in BL1 (290-500 nm), comparable to Galileo estimates. At least 50 kR were detected in UV2 images (265-330 nm). No detection was made in UV1 (235-280 nm), allowing us to place an upper limit of about 100 kR. A new detection of the equatorial glows was made in the IR1 band (670

  7. MATCHING AERIAL IMAGES TO 3D BUILDING MODELS BASED ON CONTEXT-BASED GEOMETRIC HASHING

    Directory of Open Access Journals (Sweden)

    J. Jung

    2016-06-01

    Full Text Available In this paper, a new model-to-image framework to automatically align a single airborne image with existing 3D building models using geometric hashing is proposed. As a prerequisite process for various applications such as data fusion, object tracking, change detection and texture mapping, the proposed registration method is used for determining accurate exterior orientation parameters (EOPs of a single image. This model-to-image matching process consists of three steps: 1 feature extraction, 2 similarity measure and matching, and 3 adjustment of EOPs of a single image. For feature extraction, we proposed two types of matching cues, edged corner points representing the saliency of building corner points with associated edges and contextual relations among the edged corner points within an individual roof. These matching features are extracted from both 3D building and a single airborne image. A set of matched corners are found with given proximity measure through geometric hashing and optimal matches are then finally determined by maximizing the matching cost encoding contextual similarity between matching candidates. Final matched corners are used for adjusting EOPs of the single airborne image by the least square method based on co-linearity equations. The result shows that acceptable accuracy of single image's EOP can be achievable by the proposed registration approach as an alternative to labour-intensive manual registration process.

  8. Matching Aerial Images to 3d Building Models Based on Context-Based Geometric Hashing

    Science.gov (United States)

    Jung, J.; Bang, K.; Sohn, G.; Armenakis, C.

    2016-06-01

    In this paper, a new model-to-image framework to automatically align a single airborne image with existing 3D building models using geometric hashing is proposed. As a prerequisite process for various applications such as data fusion, object tracking, change detection and texture mapping, the proposed registration method is used for determining accurate exterior orientation parameters (EOPs) of a single image. This model-to-image matching process consists of three steps: 1) feature extraction, 2) similarity measure and matching, and 3) adjustment of EOPs of a single image. For feature extraction, we proposed two types of matching cues, edged corner points representing the saliency of building corner points with associated edges and contextual relations among the edged corner points within an individual roof. These matching features are extracted from both 3D building and a single airborne image. A set of matched corners are found with given proximity measure through geometric hashing and optimal matches are then finally determined by maximizing the matching cost encoding contextual similarity between matching candidates. Final matched corners are used for adjusting EOPs of the single airborne image by the least square method based on co-linearity equations. The result shows that acceptable accuracy of single image's EOP can be achievable by the proposed registration approach as an alternative to labour-intensive manual registration process.

  9. Use of Aerial Images for Regular Updates of Buildings in the Fundamental Base of Geographic Data of the Czech Republic

    Science.gov (United States)

    Hron, V.; Halounova, L.

    2015-03-01

    Digital aerial images (DAI) include position, elevation and also spectral information (visible bands and near-infrared band) about the captured area. The aim of this paper is to present the possibilities of automatic analysis of DAI for updating of the Fundamental Base of Geographic Data of the Czech Republic with a focus on buildings. Regular updates of buildings (automatic detection of new and demolished buildings) are based on the analysis of coloured point clouds created by an automatic image matching technique from each time period. The created approach compares point clouds from different time periods to each other. The advantage of this solution is that it is independent of the manner of keeping the buildings in the database. It does not matter whether the buildings in the database have correct positions and their footprints correspond to the roof shapes or external walls. The involved method is robust because a digital surface model generated by image matching techniques can contain numerous errors. Shaded areas and objects with blurred textures are problematic for automatic image correlation algorithms and lead to false results. For this reason, derived layers containing additional information are used. Shadow masks (layers with modelled shadows) are used for the verification of indications and to filter out errors in the shaded areas using a contextual evaluation. Furthermore, additional information about the road and railway networks and morphological operations of opening and closing were used to achieve more accurate results. All these information sources are then evaluated using decision logic, which uses the generally applicable rules that are available for different datasets without the need for modification. The method was tested on different datasets with various types of buildings (villages, suburbs and city centres) which cover more than 20 square kilometres. The developed solution leads to very promising results without the need of acquiring

  10. Multi-Mode GF-3 Satellite Image Geometric Accuracy Verification Using the RPC Model.

    Science.gov (United States)

    Wang, Taoyang; Zhang, Guo; Yu, Lei; Zhao, Ruishan; Deng, Mingjun; Xu, Kai

    2017-09-01

    The GaoFen-3 (GF-3) satellite is the first C-band multi-polarization synthetic aperture radar (SAR) imaging satellite with a resolution up to 1 m in China. It is also the only SAR satellite of the High-Resolution Earth Observation System designed for civilian use. There are 12 different imaging models to meet the needs of different industry users. However, to use SAR satellite images for related applications, they must possess high geometric accuracy. In order to verify the geometric accuracy achieved by the different modes of GF-3 images, we analyze the SAR geometric error source and perform geometric correction tests based on the RPC model with and without ground control points (GCPs) for five imaging modes. These include the spotlight (SL), ultra-fine strip (UFS), Fine Strip I (FSI), Full polarized Strip I (QPSI), and standard strip (SS) modes. Experimental results show that the check point residuals are large and consistent without GCPs, but the root mean square error of the independent checkpoints for the case of four corner control points is better than 1.5 pixels, achieving a similar level of geometric positioning accuracy to that of international satellites. We conclude that the GF-3 satellite can be used for high-accuracy geometric processing and related industry applications.

  11. Satellite Image Edge Detection for Population Distribution Pattern Identification using Levelset with Morphological Filtering Process

    Science.gov (United States)

    Harsiti; Munandar, T. A.; Suhendar, A.; Abdullah, A. G.; Rohendi, D.

    2017-03-01

    Population distribution pattern is directly related with economic gap of a region. Analysis of population distribution pattern is usually performed by studying statistical data on population. This study aimed to analyze population distribution pattern using image analysis concept, i.e. using satellite images. Levelset and morphological image filtering methods were used to analyze images to see distribution pattern. The research result showed that Levelset and morphological image filtering could remove a lot of noises in analysis result images and form object edge contours very clearly. The detected object contours were used as references to recognize population distribution pattern based on satellite image analysis. The pattern made based on the research result didn’t show optimal result because Levelset performed image segmentation based on the contours of the analyzed objects. Other segmentation methods should be combined with it to produce clearer population distribution pattern.

  12. Fast Aerial Video Stitching

    Directory of Open Access Journals (Sweden)

    Jing Li

    2014-10-01

    Full Text Available The highly efficient and robust stitching of aerial video captured by unmanned aerial vehicles (UAVs is a challenging problem in the field of robot vision. Existing commercial image stitching systems have seen success with offline stitching tasks, but they cannot guarantee high-speed performance when dealing with online aerial video sequences.In this paper, we present a novel system which has an unique ability to stitch high-frame rate aerial video at a speed of 150 frames per second (FPS. In addition, rather than using a high-speed vision platform such as FPGA or CUDA, our system is running on a normal personal computer. To achieve this, after the careful comparison of the existing invariant features, we choose the FAST corner and binary descriptor for efficient feature extraction and representation, and present a spatial and temporal coherent filter to fuse the UAV motion information into the feature matching. The proposed filter can remove the majority of feature correspondence outliers and significantly increase the speed of robust feature matching by up to 20 times. To achieve a balance between robustness and efficiency, a dynamic key frame-based stitching framework is used to reduce the accumulation errors.Extensive experiments on challenging UAV datasets demonstrate that our approach can break through the speed limitation and generate an accurate stitching image for aerial video stitching tasks.

  13. Pantir - a Dual Camera Setup for Precise Georeferencing and Mosaicing of Thermal Aerial Images

    Science.gov (United States)

    Weber, I.; Jenal, A.; Kneer, C.; Bongartz, J.

    2015-03-01

    Research and monitoring in fields like hydrology and agriculture are applications of airborne thermal infrared (TIR) cameras, which suffer from low spatial resolution and low quality lenses. Common ground control points (GCPs), lacking thermal activity and being relatively small in size, cannot be used in TIR images. Precise georeferencing and mosaicing however is necessary for data analysis. Adding a high resolution visible light camera (VIS) with a high quality lens very close to the TIR camera, in the same stabilized rig, allows us to do accurate geoprocessing with standard GCPs after fusing both images (VIS+TIR) using standard image registration methods.

  14. Autonomous Sub-Pixel Satellite Track Endpoint Determination for Space Based Images

    Energy Technology Data Exchange (ETDEWEB)

    Simms, L M

    2011-03-07

    An algorithm for determining satellite track endpoints with sub-pixel resolution in spaced-based images is presented. The algorithm allows for significant curvature in the imaged track due to rotation of the spacecraft capturing the image. The motivation behind the subpixel endpoint determination is first presented, followed by a description of the methodology used. Results from running the algorithm on real ground-based and simulated spaced-based images are shown to highlight its effectiveness.

  15. Three attempts of earthquake prediction with satellite cloud images

    Directory of Open Access Journals (Sweden)

    G. Guangmeng

    2013-01-01

    Full Text Available Thermal anomalies detected from satellite data are widely reported. Nearly all the anomalies are reported after the quake. Here we report three earthquake predictions in Italy and Iran according to satellite cloud anomalies. These cloud anomalies usually show a linear pattern, stay there for hours and do not move with winds. According to these anomalies, we can give a rough estimation about impending earthquake activities. All the estimated dates and magnitudes are in good agreement with the earthquake facts, and the only unsatisfactory point is that the distance error is 100–300 km. Because the cloud anomaly is long, we can not reduce the distance error further. A possible way is to combine geophysical data and satellite data together to estimate the epicenter and this will increase the prediction accuracy.

  16. Series of aerial images over Marais des Cygnes National Wildlife Refuge, acquired in 1950

    Data.gov (United States)

    US Fish and Wildlife Service, Department of the Interior — This dataset includes 10 georeferenced images, acquired on July 13, 1950 over portions of Marais des Cygnes National Refuge in eastern Kansas. This data set is a...

  17. Series of aerial images over Marais des Cygnes National Wildlife Refuge, acquired in 1957

    Data.gov (United States)

    US Fish and Wildlife Service, Department of the Interior — This dataset includes 8 georeferenced images, acquired on May 5th, 6th and 26th, 1957 over portions of Marais des Cygnes National Refuge in eastern Kansas. This data...

  18. Series of aerial images over Bear River Migratory Bird Refuge, acquired in 1937

    Data.gov (United States)

    US Fish and Wildlife Service, Department of the Interior — This dataset includes 40 georeferenced images, acquired on September 25th, October 12th-13th, November 10th and December 1st, 1937 over portions of Bear River...

  19. Series of aerial images over Quivira National Wildlife Refuge, acquired September, 1950

    Data.gov (United States)

    US Fish and Wildlife Service, Department of the Interior — This data set is composite of original black and white series images obtained from Earth Explorer (USGS) on September 23rd, 1950. The original photos were...

  20. The influence of topographic structures on night-time surface temperatures: Evaluation of a satellite thermal image of the upper Rhine plain and the surrounding highlands. [Germany and Switzerland

    Science.gov (United States)

    Gossmann, H. (Principal Investigator)

    1980-01-01

    The author has identified the following significant results. Satellite data supplied the same information as aerial IR registrations with corresponding averaging for all studies requiring a survey of the thermal pattern within an area measuring 10 km x 10 km ore more, provided that sufficiently precise control points could be established for the purpose of geometric rectification in the surroundings of the area observed. Satellite thermal data are more comprehensive than aircraft data for studies on a regional, rather than a local scale, since airborne images often obscure the basic correlation in thermal patterns because of a variety of irrelevant topographical detail. The satellite data demonstrate the dependence of surface temperature on relief more clearly than comparable airborne imagery.

  1. Multiscale assessment of green leaf area in a semi-arid rangeland with a small unmanned aerial vehicle

    Science.gov (United States)

    Spatial variability in green leaf cover of a western rangeland was studied by comparing field measurements on 50 m crossed transects to aerial and satellite imagery. The normalized difference vegetation index was calculated for multiple 2 cm resolution images collected over the field transects with ...

  2. Wildfire monitoring using satellite images, ontologies and linked geospatial data

    NARCIS (Netherlands)

    Kyzirakos, K.; Karpathiotakis, M.; Garbis, G.; Nikolaou, C.; Bereta, K.; Papoutsis, I.; Herekakis, T.; Michail, D.; Koubarakis, M.; Kontoes, C.

    2014-01-01

    Advances in remote sensing technologies have allowed us to send an ever-increasing number of satellites in orbit around Earth. As a result, Earth Observation data archives have been constantly increasing in size in the last few years, and have become a valuable source of data for many scientific and

  3. Remote sensing place : Satellite images as visual spatial imaginaries

    NARCIS (Netherlands)

    Shim, David

    How do people come to know the world? How do they get a sense of place and space? Arguably, one of the ways in which they do this is through the practice of remote sensing, among which satellite imagery is one of the most widespread and potent tools of engaging, representing and constructing space.

  4. Provisional maps of thermal areas in Yellowstone National Park, based on satellite thermal infrared imaging and field observations

    Science.gov (United States)

    Vaughan, R. Greg; Heasler, Henry; Jaworowski, Cheryl; Lowenstern, Jacob B.; Keszthelyi, Laszlo P.

    2014-01-01

    Maps that define the current distribution of geothermally heated ground are useful toward setting a baseline for thermal activity to better detect and understand future anomalous hydrothermal and (or) volcanic activity. Monitoring changes in the dynamic thermal areas also supports decisions regarding the development of Yellowstone National Park infrastructure, preservation and protection of park resources, and ensuring visitor safety. Because of the challenges associated with field-based monitoring of a large, complex geothermal system that is spread out over a large and remote area, satellite-based thermal infrared images from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) were used to map the location and spatial extent of active thermal areas, to generate thermal anomaly maps, and to quantify the radiative component of the total geothermal heat flux. ASTER thermal infrared data acquired during winter nights were used to minimize the contribution of solar heating of the surface. The ASTER thermal infrared mapping results were compared to maps of thermal areas based on field investigations and high-resolution aerial photos. Field validation of the ASTER thermal mapping is an ongoing task. The purpose of this report is to make available ASTER-based maps of Yellowstone’s thermal areas. We include an appendix containing the names and characteristics of Yellowstone’s thermal areas, georeferenced TIFF files containing ASTER thermal imagery, and several spatial data sets in Esri shapefile format.

  5. Matching Aerial Images to 3D Building Models Using Context-Based Geometric Hashing.

    Science.gov (United States)

    Jung, Jaewook; Sohn, Gunho; Bang, Kiin; Wichmann, Andreas; Armenakis, Costas; Kada, Martin

    2016-06-22

    A city is a dynamic entity, which environment is continuously changing over time. Accordingly, its virtual city models also need to be regularly updated to support accurate model-based decisions for various applications, including urban planning, emergency response and autonomous navigation. A concept of continuous city modeling is to progressively reconstruct city models by accommodating their changes recognized in spatio-temporal domain, while preserving unchanged structures. A first critical step for continuous city modeling is to coherently register remotely sensed data taken at different epochs with existing building models. This paper presents a new model-to-image registration method using a context-based geometric hashing (CGH) method to align a single image with existing 3D building models. This model-to-image registration process consists of three steps: (1) feature extraction; (2) similarity measure; and matching, and (3) estimating exterior orientation parameters (EOPs) of a single image. For feature extraction, we propose two types of matching cues: edged corner features representing the saliency of building corner points with associated edges, and contextual relations among the edged corner features within an individual roof. A set of matched corners are found with given proximity measure through geometric hashing, and optimal matches are then finally determined by maximizing the matching cost encoding contextual similarity between matching candidates. Final matched corners are used for adjusting EOPs of the single airborne image by the least square method based on collinearity equations. The result shows that acceptable accuracy of EOPs of a single image can be achievable using the proposed registration approach as an alternative to a labor-intensive manual registration process.

  6. Matching Aerial Images to 3D Building Models Using Context-Based Geometric Hashing

    Directory of Open Access Journals (Sweden)

    Jaewook Jung

    2016-06-01

    Full Text Available A city is a dynamic entity, which environment is continuously changing over time. Accordingly, its virtual city models also need to be regularly updated to support accurate model-based decisions for various applications, including urban planning, emergency response and autonomous navigation. A concept of continuous city modeling is to progressively reconstruct city models by accommodating their changes recognized in spatio-temporal domain, while preserving unchanged structures. A first critical step for continuous city modeling is to coherently register remotely sensed data taken at different epochs with existing building models. This paper presents a new model-to-image registration method using a context-based geometric hashing (CGH method to align a single image with existing 3D building models. This model-to-image registration process consists of three steps: (1 feature extraction; (2 similarity measure; and matching, and (3 estimating exterior orientation parameters (EOPs of a single image. For feature extraction, we propose two types of matching cues: edged corner features representing the saliency of building corner points with associated edges, and contextual relations among the edged corner features within an individual roof. A set of matched corners are found with given proximity measure through geometric hashing, and optimal matches are then finally determined by maximizing the matching cost encoding contextual similarity between matching candidates. Final matched corners are used for adjusting EOPs of the single airborne image by the least square method based on collinearity equations. The result shows that acceptable accuracy of EOPs of a single image can be achievable using the proposed registration approach as an alternative to a labor-intensive manual registration process.

  7. Independent transect point locations (coordinates only) associated with images collected during unmanned aerial systems (UAS) flights over Coast Guard Beach, Nauset Spit, Nauset Inlet, and Nauset Marsh, Cape Cod National Seashore, Eastham, Massachusetts on 1 March 2016 (Text file)

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — This dataset contains the locations of independent survey points acquired on the same day that images were obtained from unmanned aerial systems (UAS) flown in the...

  8. Tables of file names, times, and locations of images collected during unmanned aerial systems (UAS) flights over Coast Guard Beach, Nauset Spit, Nauset Inlet, and Nauset Marsh, Cape Cod National Seashore, Eastham, Massachusetts on 1 March 2016 (text files)

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — These text files contain tables of the file names, times, and locations of images obtained from an unmanned aerial systems (UAS) flown in the Cape Cod National...

  9. Ground control point locations associated with images collected during unmanned aerial systems (UAS) flights over Coast Guard Beach, Nauset Spit, Nauset Inlet, and Nauset Marsh, Cape Cod National Seashore, Eastham, Massachusetts on 1 March 2016 (Text file and photos)

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — This dataset documents the locations of ground control points associated with images obtained from unmanned aerial systems (UAS) flown in the Cape Cod National...

  10. Tables of file names, times, and locations of images collected during unmanned aerial systems (UAS) flights over Coast Guard Beach, Nauset Spit, Nauset Inlet, and Nauset Marsh, Cape Cod National Seashore, Eastham, Massachusetts on 1 March 2016 (text files)

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — These text files contain tables of the file names, times, and locations of images obtained from an unmanned aerial systems (UAS) flown in the Cape Cod National...

  11. Independent transect point locations (coordinates only) associated with images collected during unmanned aerial systems (UAS) flights over Coast Guard Beach, Nauset Spit, Nauset Inlet, and Nauset Marsh, Cape Cod National Seashore, Eastham, Massachusetts on 1 March 2016 (Text file)

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — This dataset contains the locations of independent survey points acquired on the same day that images were obtained from unmanned aerial systems (UAS) flown in the...

  12. Ground control point locations associated with images collected during unmanned aerial systems (UAS) flights over Coast Guard Beach, Nauset Spit, Nauset Inlet, and Nauset Marsh, Cape Cod National Seashore, Eastham, Massachusetts on 1 March 2016 (Text file and photos)

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — This dataset documents the locations of ground control points associated with images obtained from unmanned aerial systems (UAS) flown in the Cape Cod National...

  13. Application of Technical Measures and Software in Constructing Photorealistic 3D Models of Historical Building Using Ground-Based and Aerial (UAV) Digital Images

    Science.gov (United States)

    Zarnowski, Aleksander; Banaszek, Anna; Banaszek, Sebastian

    2015-12-01

    Preparing digital documentation of historical buildings is a form of protecting cultural heritage. Recently there have been several intensive studies using non-metric digital images to construct realistic 3D models of historical buildings. Increasingly often, non-metric digital images are obtained with unmanned aerial vehicles (UAV). Technologies and methods of UAV flights are quite different from traditional photogrammetric approaches. The lack of technical guidelines for using drones inhibits the process of implementing new methods of data acquisition. This paper presents the results of experiments in the use of digital images in the construction of photo-realistic 3D model of a historical building (Raphaelsohns' Sawmill in Olsztyn). The aim of the study at the first stage was to determine the meteorological and technical conditions for the acquisition of aerial and ground-based photographs. At the next stage, the technology of 3D modelling was developed using only ground-based or only aerial non-metric digital images. At the last stage of the study, an experiment was conducted to assess the possibility of 3D modelling with the comprehensive use of aerial (UAV) and ground-based digital photographs in terms of their labour intensity and precision of development. Data integration and automatic photo-realistic 3D construction of the models was done with Pix4Dmapper and Agisoft PhotoScan software Analyses have shown that when certain parameters established in an experiment are kept, the process of developing the stock-taking documentation for a historical building moves from the standards of analogue to digital technology with considerably reduced cost.

  14. Urban vehicle detection in high-resolution aerial images via superpixel segmentation and correlation-based sequential dictionary learning

    Science.gov (United States)

    Zhang, Xunxun; Xu, Hongke; Fang, Jianwu; Sheng, Gang

    2017-04-01

    Vehicle detection in high-resolution aerial images has received widespread interests when it comes to providing the required information for traffic management and urban planning. It is challenging due to the relatively small size of the vehicles and the complex background. Furthermore, it is particularly challenging if the higher detection efficiency is required. Therefore, an urban vehicle detection algorithm is proposed via improved entropy rate clustering (IERC) and correlation-based sequential dictionary learning (CSDL). First, to enhance the detection accuracy, IERC is designed to generate more regular superpixels. It aims to avoid the situation that one superpixel sometimes straddles multiple vehicles. The generated superpixels are then treated as the seeds for the training sample selection. Then, CSDL is constructed to achieve a fast sequential training and updating of the dictionary. In CSDL, only the atoms correlated with the sparse representation of the new training data are inferred. Finally, comprehensive analyses and comparisons on two data sets demonstrate that the proposed method generates satisfactory and competitive results.

  15. An Integrated Photogrammetric and Spatial Database Management System for Producing Fully Structured Data Using Aerial and Remote Sensing Images

    Directory of Open Access Journals (Sweden)

    Farshid Farnood Ahmadi

    2009-03-01

    Full Text Available 3D spatial data acquired from aerial and remote sensing images by photogrammetric techniques is one of the most accurate and economic data sources for GIS, map production, and spatial data updating. However, there are still many problems concerning storage, structuring and appropriate management of spatial data obtained using these techniques. According to the capabilities of spatial database management systems (SDBMSs; direct integration of photogrammetric and spatial database management systems can save time and cost of producing and updating digital maps. This integration is accomplished by replacing digital maps with a single spatial database. Applying spatial databases overcomes the problem of managing spatial and attributes data in a coupled approach. This management approach is one of the main problems in GISs for using map products of photogrammetric workstations. Also by the means of these integrated systems, providing structured spatial data, based on OGC (Open GIS Consortium standards and topological relations between different feature classes, is possible at the time of feature digitizing process. In this paper, the integration of photogrammetric systems and SDBMSs is evaluated. Then, different levels of integration are described. Finally design, implementation and test of a software package called Integrated Photogrammetric and Oracle Spatial Systems (IPOSS is presented.

  16. Monitoring agricultural crop growth: comparison of high spatial-temporal satellite imagery versus UAV-based imaging spectrometer time series measurements

    Science.gov (United States)

    Mucher, Sander; Roerink, Gerbert; Franke, Jappe; Suomalainen, Juha; Kooistra, Lammert

    2014-05-01

    In 2012, the Dutch National Satellite Data Portal (NSD) was launched as a preparation to the launch of the European SENTINEL satellites in the framework of the Copernicus Programme. At the same time the Unmanned Aerial Remote Sensing Facility (UARSF: www.wageningenUR.nl/uarsf) has been established as research facility at Wageningen University and Research Centre. The NSD became available for the development of services and advice through an investment from the Dutch government in collaboration with the Netherlands Space Office (NSO) in order to develop new services for precision agriculture. The NSD contains Formosat, Radarsat as well as DMC satellite imagery. The processing of the DMC imagery resulted in the Greenmonitor service (www.groenmonitor.nl). The Greenmonitor is an unique product that covers the Netherlands with a high spatial and temporal resolution. The Greenmonitor is now being exploited for various applications, amongst others crop identification, crop phenology, and identification of management activities. The UARSF of Wageningen UR has three objectives: 1) to develop innovation in the field of remote sensing science using Unmanned Aerial Vehicles (UAV) by providing a platform for dedicated and high-quality experiments; 2) to support high quality UAV services by providing calibration facilities and disseminating processing procedures to the UAV user community; 3) to promote and test the use of UAV in a broad range of application fields such as precision agriculture and habitat monitoring. Through this coincidence of new developments the goal of our study was to compare the information for the measurements of spatial variation in crops and soils as derived from high spatial-temporal satellite imagery from the national data portal compared to the exploitation of UAVs, in our case an Altura octocopter with a hyperspectral camera. As such, the focus is on the applications in precision agriculture. Both primary producers and chain partners and service

  17. Image processing in aerial surveillance and reconnaissance: From pixels to understanding

    NARCIS (Netherlands)

    Dijk, J.; Eekeren, A.W.M. van; Rajadell Rojas, O.; Burghouts, G.J.; Schutte, K.

    2013-01-01

    Surveillance and reconnaissance tasks are currently often performed using an airborne platform such as a UAV. The airborne platform can carry different sensors. EO/IR cameras can be used to view a certain area from above. To support the task from the sensor analyst, different image processing techni

  18. Assessing the Accuracy of Ortho-image using Photogrammetric Unmanned Aerial System

    Directory of Open Access Journals (Sweden)

    H. H. Jeong

    2016-06-01

    Full Text Available Smart-camera can not only be operated under network environment anytime and any place but also cost less than the existing photogrammetric UAV since it provides high-resolution image, 3D location and attitude data on a real-time basis from a variety of built-in sensors. This study’s proposed UAV photogrammetric method, low-cost UAV and smart camera were used. The elements of interior orientation were acquired through camera calibration. The image triangulation was conducted in accordance with presence or absence of consideration of the interior orientation (IO parameters determined by camera calibration, The Digital Elevation Model (DEM was constructed using the image data photographed at the target area and the results of the ground control point survey. This study also analyzes the proposed method’s application possibility by comparing a Ortho-image the results of the ground control point survey. Considering these study findings, it is suggested that smartphone is very feasible as a payload for UAV system. It is also expected that smartphone may be loaded onto existing UAV playing direct or indirect roles significantly.

  19. Series of aerial images over Quivira National Wildlife Refuge, acquired October, 1938

    Data.gov (United States)

    US Fish and Wildlife Service, Department of the Interior — This data set is composite of original black and white series images obtained from Earth Explorer (USGS) on October 1st, 5th and 12th, 1938. The original photos were...

  20. Aerial thermal images to assess irrigation efficiency in 'Vitis vinifera' cv. Albariño

    Science.gov (United States)

    Gonzalez, Xesús Pablo; Fandiño, María; Rey, Benjamín J.; José Cancela, Javier

    2017-04-01

    Canopy temperature was defined as key data to irrigation management and to detect crop water stress (Jackson, 1982). Recently, temperature camera was installed on board in a Unmanned Aerial Vehicle (UAV), thus heterogeneity within field could be determined. Pereira et al. (2012) have defined the conceptual and terminological study of crop water use indicators, mainly water use efficiency (WUE) and water productivity (WP). Actually, it is crucial achieve higher WP and WUE, where crop yield variability between years must be reduced with the smallest irrigation water, but with a correct management of crop water stress during the season. In this study, Albariño cultivar grapevine, priority in Galicia (Spain) in Designation of Origen 'Rías Baixas', was assessed in relation to water productivity index, focus on irrigation treatments aspects, during 2016. Albariño vineyard was planted in 1996 on 110-Richter at a spacing of 3 × 2 m (1667 vines ha-1) (41°57 6 N, 8°49 26 W, elevation 101 m). Vines were trained to a vertical trellis system on a Guyot oriented in the East-West direction. Three irrigation treatments were applied: irrigation from budburst to maturation (T1), from flowering to maturation (T2), and from veraison to maturation (T3), moreover a rain-fed treatment was implemented. All WP index was referred to farm yield level (kg ha-1); where the denominator applied to WP TWUfarm, introduced rainfall and irrigation depth; to WP Irrig, only irrigation depth applied; was used. Moreover, crop water stress index (CWSI) was used to determine homogenize areas within experimental plot, using an UAV with a thermal camera (ThermoMAP, senseFly, SW) to achieve a final map with 14 cm per pixel resolution. During August 11th, at the end of veraison, camera was installed in an 'eBee Ag' UAV (senseFly, SW) with a median flight altitude of 75 m over ground level. Yield per hectare were recorded and total irrigation depth per treatment during the growing season from March to

  1. Higher resolution satellite remote sensing and the impact on image mapping

    Science.gov (United States)

    Watkins, Allen H.; Thormodsgard, June M.

    1987-01-01

    Recent advances in spatial, spectral, and temporal resolution of civil land remote sensing satellite data are presenting new opportunities for image mapping applications. The U.S. Geological Survey's experimental satellite image mapping program is evolving toward larger scale image map products with increased information content as a result of improved image processing techniques and increased resolution. Thematic mapper data are being used to produce experimental image maps at 1:100,000 scale that meet established U.S. and European map accuracy standards. Availability of high quality, cloud-free, 30-meter ground resolution multispectral data from the Landsat thematic mapper sensor, along with 10-meter ground resolution panchromatic and 20-meter ground resolution multispectral data from the recently launched French SPOT satellite, present new cartographic and image processing challenges. The need to fully exploit these higher resolution data increases the complexity of processing the images into large-scale image maps. The removal of radiometric artifacts and noise prior to geometric correction can be accomplished by using a variety of image processing filters and transforms. Sensor modeling and image restoration techniques allow maximum retention of spatial and radiometric information. An optimum combination of spectral information and spatial resolution can be obtained by merging different sensor types. These processing techniques are discussed and examples are presented. 

  2. Higher resolution satellite remote sensing and the impact on image mapping

    Science.gov (United States)

    Watkins, Allen H.; Thormodsgard, June M.

    Recent advances in spatial, spectral, and temporal resolution of civil land remote sensing satellite data are presenting new opportunities for image mapping applications. The U.S. Geological Survey's experimental satellite image mapping program is evolving toward larger scale image map products with increased information content as a result of improved image processing techniques and increased resolution. Thematic mapper data are being used to produce experimental image maps at 1:100,000 scale that meet established U.S. and European map accuracy standards. Availability of high quality, cloud-free, 30-meter ground resolution multispectral data from the Landsat thematic mapper sensor, along with 10-meter ground resolution panchromatic and 20-meter ground resolution multispectral data from the recently launched French SPOT satellite, present new cartographic and image processing challenges. The need to fully exploit these higher resolution data increases the complexity of processing the images into large-scale image maps. The removal of radiometric artifacts and noise prior to geometric correction can be accomplished by using a variety of image processing filters and transforms. Sensor modeling and image restoration techniques allow maximum retention of spatial and radiometric information. An optimum combination of spectral information and spatial resolution can be obtained by merging different sensor types. These processing techniques are discussed and examples are presented.

  3. AUTOMATED CONSTRUCTION OF COVERAGE CATALOGUES OF ASTER SATELLITE IMAGE FOR URBAN AREAS OF THE WORLD

    Directory of Open Access Journals (Sweden)

    H. Miyazaki

    2012-07-01

    Full Text Available We developed an algorithm to determine a combination of satellite images according to observation extent and image quality. The algorithm was for testing necessity for completing coverage of the search extent. The tests excluded unnecessary images with low quality and preserve necessary images with good quality. The search conditions of the satellite images could be extended, indicating the catalogue could be constructed with specified periods required for time series analysis. We applied the method to a database of metadata of ASTER satellite images archived in GEO Grid of National Institute of Advanced Industrial Science and Technology (AIST, Japan. As indexes of populated places with geographical coordinates, we used a database of 3372 populated place of more than 0.1 million populations retrieved from GRUMP Settlement Points, a global gazetteer of cities, which has geographical names of populated places associated with geographical coordinates and population data. From the coordinates of populated places, 3372 extents were generated with radiuses of 30 km, a half of swath of ASTER satellite images. By merging extents overlapping each other, they were assembled into 2214 extents. As a result, we acquired combinations of good quality for 1244 extents, those of low quality for 96 extents, incomplete combinations for 611 extents. Further improvements would be expected by introducing pixel-based cloud assessment and pixel value correction over seasonal variations.

  4. The Compact High Resolution Imaging Spectrometer (CHRIS): the future of hyperspectral satellite sensors. Imagery of Oostende coastal and inland waters

    OpenAIRE

    B. De Mol; Ruddick, K

    2004-01-01

    The gap between airborne imaging spectroscopy and traditional multi spectral satellite sensors is decreasing thanks to a new generation of satellite sensors of which CHRIS mounted on the small and low-cost PROBA satellite is the prototype. Although image acquisition and analysis are still in a test phase, the high spatial and spectral resolution and pointability have proved their potential. Because of the high resolution small features, which were before only visible on airborne images, becom...

  5. Adaptive Optics for Satellite and Debris Imaging in LEO and GEO

    Science.gov (United States)

    Copeland, M.; Bennet, F.; Zovaro, A.; Riguat, F.; Piatrou, P.; Korkiakoski, V.; Smith, C.

    2016-09-01

    The Research School of Astronomy and Astrophysics (RSAA) at the Australian National University has developed and Adaptive Optics (AO) system for satellite and debris imaging in low Earth orbit (LEO) and geostationary orbit (GEO). In LEO the size, shape and orientation of objects will be measured with resolution of 50 cm for objects at 800 km range at an 800 nm imaging wavelength. In GEO satellite position will be measured using precision astrometry of nearby stars. We use an AO system with a deformable mirror (DM) of 277 actuators and Shack-Hartmann wavefront sensor operating at 2 kHz. Imaging is performed at a rate of >30 Hz to reduce image blur due to tip-tilt and rotation. We use two imaging modes; a high resolution mode to obtain Nyquist sampled images and a acquisition mode with 75 arcsecond field of view to aid in finding targets.

  6. Biomass prediction model in maize based on satellite images

    Science.gov (United States)

    Mihai, Herbei; Florin, Sala

    2016-06-01

    Monitoring of crops by satellite techniques is very useful in the context of precision agriculture, regarding crops management and agricultural production. The present study has evaluated the interrelationship between maize biomass production and satellite indices (NDVI and NDBR) during five development stages (BBCH code), highlighting different levels of correlation. Biomass production recorded was between 2.39±0.005 t ha-1 (12-13 BBCH code) and 51.92±0.028 t ha-1 (83-85 BBCH code), in relation to vegetation stages studied. Values of chlorophyll content ranged from 24.1±0.25 SPAD unit (12-13 BBCH code) to 58.63±0.47 SPAD unit (71-73 BBCH code), and the obtained satellite indices ranged from 0.035641±0.002 and 0.320839±0.002 for NDVI indices respectively 0.035095±0.034 and 0.491038±0.018 in the case of NDBR indices. By regression analysis it was possible to obtain predictive models of biomass in maize based on the satellite indices, in statistical accurate conditions. The most accurate prediction was possible based on NDBR index (R2 = 0.986, F = 144.23, p<0.001, RMSE = 1.446), then based on chlorophyll content (R2 = 0.834, F = 16.14, p = 0.012, RMSE = 6.927) and NDVI index (R2 = 0.682, F = 3.869, p = 0.116, RMSE = 12.178).

  7. An Operational System for Estimating Road Traffic Information from Aerial Images

    Directory of Open Access Journals (Sweden)

    Jens Leitloff

    2014-11-01

    Full Text Available Given that ground stationary infrastructures for traffic monitoring are barely able to handle everyday traffic volumes, there is a risk that they could fail altogether in situations arising from mass events or disasters. In this work, we present an alternative approach for traffic monitoring during disaster and mass events, which is based on an airborne optical sensor system. With this system, optical image sequences are automatically examined on board an aircraft to estimate road traffic information, such as vehicle positions, velocities and driving directions. The traffic information, estimated in real time on board, is immediately downlinked to a ground station. The airborne sensor system consists of a three-head camera system, a real-time-capable GPS/INS unit, five industrial PCs and a downlink unit. The processing chain for automatic extraction of traffic information contains modules for the synchronization of image and navigation data streams, orthorectification and vehicle detection and tracking modules. The vehicle detector is based on a combination of AdaBoost and support vector machine classifiers. Vehicle tracking relies on shape-based matching operators. The processing chain is evaluated on a large number of image sequences recorded during several campaigns, and the data quality is compared to that obtained from induction loops. In summary, we can conclude that the achieved overall quality of the traffic data extracted by the airborne system is in the range of 68% and 81%. Thus, it is comparable to data obtained from stationary ground sensor networks.

  8. EUV pattern defect detection sensitivity based on aerial image linewidth measurements

    Energy Technology Data Exchange (ETDEWEB)

    Goldberg, K. A.; Mochi, I.; Naulleau, P.; Liang, T.; Yan, P.-Y.; Huh, S.

    2010-02-12

    As the quality of EUV-wavelength mask inspection microscopes improves over time, the image properties and intensity profiles of reflected light can be evaluated in ever-greater detail. The SEMATECH Berkeley Actinic Inspection Tool (AIT) is one such microscope, featuring mask resolution values that match or exceed those available through lithographic printing in current photoresists. In order to evaluate the defect detection sensitivity of the AIT for dense line patterns on typical masks, the authors study the line width roughness (LWR) on two masks, as measured in the EUV images. They report the through-focus and pitch dependence of contrast, image log slope, linewidth, and LWR. The AIT currently reaches LWR 3{sigma} values close to 9 nm for 175 nm half-pitch lines. This value is below 10% linewidth for nearly all lines routinely measured in the AIT. Evidence suggests that this lower level may arise from the mask's inherent pattern roughness. While the sensitivity limit of the AlT has not yet been established, it is clear that the AIT has the required sensitivity to detect defects that cause 10% linewidth changes in line sizes of 125 nm and larger.

  9. Nightfire method to track volcanic eruptions from multispectral satellite images

    Science.gov (United States)

    Trifonov, Grigory; Zhizhin, Mikhail; Melnikov, Dmitry

    2016-04-01

    This work presents the first results of an application of the Nightfire hotspot algorithm towards volcano activity detection. Nightfire algorithm have been developed to play along with a Suomi-NPP polar satellite launched in 2011, which has a new generation multispectral VIIRS thermal sensor on board, to detect gas flares related to the upstream and downstream production of oil and natural gas. Simultaneously using of nighttime data in SWIR, MWIR, and LWIR sensor bands the algorithm is able to estimate the hotspot temperature, size and radiant heat. Four years of non-filtered observations have been accumulated in a spatio-temporal detection database, which currently totals 125 GB in size. The first part of this work presents results of retrospective cross-match of the detection database with the publicly available observed eruptions databases. The second part discusses how an approximate 3D shape of a lava lake could be modeled based on the apparent source size and satellite zenith angle. The third part presents the results of fusion Landsat-8 and Himawari-8 satellites data with the VIIRS Nightfire for several active volcanoes.

  10. Applicability of New Approaches of Sensor Orientation to Micro Aerial Vehicles

    Science.gov (United States)

    Rehak, M.; Skaloud, J.

    2016-06-01

    This study highlights the benefits of precise aerial position and attitude control in the context of mapping with Micro Aerial Vehicles (MAVs). Accurate mapping with MAVs is gaining importance in applications such as corridor mapping, road and pipeline inspections or mapping of large areas with homogeneous surface structure, e.g. forests or agricultural fields. There, accurate aerial control plays a major role in successful terrain reconstruction and artifact-free ortophoto generation. The presented experiments focus on new approaches of aerial control. We confirm practically that the relative aerial position and attitude control can improve accuracy in difficult mapping scenarios. Indeed, the relative orientation method represents an attractive alternative in the context of MAVs for two reasons. First, the procedure is somewhat simplified, e.g. the angular misalignment, so called boresight, between the camera and the inertial measurement unit (IMU) does not have to be determined and, second, the effect of possible systematic errors in satellite positioning (e.g. due to multipath and/or incorrect recovery of differential carrier-phase ambiguities) is mitigated. First, we present a typical mapping project over an agricultural field and second, we perform a corridor road mapping. We evaluate the proposed methods in scenarios with and without automated image observations. We investigate a recently proposed concept where adjustment is performed using image observations limited to ground control and check points, so called fast aerial triangulation (Fast AT). In this context we show that accurate aerial control (absolute or relative) together with a few image observations can deliver accurate results comparable to classical aerial triangulation with thousands of image measurements. This procedure in turns reduces the demands on processing time and the requirements on the existence of surface texture. Finally, we compare the above mentioned procedures with direct sensor

  11. Updating Object for GIS Database Information Using High Resolution Satellite Images: a Case Study Zonguldak

    Science.gov (United States)

    Alkan, M.; Arca, D.; Bayik, Ç.; Marangoz, A. M.

    2011-09-01

    Nowadays Geographic Information Systems (GIS) uses Remote Sensing (RS) data for a lot of applications. One of the application areas is the updating of the GIS database using high resolution imagery. In this context high resolution satellite imagery data is very important for many applications areas today's and future. And also, high resolution satellite imagery data will be used in many applications for different purposes. Information systems needs to high resolution imagery data for updating. Updating is very important component for the any of the GIS systems. One of this area will be updated and kept alive GIS database information. High resolution satellite imagery is used with different data base which serve map information via internet and different aims of information systems applications in future topographic and cartographic information systems will very important in our country in this sense use of the satellite images will be unavoidable. In this study explain to how is acquired to satellite images and how is use this images in information systems for object and roads. Firstly, pan-sharpened two of the IKONOS's images have been produced by fusion of high resolution PAN and MS images using PCI Geomatica v9.1 software package. Automatic object extraction has been made using eCognition v4.0.6. On the other hand, these objects have been manually digitized from high resolution images using ArcGIS v9.3. software package. Application section of in this study, satellite images data will be compared each other and GIS objects and road database. It is also determined which data is useful in Geographic Information Systems. Finally, this article explains that integration of remote sensing technology and GIS applications.

  12. Satellite Imagery Cadastral Features Extractions using Image Processing Algorithms: A Viable Option for Cadastral Science

    Directory of Open Access Journals (Sweden)

    Usman Babawuro

    2012-07-01

    Full Text Available Satellite images are used for feature extraction among other functions. They are used to extract linear features, like roads, etc. These linear features extractions are important operations in computer vision. Computer vision has varied applications in photogrammetric, hydrographic, cartographic and remote sensing tasks. The extraction of linear features or boundaries defining the extents of lands, land covers features are equally important in Cadastral Surveying. Cadastral Surveying is the cornerstone of any Cadastral System. A two dimensional cadastral plan is a model which represents both the cadastral and geometrical information of a two dimensional labeled Image. This paper aims at using and widening the concepts of high resolution Satellite imagery data for extracting representations of cadastral boundaries using image processing algorithms, hence minimizing the human interventions. The Satellite imagery is firstly rectified hence establishing the satellite imagery in the correct orientation and spatial location for further analysis. We, then employ the much available Satellite imagery to extract the relevant cadastral features using computer vision and image processing algorithms. We evaluate the potential of using high resolution Satellite imagery to achieve Cadastral goals of boundary detection and extraction of farmlands using image processing algorithms. This method proves effective as it minimizes the human demerits associated with the Cadastral surveying method, hence providing another perspective of achieving cadastral goals as emphasized by the UN cadastral vision. Finally, as Cadastral science continues to look to the future, this research aimed at the analysis and getting insights into the characteristics and potential role of computer vision algorithms using high resolution satellite imagery for better digital Cadastre that would provide improved socio economic development.

  13. Object Based and Pixel Based Classification Using Rapideye Satellite Imager of ETI-OSA, Lagos, Nigeria

    OpenAIRE

    Esther Oluwafunmilayo Makinde; Ayobami Taofeek Salami; James Bolarinwa Olaleye; Oluwapelumi Comfort Okewusi

    2016-01-01

    Several studies have been carried out to find an appropriate method to classify the remote sensing data. Traditional classification approaches are all pixel-based, and do not utilize the spatial information within an object which is an important source of information to image classification. Thus, this study compared the pixel based and object based classification algorithms using RapidEye satellite image of Eti-Osa LGA, Lagos. In the object-oriented approach, the image was segmented to homog...

  14. Series of aerial images over Quivira National Wildlife Refuge, acquired on October 28th and November 4th, 1954

    Data.gov (United States)

    US Fish and Wildlife Service, Department of the Interior — This data set includes four georeferenced versions of original black and white aerial photographs obtained from Earth Explorer (USGS) on October 28th and November...

  15. Developing an efficient technique for satellite image denoising and resolution enhancement for improving classification accuracy

    Science.gov (United States)

    Thangaswamy, Sree Sharmila; Kadarkarai, Ramar; Thangaswamy, Sree Renga Raja

    2013-01-01

    Satellite images are corrupted by noise during image acquisition and transmission. The removal of noise from the image by attenuating the high-frequency image components removes important details as well. In order to retain the useful information, improve the visual appearance, and accurately classify an image, an effective denoising technique is required. We discuss three important steps such as image denoising, resolution enhancement, and classification for improving accuracy in a noisy image. An effective denoising technique, hybrid directional lifting, is proposed to retain the important details of the images and improve visual appearance. The discrete wavelet transform based interpolation is developed for enhancing the resolution of the denoised image. The image is then classified using a support vector machine, which is superior to other neural network classifiers. The quantitative performance measures such as peak signal to noise ratio and classification accuracy show the significance of the proposed techniques.

  16. Automatic Open Space Area Extraction and Change Detection from High Resolution Urban Satellite Images

    CERN Document Server

    Kodge, B G

    2011-01-01

    In this paper, we study efficient and reliable automatic extraction algorithm to find out the open space area from the high resolution urban satellite imagery, and to detect changes from the extracted open space area during the period 2003, 2006 and 2008. This automatic extraction and change detection algorithm uses some filters, segmentation and grouping that are applied on satellite images. The resultant images may be used to calculate the total available open space area and the built up area. It may also be used to compare the difference between present and past open space area using historical urban satellite images of that same projection, which is an important geo spatial data management application.

  17. Application of Artificial Intelligent For Armour Vehicle Detection Using Digital Image Processing For Aerial Application

    Directory of Open Access Journals (Sweden)

    Kamaruddin Abd Ghani

    2011-01-01

    Full Text Available This paper will presents a new automatic target recognition (ATR algorithm to detect targets such as battle tanks and armoured personal carriers especially that been used by Malaysia Armed Forces from air-to- ground scenario. Numerous friendly-fire incidents justify the need for identification of armour vehicle in both command control and weapon systems. Rapid and reliable identification of the targets at maximum surveillance is a challenging problem. In this paper work, the reliable method to segregate the potential target from the background scene such as Fourier Transform is applied before the extracted target will be process in order to get the detail of edges and boundaries using Hough Transform. The edges will provide sufficient information for the system to generate training data for Artificial Neural Network simulation to recognize the potential target image.

  18. Automated 3D modelling of buildings from aerial and space imagery using image understanding techniques

    Science.gov (United States)

    Kim, Taejung

    The development of a fully automated mapping system is one of the fundamental goals in photogrammetry and remote sensing. As an approach towards this goal, this thesis describes the work carried out in the automated 3D modelling of buildings in urban scenes. The whole work is divided into three parts: the development of an automated height extraction system, the development of an automated building detection system, and the combination of these two systems. After an analysis of the key problems of urban-area imagery for stereo matching, buildings were found to create isolated regions and blunders. From these findings, an automated building height extraction system was developed. This stereoscopic system is based on a pyramidal (area-based) matching algorithm with automatic seed points and a tile-based control strategy. To remove possible blunders and extract buildings from other background objects, a series of "smart" operations using linear elements from buildings were also applied. A new monoscopic building detection system was developed based on a graph constructed from extracted lines and their relations. After extracting lines from a single image using low-level image processing techniques, line relations are searched for and a graph constructed. By finding closed loops in the graph, building hypotheses are generated. These are then merged and verified using shadow analysis and perspective geometry. After verification, each building hypothesis indicates either a building or a part of a building. By combining results from these two systems, 3D building roofs can be modelled automatically. The modelling is performed using height information obtained from the height extraction system and interpolation boundaries obtained from the building detection system. Other fusion techniques and the potential improvements due to these are also discussed. Quantitative analysis was performed for each algorithm presented in this thesis and the results support the newly

  19. Gemini Planet Imager Observational Calibrations VIII: Characterization and Role of Satellite Spots

    CERN Document Server

    Wang, Jason J; Graham, James R; Savransky, Dmitry; Ingraham, Patrick J; Ward-Duong, Kimberly; Patience, Jennifer; De Rosa, Robert J; Bulger, Joanna; Sivaramakrishnan, Anand; Perrin, Marshall D; Thomas, Sandrine J; Sadakuni, Naru; Greenbaum, Alexandra Z; Pueyo, Laurent; Marois, Christian; Oppenheimer, Ben R; Kalas, Paul; Cardwell, Andrew; Goodsell, Stephen; Hibon, Pascale; Rantakyrö, Fredrik T

    2014-01-01

    The Gemini Planet Imager (GPI) combines extreme adaptive optics, an integral field spectrograph, and a high performance coronagraph to directly image extrasolar planets in the near-infrared. Because the coronagraph blocks most of the light from the star, it prevents the properties of the host star from being measured directly. Instead, satellite spots, which are created by diffraction from a square grid in the pupil plane, can be used to locate the star and extract its spectrum. We describe the techniques implemented into the GPI Data Reduction Pipeline to measure the properties of the satellite spots and discuss the precision of the reconstructed astrometry and spectrophotometry of the occulted star. We find the astrometric precision of the satellite spots in an $H$-band datacube to be $0.05$ pixels and is best when individual satellite spots have a signal to noise ratio (SNR) of $> 20$. In regards to satellite spot spectrophotometry, we find that the total flux from the satellite spots is stable to $\\sim 7\\...

  20. Geomatics techniques applied to time series of aerial images for multitemporal geomorphological analysis of the Miage Glacier (Mont Blanc).

    Science.gov (United States)

    Perotti, Luigi; Carletti, Roberto; Giardino, Marco; Mortara, Giovanni

    2010-05-01

    The Miage glacier is the major one in the Italian side of the Mont Blanc Massif, the third by area and the first by longitudinal extent among Italian glaciers. It is a typical debris covered glacier, since the end of the L.I.A. The debris coverage reduces ablation, allowing a relative stability of the glacier terminus, which is characterized by a wide and articulated moraine apparatus. For its conservative landforms, the Miage Glacier has a great importance for the analysis of the geomorphological response to recent climatic changes. Thanks to an organized existing archive of multitemporal aerial images (1935 to present) a photogrammetric approach has been applied to detect recent geomorphological changes in the Miage glacial basin. The research team provided: a) to digitize all the available images (still in analogic form) through photogrammetric scanners (very low image distortions devices) taking care of correctly defining the resolution of the acquisition compared to the scale mapping images are suitable for; b) to import digitized images into an appropriate digital photogrammetry software environment; c) to manage images in order, where possible, to carried out the stereo models orientation necessary for 3D navigation and plotting of critical geometric features of the glacier. Recognized geometric feature, referring to different periods, can be transferred to vector layers and imported in a GIS for further comparisons and investigations; d) to produce multi-temporal Digital Elevation Models for glacier volume changes; e) to perform orthoprojection of such images to obtain multitemporal orthoimages useful for areal an planar terrain evaluation and thematic analysis; f) to evaluate both planimetric positioning and height determination accuracies reachable through the photogrammetric process. Users have to known reliability of the measures they can do over such products. This can drive them to define the applicable field of this approach and this can help them to

  1. Unmanned aerial vehicle acquisition of three-dimensional digital image correlation measurements for structural health monitoring of bridges

    Science.gov (United States)

    Reagan, Daniel; Sabato, Alessandro; Niezrecki, Christopher

    2017-04-01

    Civil engineering structures such as bridges, buildings, and tunnels continue to be used despite aging and deterioration well past their design life. In 2013, the American Society of Civil Engineers (ASCE) rated the state of the U.S. bridges as mediocre, despite the $12.8 billion USD annually invested. Traditional inspection and monitoring techniques may produce inconsistent results, are labor intensive and too time-consuming to be considered effective for large-scale monitoring. Therefore, new structural health monitoring systems must be developed that are automated, highly accurate, minimally invasive, and cost effective. Three-dimensional (3D) digital image correlation (DIC) systems possess the capability of extracting full-field strain, displacement, and geometry profiles. Furthermore, as this measurement technique is implemented within an Unmanned Aerial Vehicle (UAV) the capability to expedite the optical-based measurement process is increased as well as the infrastructure downtime being reduced. These resulting integrity maps of the structure of interest can be easily interpreted by trained personal. Within this paper, the feasibility of performing DIC measurements using a pair of cameras installed on a UAV is shown. Performance is validated with in-flight measurements. Also, full-field displacement monitoring, 3D measurement stitching, and 3D point-tracking techniques are employed in conjunction with 3D mapping and data management software. The results of these experiments show that the combination of autonomous flight with 3D DIC and other non-contact measurement systems provides a highly valuable and effective civil inspection platform.

  2. GPU Accelerated Automated Feature Extraction From Satellite Images

    Directory of Open Access Journals (Sweden)

    K. Phani Tejaswi

    2013-04-01

    Full Text Available The availability of large volumes of remote sensing data insists on higher degree of automation in featureextraction, making it a need of thehour. Fusingdata from multiple sources, such as panchromatic,hyperspectraland LiDAR sensors, enhances the probability of identifying and extracting features such asbuildings, vegetation or bodies of water by using a combination of spectral and elevation characteristics.Utilizing theaforementioned featuresin remote sensing is impracticable in the absence ofautomation.Whileefforts are underway to reduce human intervention in data processing, this attempt alone may notsuffice. Thehuge quantum of data that needs to be processed entailsaccelerated processing to be enabled.GPUs, which were originally designed to provide efficient visualization,arebeing massively employed forcomputation intensive parallel processing environments. Image processing in general and hence automatedfeatureextraction, is highly computation intensive, where performance improvements have a direct impacton societal needs. In this context, an algorithm has been formulated for automated feature extraction froma panchromatic or multispectral image based on image processing techniques.Two Laplacian of Guassian(LoGmasks were applied on the image individually followed by detection of zero crossing points andextracting the pixels based on their standard deviationwiththe surrounding pixels. The two extractedimages with different LoG masks were combined together which resulted in an image withthe extractedfeatures and edges.Finally the user is at liberty to apply the image smoothing step depending on the noisecontent in the extracted image.The image ispassed through a hybrid median filter toremove the salt andpepper noise from the image.This paper discusses theaforesaidalgorithmforautomated featureextraction, necessity of deployment of GPUs for thesame;system-level challenges and quantifies thebenefits of integrating GPUs in such environment. The

  3. Effect of Training Class Label Noise on Classification Performances for Land Cover Mapping with Satellite Image Time Series

    Directory of Open Access Journals (Sweden)

    Charlotte Pelletier

    2017-02-01

    Full Text Available Supervised classification systems used for land cover mapping require accurate reference databases. These reference data come generally from different sources such as field measurements, thematic maps, or aerial photographs. Due to misregistration, update delay, or land cover complexity, they may contain class label noise, i.e., a wrong label assignment. This study aims at evaluating the impact of mislabeled training data on classification performances for land cover mapping. Particularly, it addresses the random and systematic label noise problem for the classification of high resolution satellite image time series. Experiments are carried out on synthetic and real datasets with two traditional classifiers: Support Vector Machines (SVM and Random Forests (RF. A synthetic dataset has been designed for this study, simulating vegetation profiles over one year. The real dataset is composed of Landsat-8 and SPOT-4 images acquired during one year in the south of France. The results show that both classifiers are little influenced for low random noise levels up to 25%–30%, but their performances drop down for higher noise levels. Different classification configurations are tested by increasing the number of classes, using different input feature vectors, and changing the number of training instances. Algorithm complexities are also analyzed. The RF classifier achieves high robustness to random and systematic label noise for all the tested configurations; whereas the SVM classifier is more sensitive to the kernel choice and to the input feature vectors. Finally, this work reveals that the cross-validation procedure is impacted by the presence of class label noise.

  4. Soil moisture and evapotranspiration of wetlands vegetation habitats retrieved from satellite images

    Science.gov (United States)

    Dabrowska-Zielinska, K.; Budzynska, M.; Kowalik, W.; Turlej, K.

    2010-08-01

    The research has been carried out in Biebrza Ramsar Convention test site situated in the N-E part of Poland. Data from optical and microwave satellite images have been analysed and compared to the detailed soil-vegetation ground truth measurements conducted during the satellite overpasses. Satellite data applied for the study include: ENVISAT.ASAR, ENVISAT.MERIS, ALOS.PALSAR, ALOS.AVNIR-2, ALOS.PRISM, TERRA.ASTER, and NOAA.AVHRR. Optical images have been used for classification of wetlands vegetation habitats and vegetation surface roughness expressed by LAI. Also, heat fluxes have been calculated using NOAA.AVHRR data and meteorological data. Microwave images have been used for the assessment of soil moisture. For each of the classified wetlands vegetation habitats the relationship between soil moisture and backscattering coefficient has been examined, and the best combination of microwave variables (wave length, incidence angle, polarization) has been used for mapping and monitoring of soil moisture. The results of this study give possibility to improve models of water cycle over wetlands ecosystems by adding information about soil moisture and surface heat fluxes derived from satellite images. Such information is very essential for better protection of the European sensitive wetland ecosystems. ENVISAT and ALOS images have been obtained from ESA for AO ID 122 and AOALO.3742 projects.

  5. Detecting aircrafts from satellite images using saliency and conical pyramid based template representation

    Indian Academy of Sciences (India)

    SAMIK BANERJEE; NITIN GUPTA; SUKHENDU DAS; PINAKI ROY CHOWDHURY; L K SINHA

    2016-10-01

    Automatic target localization in satellite images still remains as a challenging problem in the field of computer vision. The issues involved in locating targets in satellite images are viewpoint, spectral (intensity) and scale variations. Diversity in background texture and target clutter also adds up to the complexity of the problem of localizing aircrafts in satellite images. Failure of modern feature extraction and object detection methods highlight the complexity of the problem. In the proposed work, pre-processing techniques, viz.denoising and contrast enhancement, are first used to improve the quality of the images. Then, the concept of unsupervised saliency is used to detect the potential regions of interest, which reduces the search space. Parts from the salient regions are further processed using clustering and morphological processing to get the probable regions of isolated aircraft targets. Finally, a novel conical pyramid based framework for template representation of the target samples is proposed for matching. Experimental results shown on a few satellite images exhibit the superior performance of the proposed methods.

  6. Optimization of post-classification processing of high-resolution satellite image: A case study

    Institute of Scientific and Technical Information of China (English)

    DONG; Rencai; DONG; Jiajia; WU; Gang; DENG; Hongbing

    2006-01-01

    The application of remote sensing monitoring techniques plays a crucial role in evaluating and governing the vast amount of ecological construction projects in China. However, extracting information of ecological engineering target through high-resolution satellite image is arduous due to the unique topography and complicated spatial pattern on the Loess Plateau of China. As a result, enhancing classification accuracy is a huge challenge to high-resolution image processing techniques. Image processing techniques have a definitive effect on image properties and the selection of different parameters may change the final classification accuracy during post-classification processing. The common method of eliminating noise and smoothing image is majority filtering. However, the filter function may modify the original classified image and the final accuracy. The aim of this study is to develop an efficient and accurate post-processing technique for acquiring information of soil and water conservation engineering, on the Loess Plateau of China, using SPOT image with 2.5 rn resolution. We argue that it is vital to optimize satellite image filtering parameters for special areas and purposes, which focus on monitoring ecological construction projects. We want to know how image filtering influences final classified results and which filtering kernel is optimum. The study design used a series of window sizes to filter the original classified image, and then assess the accuracy of each output map and image quality. We measured the relationship between filtering window size and classification accuracy, and optimized the post-processing techniques of SPOT5satellite images. We conclude that (1) smoothing with the majority filter is sensitive to the information accuracy of soil and water conservation engineering, and (2) for SPOT5 2.5 m image, the 5×5 pixel majority filter is most suitable kernel for extracting information of ecological construction sites in the Loess Plateau of

  7. Effect of satellite formations and imaging modes on global albedo estimation

    Science.gov (United States)

    Nag, Sreeja; Gatebe, Charles K.; Miller, David W.; de Weck, Olivier L.

    2016-05-01

    We confirm the applicability of using small satellite formation flight for multi-angular earth observation to retrieve global, narrow band, narrow field-of-view albedo. The value of formation flight is assessed using a coupled systems engineering and science evaluation model, driven by Model Based Systems Engineering and Observing System Simulation Experiments. Albedo errors are calculated against bi-directional reflectance data obtained from NASA airborne campaigns made by the Cloud Absorption Radiometer for the seven major surface types, binned using MODIS' land cover map - water, forest, cropland, grassland, snow, desert and cities. A full tradespace of architectures with three to eight satellites, maintainable orbits and imaging modes (collective payload pointing strategies) are assessed. For an arbitrary 4-sat formation, changing the reference, nadir-pointing satellite dynamically reduces the average albedo error to 0.003, from 0.006 found in the static referencecase. Tracking pre-selected waypoints with all the satellites reduces the average error further to 0.001, allows better polar imaging and continued operations even with a broken formation. An albedo error of 0.001 translates to 1.36 W/m2 or 0.4% in Earth's outgoing radiation error. Estimation errors are found to be independent of the satellites' altitude and inclination, if the nadir-looking is changed dynamically. The formation satellites are restricted to differ in only right ascension of planes and mean anomalies within slotted bounds. Three satellites in some specific formations show average albedo errors of less than 2% with respect to airborne, ground data and seven satellites in any slotted formation outperform the monolithic error of 3.6%. In fact, the maximum possible albedo error, purely based on angular sampling, of 12% for monoliths is outperformed by a five-satellite formation in any slotted arrangement and an eight satellite formation can bring that error down four fold to 3%. More than

  8. AN IMPROVED SNAKE MODEL FOR REFINEMENT OF LIDAR-DERIVED BUILDING ROOF CONTOURS USING AERIAL IMAGES

    Directory of Open Access Journals (Sweden)

    Q. Chen

    2016-06-01

    Full Text Available Building roof contours are considered as very important geometric data, which have been widely applied in many fields, including but not limited to urban planning, land investigation, change detection and military reconnaissance. Currently, the demand on building contours at a finer scale (especially in urban areas has been raised in a growing number of studies such as urban environment quality assessment, urban sprawl monitoring and urban air pollution modelling. LiDAR is known as an effective means of acquiring 3D roof points with high elevation accuracy. However, the precision of the building contour obtained from LiDAR data is restricted by its relatively low scanning resolution. With the use of the texture information from high-resolution imagery, the precision can be improved. In this study, an improved snake model is proposed to refine the initial building contours extracted from LiDAR. First, an improved snake model is constructed with the constraints of the deviation angle, image gradient, and area. Then, the nodes of the contour are moved in a certain range to find the best optimized result using greedy algorithm. Considering both precision and efficiency, the candidate shift positions of the contour nodes are constrained, and the searching strategy for the candidate nodes is explicitly designed. The experiments on three datasets indicate that the proposed method for building contour refinement is effective and feasible. The average quality index is improved from 91.66% to 93.34%. The statistics of the evaluation results for every single building demonstrated that 77.0% of the total number of contours is updated with higher quality index.

  9. An Improved Snake Model for Refinement of Lidar-Derived Building Roof Contours Using Aerial Images

    Science.gov (United States)

    Chen, Qi; Wang, Shugen; Liu, Xiuguo

    2016-06-01

    Building roof contours are considered as very important geometric data, which have been widely applied in many fields, including but not limited to urban planning, land investigation, change detection and military reconnaissance. Currently, the demand on building contours at a finer scale (especially in urban areas) has been raised in a growing number of studies such as urban environment quality assessment, urban sprawl monitoring and urban air pollution modelling. LiDAR is known as an effective means of acquiring 3D roof points with high elevation accuracy. However, the precision of the building contour obtained from LiDAR data is restricted by its relatively low scanning resolution. With the use of the texture information from high-resolution imagery, the precision can be improved. In this study, an improved snake model is proposed to refine the initial building contours extracted from LiDAR. First, an improved snake model is constructed with the constraints of the deviation angle, image gradient, and area. Then, the nodes of the contour are moved in a certain range to find the best optimized result using greedy algorithm. Considering both precision and efficiency, the candidate shift positions of the contour nodes are constrained, and the searching strategy for the candidate nodes is explicitly designed. The experiments on three datasets indicate that the proposed method for building contour refinement is effective and feasible. The average quality index is improved from 91.66% to 93.34%. The statistics of the evaluation results for every single building demonstrated that 77.0% of the total number of contours is updated with higher quality index.

  10. Digital image processing for the earth resources technology satellite data.

    Science.gov (United States)

    Will, P. M.; Bakis, R.; Wesley, M. A.

    1972-01-01

    This paper discusses the problems of digital processing of the large volumes of multispectral image data that are expected to be received from the ERTS program. Correction of geometric and radiometric distortions are discussed and a byte oriented implementation is proposed. CPU timing estimates are given for a System/360 Model 67, and show that a processing throughput of 1000 image sets per week is feasible.

  11. Community patterns of tropical tree phenology derived from Unmanned Aerial Vehicle images: intra- and interspecific variation, association with species plant traits, and response to interannual climate variation

    Science.gov (United States)

    Bohlman, Stephanie; Rifai, Sami; Park, John; Dandois, Jonathan; Muller-Landau, Helene

    2017-04-01

    Phenology is a key life history trait of plant species and critical driver of ecosystem processes. There is strong evidence that phenology is shifting in temperate ecosystems in response to climate change, but tropical forest phenology remains poorly quantified and understood. A key challenge is that tropical forests contain hundreds of plant species with a wide variety of phenological patterns, which makes it difficult to collect sufficient ground-based field data to characterize individual tropical tree species phenologies. Satellite-based observations, an important source of phenology data in northern latitudes, are hindered by frequent cloud cover in the tropics. To quantify phenology over a large number of individuals and species, we collected bi-weekly images from unmanned aerial vehicles (UAVs) in the well-studied 50-ha forest inventory plot on Barro Colorado Island, Panama. The objective of this study is to quantify inter- and intra-specific responses of tropical tree leaf phenology to environmental variation over large spatial scales and identify key environmental variables and physiological mechanisms underpinning phenological variation. Between October 2014 and December 2015 and again in May 2015, we collected a total of 35 sets of UAV images, each with continuous coverage of the 50-ha plot, where every tree ≥ 1 cm DBH is mapped. UAV imagery was corrected for exposure, orthorectified, and then processed to extract spectral, texture, and image information for individual tree crowns, which was then used as inputs for a machine learning algorithm that successfully predicted the percentages of leaf, branch, and flower cover for each tree crown (r2=0.76 between observed and predicted percent branch cover for individual tree crowns). We then quantified cumulative annual deciduousness for each crown by fitting a non-parametric curve of flexible shape to its predicted percent branch time series and calculated the area under the curve. We obtained the species

  12. Pre-processing Algorithm for Rectification of Geometric Distortions in Satellite Images

    Directory of Open Access Journals (Sweden)

    Narayan Panigrahi

    2011-02-01

    Full Text Available A number of algorithms have been reported to process and remove geometric distortions in satellite images. Ortho-correction, geometric error correction, radiometric error removal, etc are a few important examples. These algorithm require supplementary meta-information of the satellite images such as ground control points and correspondence, sensor orientation details, elevation profile of the terrain, etc to establish corresponding transformations. In this paper, a pre-processing algorithm has been proposed which removes systematic distortions of a satellite image and thereby removes the blank portion of the image. It is an input-to-output mapping of image pixels, where the transformation computes the coordinate of each output pixel corresponding to the input pixel of an image. The transformation is established by the exact amount of scaling, rotation and translation needed for each pixel in the input image so that the distortion induced during the recording stage is corrected.Defence Science Journal, 2011, 61(2, pp.174-179, DOI:http://dx.doi.org/10.14429/dsj.61.421

  13. A fast and automatic mosaic method for high-resolution satellite images

    Science.gov (United States)

    Chen, Hongshun; He, Hui; Xiao, Hongyu; Huang, Jing

    2015-12-01

    We proposed a fast and fully automatic mosaic method for high-resolution satellite images. First, the overlapped rectangle is computed according to geographical locations of the reference and mosaic images and feature points on both the reference and mosaic images are extracted by a scale-invariant feature transform (SIFT) algorithm only from the overlapped region. Then, the RANSAC method is used to match feature points of both images. Finally, the two images are fused into a seamlessly panoramic image by the simple linear weighted fusion method or other method. The proposed method is implemented in C++ language based on OpenCV and GDAL, and tested by Worldview-2 multispectral images with a spatial resolution of 2 meters. Results show that the proposed method can detect feature points efficiently and mosaic images automatically.

  14. Multiangle Bistatic SAR Imaging and Fusion Based on BeiDou-2 Navigation Satellite System

    Directory of Open Access Journals (Sweden)

    Zeng Tao

    2015-01-01

    Full Text Available Bistatic Synthetic Aperture Radar (BSAR based on the Global Navigation Service System (GNSSBSAR uses navigation satellites as radar transmitters, which are low in cost. However, GNSS-BSAR images have poor resolution and low Signal-to-Noise Ratios (SNR. In this paper, a multiangle observation and data processing strategy are presented based on BeiDou-2 navigation satellite imagery, from which twenty-six BSAR images in different configurations are obtained. A region-based fusion algorithm using region of interest segmentation is proposed, and a high-quality fusion image is obtained. The results reveal that the multiangle imaging method can extend the applications of GNSS-BSAR.

  15. A fast and mobile system for registration of low-altitude visual and thermal aerial images using multiple small-scale UAVs

    Science.gov (United States)

    Yahyanejad, Saeed; Rinner, Bernhard

    2015-06-01

    The use of multiple small-scale UAVs to support first responders in disaster management has become popular because of their speed and low deployment costs. We exploit such UAVs to perform real-time monitoring of target areas by fusing individual images captured from heterogeneous aerial sensors. Many approaches have already been presented to register images from homogeneous sensors. These methods have demonstrated robustness against scale, rotation and illumination variations and can also cope with limited overlap among individual images. In this paper we focus on thermal and visual image registration and propose different methods to improve the quality of interspectral registration for the purpose of real-time monitoring and mobile mapping. Images captured by low-altitude UAVs represent a very challenging scenario for interspectral registration due to the strong variations in overlap, scale, rotation, point of view and structure of such scenes. Furthermore, these small-scale UAVs have limited processing and communication power. The contributions of this paper include (i) the introduction of a feature descriptor for robustly identifying corresponding regions of images in different spectrums, (ii) the registration of image mosaics, and (iii) the registration of depth maps. We evaluated the first method using a test data set consisting of 84 image pairs. In all instances our approach combined with SIFT or SURF feature-based registration was superior to the standard versions. Although we focus mainly on aerial imagery, our evaluation shows that the presented approach would also be beneficial in other scenarios such as surveillance and human detection. Furthermore, we demonstrated the advantages of the other two methods in case of multiple image pairs.

  16. Wave Period and Coastal Bathymetry Estimations from Satellite Images

    Science.gov (United States)

    Danilo, Celine; Melgani, Farid

    2016-08-01

    We present an approach for wave period and coastal water depth estimation. The approach based on wave observations, is entirely independent of ancillary data and can theoretically be applied to SAR or optical images. In order to demonstrate its feasibility we apply our method to more than 50 Sentinel-1A images of the Hawaiian Islands, well-known for its long waves. Six wave buoys are available to compare our results with in-situ measurements. The results on Sentinel-1A images show that half of the images were unsuitable for applying the method (no swell or wavelength too small to be captured by the SAR). On the other half, 78% of the estimated wave periods are in accordance with buoy measurements. In addition, we present preliminary results of the estimation of the coastal water depth on a Landsat-8 image (with characteristics close to Sentinel-2A). With a squared correlation coefficient of 0.7 for ground truth measurement, this approach reveals promising results for monitoring coastal bathymetry.

  17. SURF-based position estimation method using aerial image sequences.%基于SURF特征的航空序列图像位置估计算法

    Institute of Scientific and Technical Information of China (English)

    乔奎贤; 赵妮; 李耀军

    2013-01-01

    视觉传感器在航空无人机导航和定位任务中应用越来越广泛。针对无人机位置参数估计问题,提出了一种基于SURF特征的图像配准算法,该算法能够适应航空序列图像的旋转、尺度变换及噪声干扰,实现无人机位置的精确估计。构建了SURF尺度空间,运用快速Hessian矩阵定位极值点,计算出航空图像的64维SURF特征描述子;基于Hessian矩阵迹完成特征点匹配;使用RANSAC算法剔除出格点,实现位置参数的精确估计。通过航空图像序列实测数据位置估计实验,验证了该算法的有效性。%Vision sensor is widely used in the aviation aircraft navigation and positioning tasks. For position parameter estima-tion of aircraft, this paper presents a SURF feature based image registration algorithm, it is able to adapt to aerial image sequence rotation, scale transformation and noise, to achieve accurate estimates of aircraft position. It builds SURF scale space, uses fast Hessian matrix maximum values to calculate 64-dimensional SURF feature descriptors of aerial images. Based on the trace of the Hessian matrix, it completes feature points matching task. By using RANSAC algorithm, it removes the outliers matching points for accurate position estimate. Experiments with two real aerial image sequences show the effectiveness of the proposed position estimation algorithm.

  18. Attitude motion compensation for imager on Fengyun-4 geostationary meteorological satellite

    Science.gov (United States)

    Lyu, Wang; Dai, Shoulun; Dong, Yaohai; Shen, Yili; Song, Xiaozheng; Wang, Tianshu

    2017-09-01

    A compensation method is used in Chinese Fengyun-4 satellite to counteracting the line-of-sight influence by attitude motion during imaging. The method is acted on-board by adding the compensation amount to the instrument scanning control circuit. The mathematics simulation and the three-axis air-bearing test results show that the method works effectively.

  19. Collection of road traffic information from satellite images and digital map

    Science.gov (United States)

    Shinmura, Fumito; Saji, Hitoshi

    2010-10-01

    There have been many reports on the analysis of the Earth's surface by remote sensing. The purpose of this study is to analyze traffic information, and we have been studying methods of collecting traffic information by remote sensing. To collect traffic information, sensors installed on the roadside are frequently used. However, methods using sensors only collect information around the positions of the sensors. In this study, we attempt to solve this problem by using satellite images, which have recently become increasingly available. We propose a method of collecting traffic information over a large area using satellite images as well as three-dimensional digital maps. We assess traffic conditions by computing the number of edges of vehicles per road section as follows. First, the edges of vehicles are detected in satellite images. During this processing, three-dimensional digital maps are used to increase the accuracy of vehicle edge detection. The number of vehicles per road section, which is computed from the number of edges of vehicles, is computed and referred to as the vehicle density. Traffic conditions can be assessed from the vehicle density and are considered useful for collecting information on traffic congestion. In this study, we experimentally confirm that congested roads can be extracted from satellite images by our method.

  20. Vegetation Cover Change in Yellowstone National Park Detected Using Landsat Satellite Image Analysis

    Science.gov (United States)

    Potter, Christopher S.

    2015-01-01

    Results from Landsat satellite image analysis since 1987 in all unburned areas (since the 1880s) of Yellowstone National Park (YNP) showed that consistent decreases in the normalized difference vegetation index (NDVI) have been strongly dependent on periodic variations in peak annual snow water equivalents (SWE).

  1. Performance Evaluation of Machine Learning Algorithms for Urban Pattern Recognition from Multi-spectral Satellite Images

    OpenAIRE

    Marc Wieland; Massimiliano Pittore

    2014-01-01

    In this study, a classification and performance evaluation framework for the recognition of urban patterns in medium (Landsat ETM, TM and MSS) and very high resolution (WorldView-2, Quickbird, Ikonos) multi-spectral satellite images is presented. The study aims at exploring the potential of machine learning algorithms in the context of an object-based image analysis and to thoroughly test the algorithm’s performance under varying conditions to optimize their usage for urban pattern recognitio...

  2. Mapping land cover from satellite images: A basic, low cost approach

    Science.gov (United States)

    Elifrits, C. D.; Barney, T. W.; Barr, D. J.; Johannsen, C. J.

    1978-01-01

    Simple, inexpensive methodologies developed for mapping general land cover and land use categories from LANDSAT images are reported. One methodology, a stepwise, interpretive, direct tracing technique was developed through working with university students from different disciplines with no previous experience in satellite image interpretation. The technique results in maps that are very accurate in relation to actual land cover and relative to the small investment in skill, time, and money needed to produce the products.

  3. Extraction of DTM from Satellite Images Using Neural Networks

    OpenAIRE

    Tapper, Gustav

    2016-01-01

    This thesis presents a way to generate a Digital Terrain Model (dtm) from a Digital Surface Model (dsm) and multi spectral images (including the Near Infrared (nir) color band). An Artificial Neural Network (ann) is used to pre-classify the dsm and multi spectral images. This in turn is used to filter the dsm to a dtm. The use of an ann as a classifier provided good results. Additionally, the addition of the nir color band resulted in an improvement of the accuracy of the classifier. Using th...

  4. Magnetic resonance imaging research in sub-Saharan Africa: challenges and satellite-based networking implementation.

    Science.gov (United States)

    Latourette, Matthew T; Siebert, James E; Barto, Robert J; Marable, Kenneth L; Muyepa, Anthony; Hammond, Colleen A; Potchen, Michael J; Kampondeni, Samuel D; Taylor, Terrie E

    2011-08-01

    As part of an NIH-funded study of malaria pathogenesis, a magnetic resonance (MR) imaging research facility was established in Blantyre, Malaŵi to enhance the clinical characterization of pediatric patients with cerebral malaria through application of neurological MR methods. The research program requires daily transmission of MR studies to Michigan State University (MSU) for clinical research interpretation and quantitative post-processing. An intercontinental satellite-based network was implemented for transmission of MR image data in Digital Imaging and Communications in Medicine (DICOM) format, research data collection, project communications, and remote systems administration. Satellite Internet service costs limited the bandwidth to symmetrical 384 kbit/s. DICOM routers deployed at both the Malaŵi MRI facility and MSU manage the end-to-end encrypted compressed data transmission. Network performance between DICOM routers was measured while transmitting both mixed clinical MR studies and synthetic studies. Effective network latency averaged 715 ms. Within a mix of clinical MR studies, the average transmission time for a 256 × 256 image was ~2.25 and ~6.25 s for a 512 × 512 image. Using synthetic studies of 1,000 duplicate images, the interquartile range for 256 × 256 images was [2.30, 2.36] s and [5.94, 6.05] s for 512 × 512 images. Transmission of clinical MRI studies between the DICOM routers averaged 9.35 images per minute, representing an effective channel utilization of ~137% of the 384-kbit/s satellite service as computed using uncompressed image file sizes (including the effects of image compression, protocol overhead, channel latency, etc.). Power unreliability was the primary cause of interrupted operations in the first year, including an outage exceeding 10 days.

  5. Simultaneous hierarchical segmentation and vectorization of satellite images through combined data sampling and anisotropic triangulation

    Energy Technology Data Exchange (ETDEWEB)

    Grazzini, Jacopo [Los Alamos National Laboratory; Prasad, Lakshman [Los Alamos National Laboratory; Dillard, Scott [PNNL

    2010-10-21

    The automatic detection, recognition , and segmentation of object classes in remote sensed images is of crucial importance for scene interpretation and understanding. However, it is a difficult task because of the high variability of satellite data. Indeed, the observed scenes usually exhibit a high degree of complexity, where complexity refers to the large variety of pictorial representations of objects with the same semantic meaning and also to the extensive amount of available det.ails. Therefore, there is still a strong demand for robust techniques for automatic information extraction and interpretation of satellite images. In parallel, there is a growing interest in techniques that can extract vector features directly from such imagery. In this paper, we investigate the problem of automatic hierarchical segmentation and vectorization of multispectral satellite images. We propose a new algorithm composed of the following steps: (i) a non-uniform sampling scheme extracting most salient pixels in the image, (ii) an anisotropic triangulation constrained by the sampled pixels taking into account both strength and directionality of local structures present in the image, (iii) a polygonal grouping scheme merging, through techniques based on perceptual information , the obtained segments to a smaller quantity of superior vectorial objects. Besides its computational efficiency, this approach provides a meaningful polygonal representation for subsequent image analysis and/or interpretation.

  6. Tidal Flooding and Vegetation Patterns in a Salt Marsh Tidal Creek Imaged by Low-altitude Balloon Aerial Photography

    Science.gov (United States)

    White, S. M.; Madsen, E.

    2013-12-01

    soil water content. These other factors are all directly affected by the hydroperiod, creating a complex system of feedbacks. Inundation frequencies show a pronounced relationship to zonation. Creek bank height and the hydroperiod have a curvilinear relationship at low bank heights such that small decreases in creek bank height can result in large increases in inundation frequency. Biological zonation is not simply a result of bank height and inundation frequency, other contributing factors include species competition, adaptability, and groundwater flow. Vegetation patterns delineated by a ground-based GPS survey and image classification from the aerial photos show that not all changes in eco-zonation are a direct function of elevation. Some asymmetry across the creek is observed in plant habitat, and eliminating topography (and thereby tidal inundation) as a factor, we attribute the remaining variability to groundwater flow.

  7. Automatic Detection of Clouds and Shadows Using High Resolution Satellite Image Time Series

    Science.gov (United States)

    Champion, Nicolas

    2016-06-01

    Detecting clouds and their shadows is one of the primaries steps to perform when processing satellite images because they may alter the quality of some products such as large-area orthomosaics. The main goal of this paper is to present the automatic method developed at IGN-France for detecting clouds and shadows in a sequence of satellite images. In our work, surface reflectance orthoimages are used. They were processed from initial satellite images using a dedicated software. The cloud detection step consists of a region-growing algorithm. Seeds are firstly extracted. For that purpose and for each input ortho-image to process, we select the other ortho-images of the sequence that intersect it. The pixels of the input ortho-image are secondly labelled seeds if the difference of reflectance (in the blue channel) with overlapping ortho-images is bigger than a given threshold. Clouds are eventually delineated using a region-growing method based on a radiometric and homogeneity criterion. Regarding the shadow detection, our method is based on the idea that a shadow pixel is darker when comparing to the other images of the time series. The detection is basically composed of three steps. Firstly, we compute a synthetic ortho-image covering the whole study area. Its pixels have a value corresponding to the median value of all input reflectance ortho-images intersecting at that pixel location. Secondly, for each input ortho-image, a pixel is labelled shadows if the difference of reflectance (in the NIR channel) with the synthetic ortho-image is below a given threshold. Eventually, an optional region-growing step may be used to refine the results. Note that pixels labelled clouds during the cloud detection are not used for computing the median value in the first step; additionally, the NIR input data channel is used to perform the shadow detection, because it appeared to better discriminate shadow pixels. The method was tested on times series of Landsat 8 and Pl

  8. Automatic cloud detection for high resolution satellite stereo images and its application in terrain extraction

    Science.gov (United States)

    Wu, Teng; Hu, Xiangyun; Zhang, Yong; Zhang, Lulin; Tao, Pengjie; Lu, Luping

    2016-11-01

    The automatic extraction of terrain from high-resolution satellite optical images is very difficult under cloudy conditions. Therefore, accurate cloud detection is necessary to fully use the cloud-free parts of images for terrain extraction. This paper addresses automated cloud detection by introducing an image matching based method under a stereo vision framework, and the optimization usage of non-cloudy areas in stereo matching and the generation of digital surface models (DSMs). Given that clouds are often separated from the terrain surface, cloudy areas are extracted by integrating dense matching DSM, worldwide digital elevation model (DEM) (i.e., shuttle radar topography mission (SRTM)) and gray information from the images. This process consists of the following steps: an image based DSM is firstly generated through a multiple primitive multi-image matcher. Once it is aligned with the reference DEM based on common features, places with significant height differences between the DSM and the DEM will suggest the potential cloud covers. Detecting cloud at these places in the images then enables precise cloud delineation. In the final step, elevations of the reference DEM within the cloud covers are assigned to the corresponding region of the DSM to generate a cloud-free DEM. The proposed approach is evaluated with the panchromatic images of the Tianhui satellite and has been successfully used in its daily operation. The cloud detection accuracy for images without snow is as high as 95%. Experimental results demonstrate that the proposed method can significantly improve the usage of the cloudy panchromatic satellite images for terrain extraction.

  9. Topic Modelling for Object-Based Unsupervised Classification of VHR Panchromatic Satellite Images Based on Multiscale Image Segmentation

    Directory of Open Access Journals (Sweden)

    Li Shen

    2017-08-01

    Full Text Available Image segmentation is a key prerequisite for object-based classification. However, it is often difficult, or even impossible, to determine a unique optimal segmentation scale due to the fact that various geo-objects, and even an identical geo-object, present at multiple scales in very high resolution (VHR satellite images. To address this problem, this paper presents a novel unsupervised object-based classification for VHR panchromatic satellite images using multiple segmentations via the latent Dirichlet allocation (LDA model. Firstly, multiple segmentation maps of the original satellite image are produced by means of a common multiscale segmentation technique. Then, the LDA model is utilized to learn the grayscale histogram distribution for each geo-object and the mixture distribution of geo-objects within each segment. Thirdly, the histogram distribution of each segment is compared with that of each geo-object using the Kullback-Leibler (KL divergence measure, which is weighted with a constraint specified by the mixture distribution of geo-objects. Each segment is allocated a geo-object category label with the minimum KL divergence. Finally, the final classification map is achieved by integrating the multiple classification results at different scales. Extensive experimental evaluations are designed to compare the performance of our method with those of some state-of-the-art methods for three different types of images. The experimental results over three different types of VHR panchromatic satellite images demonstrate the proposed method is able to achieve scale-adaptive classification results, and improve the ability to differentiate the geo-objects with spectral overlap, such as water and grass, and water and shadow, in terms of both spatial consistency and semantic consistency.

  10. Building identification from very high-resolution satellite images

    Science.gov (United States)

    Lhomme, Stephane

    Urbanisation still remains one of the main problems worldwide. The extent and rapidity of the urban growth induce a number of socio-economic and environmental conflicts everywhere. In order to reduce these problems, urban planners need to integrate spatial information in planning tools. Actually high expectations are made on Very High Spatial Resolution imagery (VHSR). These high-spatial resolution images are available at a reasonable price and due to short revisit periods, they offer a high degree of actuality. However, interpretation methods seem not to be adapted to this new type of images. The aim of our study is to develop a new method for semi-automatic building extraction with VHSR. The different steps performed to achieve our objective are each presented in a chapter. In the first chapter, the general context of our research is described with the definition of our objective. After a short historical review of urbanisation, we focus on urban growth and associated problems. In the following we discuss the possible contributions of geography to reduce these problems. After discussing concepts, theories and methodologies of geographical analysis in urban areas, we present existing general urban planning tools. Finally, we show the special interest of our study that is due to a growing need to integrate spatial information in these decision support tools. In the second chapter we verify the possibility of reaching our objective by analysing the technical characteristics of the images, the noise and the distortions which affect the images. Quality and interpretability of the studied image is analysed in order to show the capacity of these image to represent urban objects as close to reality as possible. The results confirm the potential of VHSR Imagery for urban objects analysis. The third chapter deal with the preliminary steps necessary for the elaboration of our method of building extraction. First, we evaluate the quality of the Sherbrooke Ikonos image

  11. SHADOW DETECTION FROM VERY HIGH RESOLUTON SATELLITE IMAGE USING GRABCUT SEGMENTATION AND RATIO-BAND ALGORITHMS

    Directory of Open Access Journals (Sweden)

    N. M. S. M. Kadhim

    2015-03-01

    Full Text Available Very-High-Resolution (VHR satellite imagery is a powerful source of data for detecting and extracting information about urban constructions. Shadow in the VHR satellite imageries provides vital information on urban construction forms, illumination direction, and the spatial distribution of the objects that can help to further understanding of the built environment. However, to extract shadows, the automated detection of shadows from images must be accurate. This paper reviews current automatic approaches that have been used for shadow detection from VHR satellite images and comprises two main parts. In the first part, shadow concepts are presented in terms of shadow appearance in the VHR satellite imageries, current shadow detection methods, and the usefulness of shadow detection in urban environments. In the second part, we adopted two approaches which are considered current state-of-the-art shadow detection, and segmentation algorithms using WorldView-3 and Quickbird images. In the first approach, the ratios between the NIR and visible bands were computed on a pixel-by-pixel basis, which allows for disambiguation between shadows and dark objects. To obtain an accurate shadow candidate map, we further refine the shadow map after applying the ratio algorithm on the Quickbird image. The second selected approach is the GrabCut segmentation approach for examining its performance in detecting the shadow regions of urban objects using the true colour image from WorldView-3. Further refinement was applied to attain a segmented shadow map. Although the detection of shadow regions is a very difficult task when they are derived from a VHR satellite image that comprises a visible spectrum range (RGB true colour, the results demonstrate that the detection of shadow regions in the WorldView-3 image is a reasonable separation from other objects by applying the GrabCut algorithm. In addition, the derived shadow map from the Quickbird image indicates

  12. Thermal precursors in satellite images of the 1999 eruption of Shishaldin Volcano

    Science.gov (United States)

    Dehn, Jonathan; Dean, Kenneson; Engle, Kevin; Izbekov, Pavel

    2002-07-01

    Shishaldin Volcano, Unimak Island Alaska, began showing signs of thermal unrest in satellite images on 9 February 1999. A thermal anomaly and small steam plume were detected at the summit of the volcano in short-wave thermal infrared AVHRR (advanced very high resolution radiometer) satellite data. This was followed by over 2 months of changes in the observed thermal character of the volcano. Initially, the thermal anomaly was only visible when the satellite passed nearly directly over the volcano, suggesting a hot source deep in the central crater obscured from more oblique satellite passes. The "zenith angle" needed to see the anomaly increased with time, presumably as the thermal source rose within the conduit. Based on this change, an ascent rate of ca. 14 m per day for the thermal source was estimated, until it reached the summit on around 21 March. It is thought that Strombolian activity began around this time. The precursory activity culminated in a sub-Plinian eruption on 19 April, ejecting ash to over 45,000 ft. (13,700 m). The thermal energy output through the precursory period was calculated based on geometric constraints unique to Shishaldin. These calculations show fluctuations that can be tied to changes in the eruptive character inferred from seismic records and later geologic studies. The remote location of this volcano made satellite images a necessary observation tool for this eruption. To date, this is the longest thermal precursory activity preceding a sub-Plinian eruption recorded by satellite images in the region. This type of thermal monitoring of remote volcanoes is central in the efforts of the Alaska Volcano Observatory to provide timely warnings of volcanic eruption, and mitigate their associated hazards to air-traffic and local residents.

  13. Mosaic Algorithm for Aerial Remote Sensing Image and Its Accuracy Evaluation%航空遥感图像拼接算法及其精度评价

    Institute of Scientific and Technical Information of China (English)

    杨彬; 李旭东; 晏磊; 余优生

    2014-01-01

    Small imaging coverage and big parallax angle make aerial remote sensing more difficult to image processing. This paper proposes an algorithm for aerial remote sensing image mosaic. The original image is corrected by perspective transform with estimating matrix which is determined by the relative attitude of two images. It solves the distortion problem caused by big parallax angle. Image registration is approached by the Scale Invariant Feature Transform(SIFT) algorithm and a method based on probability density. The image mosaic process is completed by the wavelet transform. Experimental result shows that the mosaic performance of this algorithm is better than traditional SIFT algorithm in different surface conditions.%针对航空遥感成像范围小、视差角大的特点,提出一种航空遥感图像拼接算法。通过估算图像相对方位矩阵,使用透视变换实现图像校正,解决大视差角下的畸变问题。利用SIFT算法和基于概率密度的错误匹配点剔除方法,实现高精度图像配准,并通过小波变换完成图像融合工作。实验结果表明,在不同地表情况下,该算法的拼接性能均优于传统SIFT算法。

  14. Modifications of the heliostat procedures for irradiance estimates from satellite images

    Energy Technology Data Exchange (ETDEWEB)

    Beyer, H.G.; Costanzo, Claudio; Heinemann, Detlev [Oldenburg Univ. (Germany). Fachbereich 8 - Physik

    1996-03-01

    Images taken by geostationary satellites may be used to estimate solar irradiance fluxes at the earth`s surface. The Heliostat method is a widely applied procedure for this task. It is based on the empirical correlation between a satellite derived cloud index and the irradiance at the ground. Modifications to this procedure that may reduce the temporal variability of the correlation are presented. The modified method may open the way to the use of a generic relation of cloud index and global irradiance. (author)

  15. An Investigation on Water Quality of Darlik Dam Drinking Water using Satellite Images

    Directory of Open Access Journals (Sweden)

    Erhan Alparslan

    2010-01-01

    Full Text Available Darlik Dam supplies 15% of the water demand of Istanbul Metropolitan City of Turkey. Water quality (WQ in the Darlik Dam was investigated from Landsat 5 TM satellite images of the years 2004, 2005, and 2006 in order to determine land use/land cover changes in the watershed of the dam that may deteriorate its WQ. The images were geometrically and atmospherically corrected for WQ analysis. Next, an investigation was made by multiple regression analysis between the unitless planetary reflectance values of the first four bands of the June 2005 Landsat TM image of the dam and WQ parameters, such as chlorophyll-a, total dissolved matter, turbidity, total phosphorous, and total nitrogen, measured at satellite image acquisition time at seven stations in the dam. Finally, WQ in the dam was studied from satellite images of the years 2004, 2005, and 2006 by pattern recognition techniques in order to determine possible water pollution in the dam. This study was compared to a previous study done by the authors in the Küçükçekmece water reservoir, also in Istanbul City.

  16. Fast segmentation of satellite images using SLIC, WebGL and Google Earth Engine

    Science.gov (United States)

    Donchyts, Gennadii; Baart, Fedor; Gorelick, Noel; Eisemann, Elmar; van de Giesen, Nick

    2017-04-01

    Google Earth Engine (GEE) is a parallel geospatial processing platform, which harmonizes access to petabytes of freely available satellite images. It provides a very rich API, allowing development of dedicated algorithms to extract useful geospatial information from these images. At the same time, modern GPUs provide thousands of computing cores, which are mostly not utilized in this context. In the last years, WebGL became a popular and well-supported API, allowing fast image processing directly in web browsers. In this work, we will evaluate the applicability of WebGL to enable fast segmentation of satellite images. A new implementation of a Simple Linear Iterative Clustering (SLIC) algorithm using GPU shaders will be presented. SLIC is a simple and efficient method to decompose an image in visually homogeneous regions. It adapts a k-means clustering approach to generate superpixels efficiently. While this approach will be hard to scale, due to a significant amount of data to be transferred to the client, it should significantly improve exploratory possibilities and simplify development of dedicated algorithms for geoscience applications. Our prototype implementation will be used to improve surface water detection of the reservoirs using multispectral satellite imagery.

  17. Improving Sediment Transport Prediction by Assimilating Satellite Images in a Tidal Bay Model of Hong Kong

    Directory of Open Access Journals (Sweden)

    Peng Zhang

    2014-03-01

    Full Text Available Numerical models being one of the major tools for sediment dynamic studies in complex coastal waters are now benefitting from remote sensing images that are easily available for model inputs. The present study explored various methods of integrating remote sensing ocean color data into a numerical model to improve sediment transport prediction in a tide-dominated bay in Hong Kong, Deep Bay. Two sea surface sediment datasets delineated from satellite images from the Moderate Resolution Imaging Spectra-radiometer (MODIS were assimilated into a coastal ocean model of the bay for one tidal cycle. It was found that remote sensing sediment information enhanced the sediment transport model ability by validating the model results with in situ measurements. Model results showed that root mean square errors of forecast sediment both at the surface layer and the vertical layers from the model with satellite sediment assimilation are reduced by at least 36% over the model without assimilation.

  18. MORPHOLOGICAL PROFILE AND GRANULOMETRIC MAPS IN EXTRACTION OF BUILDINGS IN VHR SATELLITE IMAGES

    Directory of Open Access Journals (Sweden)

    Kupidura Przemysław

    2015-12-01

    Full Text Available The article is focused on the analysis of possibilities of using granulometric analysis methods: the morphological profile, and granulometric maps in detecting buildings on satellite images. It briefly explains the theoretical basis for granulometric analysis of image and compares two methods used in research. Tests were carried out on a fragment of QuickBird satellite scene – pansharpened multispectral image. 8 variants of classification differing in terms of the data and the model of classification were compared. Evaluation of the effectiveness of the different options for classification based on the analysis factor kappa values and omission and commission errors. The results indicate the significant potential of the proposed methods, and analysis of the observed imperfections allows to specify the possible fields of their development

  19. Parameter optimization of image classification techniques to delineate crowns of coppice trees on UltraCam-D aerial imagery in woodlands

    Science.gov (United States)

    Erfanifard, Yousef; Stereńczak, Krzysztof; Behnia, Negin

    2014-01-01

    Estimating the optimal parameters of some classification techniques becomes their negative aspect as it affects their performance for a given dataset and reduces classification accuracy. It was aimed to optimize the combination of effective parameters of support vector machine (SVM), artificial neural network (ANN), and object-based image analysis (OBIA) classification techniques by the Taguchi method. The optimized techniques were applied to delineate crowns of Persian oak coppice trees on UltraCam-D very high spatial resolution aerial imagery in Zagros semiarid woodlands, Iran. The imagery was classified and the maps were assessed by receiver operating characteristic curve and other performance metrics. The results showed that Taguchi is a robust approach to optimize the combination of effective parameters in these image classification techniques. The area under curve (AUC) showed that the optimized OBIA could well discriminate tree crowns on the imagery (AUC=0.897), while SVM and ANN yielded slightly less AUC performances of 0.819 and 0.850, respectively. The indices of accuracy (0.999) and precision (0.999) and performance metrics of specificity (0.999) and sensitivity (0.999) in the optimized OBIA were higher than with other techniques. The optimization of effective parameters of image classification techniques by the Taguchi method, thus, provided encouraging results to discriminate the crowns of Persian oak coppice trees on UltraCam-D aerial imagery in Zagros semiarid woodlands.

  20. Very High Resolution Satellite Image Classification Using Fuzzy Rule-Based Systems

    Directory of Open Access Journals (Sweden)

    Yun Zhang

    2013-11-01

    Full Text Available The aim of this research is to present a detailed step-by-step method for classification of very high resolution urban satellite images (VHRSI into specific classes such as road, building, vegetation, etc., using fuzzy logic. In this study, object-based image analysis is used for image classification. The main problems in high resolution image classification are the uncertainties in the position of object borders in satellite images and also multiplex resemblance of the segments to different classes. In order to solve this problem, fuzzy logic is used for image classification, since it provides the possibility of image analysis using multiple parameters without requiring inclusion of certain thresholds in the class assignment process. In this study, an inclusive semi-automatic method for image classification is offered, which presents the configuration of the related fuzzy functions as well as fuzzy rules. The produced results are compared to the results of a normal classification using the same parameters, but with crisp rules. The overall accuracies and kappa coefficients of the presented method stand higher than the check projects.

  1. Detecting Weather Radar Clutter by Information Fusion With Satellite Images and Numerical Weather Prediction Model Output

    DEFF Research Database (Denmark)

    Bøvith, Thomas; Nielsen, Allan Aasbjerg; Hansen, Lars Kai

    2006-01-01

    A method for detecting clutter in weather radar images by information fusion is presented. Radar data, satellite images, and output from a numerical weather prediction model are combined and the radar echoes are classified using supervised classification. The presented method uses indirect...... information on precipitation in the atmosphere from Meteosat-8 multispectral images and near-surface temperature estimates from the DMI-HIRLAM-S05 numerical weather prediction model. Alternatively, an operational nowcasting product called 'Precipitating Clouds' based on Meteosat-8 input is used. A scale...

  2. Parallel algorithms of relative radiometric correction for images of TH-1 satellite

    Science.gov (United States)

    Wang, Xiang; Zhang, Tingtao; Cheng, Jiasheng; Yang, Tao

    2014-05-01

    The first generation of transitive stereo-metric satellites in China, TH-1 Satellite, is able to gain stereo images of three-line-array with resolution of 5 meters, multispectral images of 10 meters, and panchromatic high resolution images of 2 meters. The procedure between level 0 and level 1A of high resolution images is so called relative radiometric correction (RRC for short). The processing algorithm of high resolution images, with large volumes of data, is complicated and time consuming. In order to bring up the processing speed, people in industry commonly apply parallel processing techniques based on CPU or GPU. This article firstly introduces the whole process and each step of the algorithm - that is in application - of RRC for high resolution images in level 0; secondly, the theory and characteristics of MPI (Message Passing Interface) and OpenMP (Open Multi-Processing) parallel programming techniques is briefly described, as well as the superiority for parallel technique in image processing field; thirdly, aiming at each step of the algorithm in application and based on MPI+OpenMP hybrid paradigm, the parallelizability and the strategies of parallelism for three processing steps: Radiometric Correction, Splicing Pieces of TDICCD (Time Delay Integration Charge-Coupled Device) and Gray Level Adjustment among pieces of TDICCD are deeply discussed, and furthermore, deducts the theoretical acceleration rates of each step and the one of whole procedure, according to the processing styles and independence of calculation; for the step Splicing Pieces of TDICCD, two different strategies of parallelism are proposed, which are to be chosen with consideration of hardware capabilities; finally, series of experiments are carried out to verify the parallel algorithms by applying 2-meter panchromatic high resolution images of TH-1 Satellite, and the experimental results are analyzed. Strictly on the basis of former parallel algorithms, the programs in the experiments

  3. Cuckoo search algorithm based satellite image contrast and brightness enhancement using DWT-SVD.

    Science.gov (United States)

    Bhandari, A K; Soni, V; Kumar, A; Singh, G K

    2014-07-01

    This paper presents a new contrast enhancement approach which is based on Cuckoo Search (CS) algorithm and DWT-SVD for quality improvement of the low contrast satellite images. The input image is decomposed into the four frequency subbands through Discrete Wavelet Transform (DWT), and CS algorithm used to optimize each subband of DWT and then obtains the singular value matrix of the low-low thresholded subband image and finally, it reconstructs the enhanced image by applying IDWT. The singular value matrix employed intensity information of the particular image, and any modification in the singular values changes the intensity of the given image. The experimental results show superiority of the proposed method performance in terms of PSNR, MSE, Mean and Standard Deviation over conventional and state-of-the-art techniques. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

  4. CORRECCION DE ILUMINACION PARA IMAGENES AEREAS DE CULTIVOS TOMADAS A BAJA ALTITUD ILLUMINATION CORRECTION FOR AERIAL AGRICULTURE IMAGES TAKEN AT LOW ALTITUDE

    Directory of Open Access Journals (Sweden)

    Juan Camilo Mejía Ospina

    2007-12-01

    Full Text Available En este trabajo se propone un método para realizar correcciones de color de imágenes aéreas de cultivos tomadas a baja altitud, en diferentes fechas, horas y condiciones de nubosidad. El método no requiere que la escena posea superficies o datos concretos que sirvan de estándar para realizar la corrección sobre la iluminación presente. El está basado en la técnica de constancia de color llamada gamut mapping, a la cual se le han hecho algunas variaciones aprovechando las características propias de las imágenes aéreas. El método permite, para imágenes capturadas bajo diferentes condiciones de iluminación, su normalización de modo que sean comparables entre ellas. En particular se aplicó a plantaciones de banano, pero puede ser extendido a cualquier tipo de cultivo, siempre que se utilice fotografía aérea de baja altitud. Se probó y validó usando imágenes simuladas debido a las dificultades encontradas durante la etapa de adquisición de imágenes reales.A new method is presented in order to carry out the color corrections of aerial agriculture images taken at low altitude, in different dates, times and conditions of cloudiness. The method does not require scene targets or specific data in order to develop the correction of the actual illumination. It is based on the constancy of color technique called gamut mapping, which was adapted to take advantage of the own characteristics of the aerial images. For images captured under different conditions of illumination, the method allows its normalization for comparison purposes. It was applied to banana plantations, but it can be extended to any type of agriculture images, registered by aerial images at low altitude. It was proven and validated using synthetic images because of some difficulties found during the acquisition stage of real images.

  5. Modeling of solar irradiance using satellite images and direct terrestrial measurements with PV modules

    Science.gov (United States)

    Tyukhov, Igor; Schakhramanyan, Michael; Strebkov, Dmitry; Tikhonov, Anton; Vignola, Frank

    2009-08-01

    A simple, affordable and efficient multifaceted system with technical software programs, "Kosmos 3M", was developed for taking images of the Earth from NOAA satellites and for handling this images and analyzing many geographical and meteorological parameters. Technical software programs have been developed that utilize the "Kosmos 3M" Receiver system. Basic capabilities of the multifaceted "Kosmos 3M" system include: receiving signal from NOAA satellites; digital processing of space images with geographical fixing, superposition of maps of cities and coordinate grid; finding of geographical coordinates at any point of space image; finding of temperature of underlying surface at given points; finding of albedo (reflection coefficient) at any point of space image; finding of upper boundary of clouds (cloudiness); forecasting of dangerous weather phenomena; defining wind fields in cyclones; precipitations forecast; measuring distances between given points; measuring surfaces (areas); and forming of electronic library of images of the Earth. Work is underway to use the "Kosmos 3M" cloudiness images to estimate the incident solar radiation values for evaluating terrestrial solar energy performance in real time. Such kind of system would have a wide variety of uses from the classroom to the field.

  6. Determination of the Impact of Urbanization on Agricultural Lands using Multi-temporal Satellite Sensor Images

    Science.gov (United States)

    Kaya, S.; Alganci, U.; Sertel, E.; Ustundag, B.

    2015-12-01

    Throughout the history, agricultural activities have been performed close to urban areas. Main reason behind this phenomenon is the need of fast marketing of the agricultural production to urban residents and financial provision. Thus, using the areas nearby cities for agricultural activities brings out advantage of easy transportation of productions and fast marketing. For decades, heavy migration to cities has directly and negatively affected natural grasslands, forests and agricultural lands. This pressure has caused agricultural lands to be changed into urban areas. Dense urbanization causes increase in impervious surfaces, heat islands and many other problems in addition to destruction of agricultural lands. Considering the negative impacts of urbanization on agricultural lands and natural resources, a periodic monitoring of these changes becomes indisputably important. At this point, satellite images are known to be good data sources for land cover / use change monitoring with their fast data acquisition, large area coverages and temporal resolution properties. Classification of the satellite images provides thematic the land cover / use maps of the earth surface and changes can be determined with GIS based analysis multi-temporal maps. In this study, effects of heavy urbanization over agricultural lands in Istanbul, metropolitan city of Turkey, were investigated with use of multi-temporal Landsat TM satellite images acquired between 1984 and 2011. Images were geometrically registered to each other and classified using supervised maximum likelihood classification algorithm. Resulting thematic maps were exported to GIS environment and destructed agricultural lands by urbanization were determined using spatial analysis.

  7. Direct Geolocation of Satellite Images with the EO-CFI Libraries

    Science.gov (United States)

    de Miguel, Eduardo; Prado, Elena; Estebanez, Monica; Martin, Ana I.; Gonzalez, Malena

    2016-08-01

    The INTA Remote Sensing Laboratory has implemented a tool for the direct geolocation of satellite images. The core of the tool is a C code based on the "Earth Observation Mission CFI SW" from ESA. The tool accepts different types of inputs for satellite attitude (euler angles, quaternions, default attitude models). Satellite position can be provided either in ECEF or ECI coordinates. The line of sight of each individual detector is imported from an external file or is generated by the tool from camera parameters. Global DEM ACE2 is used to define ground intersection of the LOS.The tool has been already tailored for georeferencing images from the forthcoming Spanish Earth Observation mission SEOSat/Ingenio, and for the camera APIS onboard the INTA cubesat OPTOS. The next step is to configure it for the geolocation of Sentinel 2 L1b images.The tool has been internally validated by different means. This validation shows that the tool is suitable for georeferencing images from high spatial resolution missions. As part of the validation efforts, a code for simulating orbital info for LEO missions using EO-CFI has been produced.

  8. Detection of Burn Area and Severity with MODIS Satellite Images and Spatial Autocorrelation Techniques

    Science.gov (United States)

    Kaya, S.; Kavzoglu, T.; Tonbul, H.

    2014-12-01

    Effects of forest fires and implications are one of the most important natural disasters all over the world. Statistical data observed that forest fires had a variable structure in the last century in Turkey, but correspondingly the population growth amount of forest fires and burn area increase widely in recent years. Depending on this, erosion, landslides, desertification and mass loss come into existence. In addition; after forest fires, renewal of forests and vegetation are very important for land management. Classic methods used for detection of burn area and severity requires a long and challenging process due to time and cost factors. Thanks to advanced techniques used in the field of Remote Sensing, burn area and severity can be determined with high detail and precision. The purpose of this study based on blending MODIS (Moderate Resolution Imaging Spectradiometer) satellite images and spatial autocorrelation techniques together, thus detect burn area and severity absolutely. In this context, spatial autocorrelation statistics like Moran's I and Get is-Ord Local Gi indexes were used to measure and analyze to burned area characteristics. Prefire and postfire satellite images were used to determine fire severity depending on spectral indexes corresponding to biomass loss and carbon emissivity intensities. Satellite images have used for identification of fire damages and risks in terms of fire management for a long time. This study was performed using prefire and postfire satellite images and spatial autocorrelation techniques to determining and analyzing forest fires in Antalya, Turkey region which serious fires occurred. In this context, this approach enables the characterization of distinctive texture of burned area and helps forecasting more precisely. Finally, it is observed that mapping of burned area and severity could be performed from local scale to national scale. Key Words: Spatial autocorrelation, MODIS, Fire, Burn Severity

  9. Foodstuff Survey Around a Major Nuclear Facility with Test of Satellite Image Application

    Energy Technology Data Exchange (ETDEWEB)

    Fledderman, P.D.

    1999-07-16

    'A foodstuff survey was performed around the Savannah River Site, Aiken SC. It included a census of buildings and fields within 5 km of the boundary and determination of the locations and amounts of crops grown within 80 km of SRS center. Recent information for this region was collected on the amounts of meat, poultry, milk, and eggs produced, of deer hunted, and of sports fish caught. The locations and areas devoted to growing each crop were determined in two ways: by the usual process of assuming uniform crop distribution in each county on the basis of agricultural statistics reported by state agencies, and by analysis of two LANDSAT TM images obtained in May and September. For use with environmental radionuclide transfer and radiation dose calculation codes, locations within 80 km were defined for 64 sections by 16 sectors centered on the Site and by 16-km distance intervals from 16 km to 80 km. Most locally-raised foodstuff was distributed regionally and not retained locally for consumption. For four food crops, the amounts per section based on county agricultural statistics prorated by area were compared with the amounts per section based on satellite image analysis. The median ratios of the former to the latter were 0.6 - 0.7, suggesting that the two approaches are comparable but that satellite image analysis gave consistently higher amounts. Use of satellite image analysis is recommended on the basis of these findings to obtain site-specific, as compared to area-averaged, information on crop locations in conjunction with radionuclide pathway modelling. Some improvements in technique are suggested for satellite image application to characterize additional crops.'

  10. Image and Processing Models for Satellite Detection in Images Acquired by Space-based Surveillance-of-Space Sensors

    Science.gov (United States)

    2010-09-01

    software. Résumé …..... Dans le cadre de la surveillance de l’espace, les objets spatiaux connus en orbite (OSO), i.e., satellites actifs ou débris...SAPPHIRE et NEOSSat. Ce document contient des modèles qui décrivent la formation des images et le processus d’acquisition de capteurs , basés au sol ou dans

  11. Contrail frequency over Europe from NOAA-satellite images

    Directory of Open Access Journals (Sweden)

    V. Gayler

    Full Text Available Contrail cloudiness over Europe and the eastern part of the North Atlantic Ocean was analyzed for the two periods September 1979 - December 1981 and September 1989 - August 1992 by visual inspection of quicklook photographic prints of NOAA/AVHRR infrared images. The averaged contrail cover exhibits maximum values along the transatlantic flight corridor around 50 °N (of almost 2% and over western Europe resulting in 0.5% contrail cloudiness on average. A strong yearly cycle appears with a maximum (<2% in spring and summer over the Atlantic and a smaller maximum (<1% in winter over southwestern Europe. Comparing the two time periods, which are separated by one decade, shows there is a significant decrease in contrail cloudiness over western Europe and a significant increase over the North Atlantic between March and July. Contrail cloud cover during daytime is about twice as high as during nighttime. Contrails are found preferentially in larger fields of 1000 km diameter which usually last for more than a day. Causes, possible errors and consequences are discussed.

  12. Contrail frequency over Europe from NOAA-satellite images

    Science.gov (United States)

    Bakan, S.; Betancor, M.; Gayler, V.; Graßl, H.

    1994-10-01

    Contrail cloudiness over Europe and the eastern part of the North Atlantic Ocean was analyzed for the two periods September 1979 - December 1981 and September 1989 - August 1992 by visual inspection of quicklook photographic prints of NOAA/AVHRR infrared images. The averaged contrail cover exhibits maximum values along the transatlantic flight corridor around 50 °N (of almost 2%) and over western Europe resulting in 0.5% contrail cloudiness on average. A strong yearly cycle appears with a maximum (<2%) in spring and summer over the Atlantic and a smaller maximum (<1%) in winter over southwestern Europe. Comparing the two time periods, which are separated by one decade, shows there is a significant decrease in contrail cloudiness over western Europe and a significant increase over the North Atlantic between March and July. Contrail cloud cover during daytime is about twice as high as during nighttime. Contrails are found preferentially in larger fields of 1000 km diameter which usually last for more than a day. Causes, possible errors and consequences are discussed.

  13. Retrospective 70 y-spatial analysis of repeated vine mortality patterns using ancient aerial time series, Pléiades images and multi-source spatial and field data

    Science.gov (United States)

    Vaudour, E.; Leclercq, L.; Gilliot, J. M.; Chaignon, B.

    2017-06-01

    For any wine estate, there is a need to demarcate homogeneous within-vineyard zones ('terroirs') so as to manage grape production, which depends on vine biological condition. Until now, the studies performing digital zoning of terroirs have relied on recent spatial data and scant attention has been paid to ancient geoinformation likely to retrace past biological condition of vines and especially occurrence of vine mortality. Is vine mortality characterized by recurrent and specific patterns and if so, are these patterns related to terroir units and/or past landuse? This study aimed at performing a historical and spatial tracing of vine mortality patterns using a long time-series of aerial survey images (1947-2010), in combination with recent data: soil apparent electrical conductivity EM38 measurements, very high resolution Pléiades satellite images, and a detailed field survey. Within a 6 ha-estate in the Southern Rhone Valley, landuse and planting history were retraced and the map of missing vines frequency was constructed from the whole time series including a 2015-Pléiades panchromatic band. Within-field terroir units were obtained from a support vector machine classifier computed on the spectral bands and NDVI of Pléiades images, EM38 data and morphometric data. Repeated spatial patterns of missing vines were highlighted throughout several plantings, uprootings, and vine replacements, and appeared to match some within-field terroir units, being explained by their specific soil characteristics, vine/soil management choices and the past landuse of the 1940s. Missing vines frequency was spatially correlated with topsoil CaCO3 content, and negatively correlated with topsoil iron, clay, total N, organic C contents and NDVI. A retrospective spatio-temporal assessment of terroir therefore brings a renewed focus on some key parameters for maintaining a sustainable grape production.

  14. Fusion between Satellite and Geophysical images in the study of Archaeological Sites

    Science.gov (United States)

    Karamitrou, A. A.; Tsokas, G. N.; Petrou, M.; Maggidis, C.

    2012-12-01

    In this work various image fusion techniques are used between one satellite (Quickbird) and one geophysical (electric resistivity) image to create various combinations with higher information content than the two original images independently. The resultant images provide more information about possible buried archaeological relics. The examined archaeological area is located in mainland Greece near the city of Boetia at the acropolis of Gla. The acropolis was built on a flat-topped bedrock outcrop at the north-eastern edge of the Kopais basin. When Kopais was filled with water, Glas was emerging as an island. At the end of 14th century the two palaces of Thebes and Orchomenos jointly utilized a large scale engineering project in order to transform the Kopais basin into a fertile plain. They used the acropolis to monitor the project, and as a warehouse to storage the harvest. To examine the Acropolis for potential archaeological remnants we use one Quickbird satellite image that covers the surrounding area of Gla. The satellite image includes one panchromatic (8532x8528 pixels) and one multispectral (2133x2132 pixels) image, collected on 30th of August 2011, covering an area of 20 square kilometers. On the other hand, geophysical measurements were performed using the electric resistivity method to the south west part of the Acropolis. To combine these images we investigate mean-value fusion, wavelets fusion, and curvelet fusion. In the cases of wavelet and curvelet fusion we apply as the fusion criterion the maximum frequency rule. Furthermore, the two original images, and excavations near the area suggest that the dominant orientations of the buried features are north-south and east-west. Therefore, in curvelet fusion method, in curvelet domain we enhance the image details along these specific orientations, additionally to the fusion. The resultant fused images succeed to map linear and rectangular features that were not easily visible in the original images

  15. Combined aerial and terrestrial images for complete 3D documentation of Singosari Temple based on Structure from Motion algorithm

    Science.gov (United States)

    Hidayat, Husnul; Cahyono, A. B.

    2016-11-01

    Singosaritemple is one of cultural heritage building in East Java, Indonesia which was built in 1300s and restorated in 1934-1937. Because of its history and importance, complete documentation of this temple is required. Nowadays with the advent of low cost UAVs combining aerial photography with terrestrial photogrammetry gives more complete data for 3D documentation. This research aims to make complete 3D model of this landmark from aerial and terrestrial photographs with Structure from Motion algorithm. To establish correct scale, position, and orientation, the final 3D model was georeferenced with Ground Control Points in UTM 49S coordinate system. The result shows that all facades, floor, and upper structures can be modeled completely in 3D. In terms of 3D coordinate accuracy, the Root Mean Square Errors (RMSEs) are RMSEx=0,041 m; RMSEy=0,031 m; RMSEz=0,049 m which represent 0.071 m displacement in 3D space. In addition the mean difference of lenght measurements of the object is 0,057 m. With this accuracy, this method can be used to map the site up to 1:237 scale. Although the accuracy level is still in centimeters, the combined aerial and terrestrial photographs with Structure from Motion algorithm can provide complete and visually interesting 3D model.

  16. Adapting astronomical source detection software to help detect animals in thermal images obtained by unmanned aerial systems

    Science.gov (United States)

    Longmore, S. N.; Collins, R. P.; Pfeifer, S.; Fox, S. E.; Mulero-Pazmany, M.; Bezombes, F.; Goodwind, A.; de Juan Ovelar, M.; Knapen, J. H.; Wich, S. A.

    2017-02-01

    In this paper we describe an unmanned aerial system equipped with a thermal-infrared camera and software pipeline that we have developed to monitor animal populations for conservation purposes. Taking a multi-disciplinary approach to tackle this problem, we use freely available astronomical source detection software and the associated expertise of astronomers, to efficiently and reliably detect humans and animals in aerial thermal-infrared footage. Combining this astronomical detection software with existing machine learning algorithms into a single, automated, end-to-end pipeline, we test the software using aerial video footage taken in a controlled, field-like environment. We demonstrate that the pipeline works reliably and describe how it can be used to estimate the completeness of different observational datasets to objects of a given type as a function of height, observing conditions etc. - a crucial step in converting video footage to scientifically useful information such as the spatial distribution and density of different animal species. Finally, having demonstrated the potential utility of the system, we describe the steps we are taking to adapt the system for work in the field, in particular systematic monitoring of endangered species at National Parks around the world.

  17. Persistent scatterers detection on synthetic aperture radar images acquired by Sentinel-1 satellite

    Science.gov (United States)

    Dǎnişor, Cosmin; Popescu, Anca; Datcu, Mihai

    2016-12-01

    Persistent Scatterers Interferometry (PS-InSAR) has become a popular method in remote sensing because of its capability to measure terrain deformations with very high accuracy. It relies on multiple Synthetic Aperture Radar (SAR) acquisitions, to monitor points with stable proprieties over time, called Persistent Scatterers (PS)[1]. These points are unaffected by temporal decorrelation, therefore by analyzing their interferometric phase variation we can estimate the scene's deformation rates within a given time interval. In this work, we apply two incoherent detection algorithms to identify Persistent Scatterers candidates in the city of Focșani, Romania. The first method studies the variation of targets' intensities along the SAR acquisitions and the second method analyzes the spectral proprieties of the scatterers. The algorithms were implemented on a dataset containing 11 complex images of the region covering Buzău, Brăila and Focșani cities. Images were acquired by Sentinel-1 satellite in a time span of 5 months, from October 2014 to February 2015. The processing chain follows the requirements imposed by the new C-band SAR images delivered by the Sentinel-1 satellite (launched in April 2014) imaging in Interferometric Wide (IW) mode. Considering the particularities of the TOPS (Terrain Observation with Progressive Scans in Azimuth) imaging mode[2], special requirements had to be considered for pre-processing steps. The PS detection algorithms were implemented in Gamma RS program, a software which contains various function packages dedicated to SAR images focalization, analysis and processing.

  18. A data mining based approach to predict spatiotemporal changes in satellite images

    Science.gov (United States)

    Boulila, W.; Farah, I. R.; Ettabaa, K. Saheb; Solaiman, B.; Ghézala, H. Ben

    2011-06-01

    The interpretation of remotely sensed images in a spatiotemporal context is becoming a valuable research topic. However, the constant growth of data volume in remote sensing imaging makes reaching conclusions based on collected data a challenging task. Recently, data mining appears to be a promising research field leading to several interesting discoveries in various areas such as marketing, surveillance, fraud detection and scientific discovery. By integrating data mining and image interpretation techniques, accurate and relevant information (i.e. functional relation between observed parcels and a set of informational contents) can be automatically elicited. This study presents a new approach to predict spatiotemporal changes in satellite image databases. The proposed method exploits fuzzy sets and data mining concepts to build predictions and decisions for several remote sensing fields. It takes into account imperfections related to the spatiotemporal mining process in order to provide more accurate and reliable information about land cover changes in satellite images. The proposed approach is validated using SPOT images representing the Saint-Denis region, capital of Reunion Island. Results show good performances of the proposed framework in predicting change for the urban zone.

  19. Effects of rubber shock absorber on the flywheel micro vibration in the satellite imaging system

    Science.gov (United States)

    Deng, Changcheng; Mu, Deqiang; Jia, Xuezhi; Li, Zongxuan

    2016-12-01

    When a satellite is in orbit, its flywheel will generate micro vibration and affect the imaging quality of the camera. In order to reduce this effect, a rubber shock absorber is used, and a numerical model and an experimental setup are developed to investigate its effect on the micro vibration in the study. An integrated model is developed for the system, and a ray tracing method is used in the modeling. The spot coordinates and displacements of the image plane are obtained, and the modulate transfer function (MTF) of the system is calculated. A satellite including a rubber shock absorber is designed, and the experiments are carried out. Both simulation and experiments results show that the MTF increases almost 10 %, suggesting the rubber shock absorber is useful to decrease the flywheel vibration.