SAR Image Classification Based on Its Texture Features

Institute of Scientific and Technical Information of China (English)

LI Pingxiang; FANG Shenghui

2003-01-01

SAR images not only have the characteristics of all-ay, all-eather, but also provide object information which is different from visible and infrared sensors. However, SAR images have some faults, such as more speckles and fewer bands. The authors conducted the experiments of texture statistics analysis on SAR image features in order to improve the accuracy of SAR image interpretation.It is found that the texture analysis is an effective method for improving the accuracy of the SAR image interpretation.

Image based SAR product simulation for analysis

Science.gov (United States)

Domik, G.; Leberl, F.

1987-01-01

SAR product simulation serves to predict SAR image gray values for various flight paths. Input typically consists of a digital elevation model and backscatter curves. A new method is described of product simulation that employs also a real SAR input image for image simulation. This can be denoted as 'image-based simulation'. Different methods to perform this SAR prediction are presented and advantages and disadvantages discussed. Ascending and descending orbit images from NASA's SIR-B experiment were used for verification of the concept: input images from ascending orbits were converted into images from a descending orbit; the results are compared to the available real imagery to verify that the prediction technique produces meaningful image data.

Convolutional Neural Networks for SAR Image Segmentation

DEFF Research Database (Denmark)

Malmgren-Hansen, David; Nobel-Jørgensen, Morten

2015-01-01

Segmentation of Synthetic Aperture Radar (SAR) images has several uses, but it is a difficult task due to a number of properties related to SAR images. In this article we show how Convolutional Neural Networks (CNNs) can easily be trained for SAR image segmentation with good results. Besides...

Foliar application of systemic acquired resistance (SAR) inducers for ...

African Journals Online (AJOL)

nbuensanteai

2013-08-14

Aug 14, 2013 ... induced by chitosan and BTH were involved in defense mechanism, reflecting the strong direct positive effect that chitosan ... to control plant diseases based on the systemic acquired resistance ... salicylic acid (SA) as a signal molecule and is associated ... treated plants for SAR relating chemical analyses.

Advanced Interferometric Synthetic Aperture Imaging Radar (InSAR) for Dune Mapping

Science.gov (United States)

Havivi, Shiran; Amir, Doron; Schvartzman, Ilan; August, Yitzhak; Mamman, Shimrit; Rotman, Stanely R.; Blumberg, Dan G.

2016-04-01

Aeolian morphologies are formed in the presence of sufficient wind energy and available lose particles. These processes occur naturally or are further enhanced or reduced by human intervention. The dimensions of change are dependent primarily on the wind energy and surface properties. Since the 1970s, remote sensing imagery, both optical and radar, have been used for documentation and interpretation of the geomorphologic changes of sand dunes. Remote sensing studies of aeolian morphologies is mostly useful to document major changes, yet, subtle changes, occurring in a period of days or months in scales of centimeters, are very difficult to detect in imagery. Interferometric Synthetic Aperture Radar (InSAR) is an imaging technique for measuring Earth's surface topography and deformation. InSAR images are produced by measuring the radar phase difference between two separated antennas that view the same surface area. Classical InSAR is based on high coherence between two or more images. The output (interferogram) can show subtle changes with an accuracy of several millimeters to centimeters. Very little work has been done on measuring or identifying the changes in dunes using InSAR methods. The reason is that dunes tend to be less coherent than firm, stable, surfaces. This work aims to demonstrate how interferometric decorrelation can be used for identifying dune instability. We hypothesize and demonstrate that the loss of radar coherence over time on dunes can be used as an indication of the dune's instability. When SAR images are acquired at sufficiently close intervals one can measure the time it takes to lose coherence and associate this time with geomorphic stability. To achieve our goals, the coherence change detection method was used, in order to identify dune stability or instability and the dune activity level. The Nitzanim-Ashdod coastal dunes along the Mediterranean, 40 km south of Tel-Aviv, Israel, were chosen as a case study. The dunes in this area are of

Target discrimination method for SAR images based on semisupervised co-training

Science.gov (United States)

Wang, Yan; Du, Lan; Dai, Hui

2018-01-01

Synthetic aperture radar (SAR) target discrimination is usually performed in a supervised manner. However, supervised methods for SAR target discrimination may need lots of labeled training samples, whose acquirement is costly, time consuming, and sometimes impossible. This paper proposes an SAR target discrimination method based on semisupervised co-training, which utilizes a limited number of labeled samples and an abundant number of unlabeled samples. First, Lincoln features, widely used in SAR target discrimination, are extracted from the training samples and partitioned into two sets according to their physical meanings. Second, two support vector machine classifiers are iteratively co-trained with the extracted two feature sets based on the co-training algorithm. Finally, the trained classifiers are exploited to classify the test data. The experimental results on real SAR images data not only validate the effectiveness of the proposed method compared with the traditional supervised methods, but also demonstrate the superiority of co-training over self-training, which only uses one feature set.

The artificial object detection and current velocity measurement using SAR ocean surface images

Science.gov (United States)

Alpatov, Boris; Strotov, Valery; Ershov, Maksim; Muraviev, Vadim; Feldman, Alexander; Smirnov, Sergey

2017-10-01

Due to the fact that water surface covers wide areas, remote sensing is the most appropriate way of getting information about ocean environment for vessel tracking, security purposes, ecological studies and others. Processing of synthetic aperture radar (SAR) images is extensively used for control and monitoring of the ocean surface. Image data can be acquired from Earth observation satellites, such as TerraSAR-X, ERS, and COSMO-SkyMed. Thus, SAR image processing can be used to solve many problems arising in this field of research. This paper discusses some of them including ship detection, oil pollution control and ocean currents mapping. Due to complexity of the problem several specialized algorithm are necessary to develop. The oil spill detection algorithm consists of the following main steps: image preprocessing, detection of dark areas, parameter extraction and classification. The ship detection algorithm consists of the following main steps: prescreening, land masking, image segmentation combined with parameter measurement, ship orientation estimation and object discrimination. The proposed approach to ocean currents mapping is based on Doppler's law. The results of computer modeling on real SAR images are presented. Based on these results it is concluded that the proposed approaches can be used in maritime applications.

Investigation of Joint Visibility Between SAR and Optical Images of Urban Environments

Science.gov (United States)

Hughes, L. H.; Auer, S.; Schmitt, M.

2018-05-01

In this paper, we present a work-flow to investigate the joint visibility between very-high-resolution SAR and optical images of urban scenes. For this task, we extend the simulation framework SimGeoI to enable a simulation of individual pixels rather than complete images. Using the extended SimGeoI simulator, we carry out a case study using a TerraSAR-X staring spotlight image and a Worldview-2 panchromatic image acquired over the city of Munich, Germany. The results of this study indicate that about 55 % of the scene are visible in both images and are thus suitable for matching and data fusion endeavours, while about 25 % of the scene are affected by either radar shadow or optical occlusion. Taking the image acquisition parameters into account, our findings can provide support regarding the definition of upper bounds for image fusion tasks, as well as help to improve acquisition planning with respect to different application goals.

Imaging in severe acute respiratory syndrome (SARS)

International Nuclear Information System (INIS)

Antonio, G.E.; Wong, K.T.; Chu, W.C.W.; Hui, D.S.C.; Cheng, F.W.T.; Yuen, E.H.Y.; Chung, S.S.C.; Fok, T.F.; Sung, J.J.Y.; Ahuja, A.T.

2003-01-01

Severe acute respiratory syndrome (SARS) is a highly infectious disease caused by a novel coronavirus, and has become pandemic within a short period of time. Imaging plays an important role in the diagnosis, management and follow-up of patients with SARS. The current status of imaging in SARS is presented in this review

Classification of agricultural fields using time series of dual polarimetry TerraSAR-X images

Directory of Open Access Journals (Sweden)

S. Mirzaee

2014-10-01

Full Text Available Due to its special imaging characteristics, Synthetic Aperture Radar (SAR has become an important source of information for a variety of remote sensing applications dealing with environmental changes. SAR images contain information about both phase and intensity in different polarization modes, making them sensitive to geometrical structure and physical properties of the targets such as dielectric and plant water content. In this study we investigate multi temporal changes occurring to different crop types due to phenological changes using high-resolution TerraSAR-X imagers. The dataset includes 17 dual-polarimetry TSX data acquired from June 2012 to August 2013 in Lorestan province, Iran. Several features are extracted from polarized data and classified using support vector machine (SVM classifier. Training samples and different features employed in classification are also assessed in the study. Results show a satisfactory accuracy for classification which is about 0.91 in kappa coefficient.

Attribute Learning for SAR Image Classification

Directory of Open Access Journals (Sweden)

Chu He

2017-04-01

Full Text Available This paper presents a classification approach based on attribute learning for high spatial resolution Synthetic Aperture Radar (SAR images. To explore the representative and discriminative attributes of SAR images, first, an iterative unsupervised algorithm is designed to cluster in the low-level feature space, where the maximum edge response and the ratio of mean-to-variance are included; a cross-validation step is applied to prevent overfitting. Second, the most discriminative clustering centers are sorted out to construct an attribute dictionary. By resorting to the attribute dictionary, a representation vector describing certain categories in the SAR image can be generated, which in turn is used to perform the classifying task. The experiments conducted on TerraSAR-X images indicate that those learned attributes have strong visual semantics, which are characterized by bright and dark spots, stripes, or their combinations. The classification method based on these learned attributes achieves better results.

Relevant Scatterers Characterization in SAR Images

Science.gov (United States)

Chaabouni, Houda; Datcu, Mihai

2006-11-01

Recognizing scenes in a single look meter resolution Synthetic Aperture Radar (SAR) images, requires the capability to identify relevant signal signatures in condition of variable image acquisition geometry, arbitrary objects poses and configurations. Among the methods to detect relevant scatterers in SAR images, we can mention the internal coherence. The SAR spectrum splitted in azimuth generates a series of images which preserve high coherence only for particular object scattering. The detection of relevant scatterers can be done by correlation study or Independent Component Analysis (ICA) methods. The present article deals with the state of the art for SAR internal correlation analysis and proposes further extensions using elements of inference based on information theory applied to complex valued signals. The set of azimuth looks images is analyzed using mutual information measures and an equivalent channel capacity is derived. The localization of the "target" requires analysis in a small image window, thus resulting in imprecise estimation of the second order statistics of the signal. For a better precision, a Hausdorff measure is introduced. The method is applied to detect and characterize relevant objects in urban areas.

Mapping Winter Wheat with Multi-Temporal SAR and Optical Images in an Urban Agricultural Region.

Science.gov (United States)

Zhou, Tao; Pan, Jianjun; Zhang, Peiyu; Wei, Shanbao; Han, Tao

2017-05-25

Winter wheat is the second largest food crop in China. It is important to obtain reliable winter wheat acreage to guarantee the food security for the most populous country in the world. This paper focuses on assessing the feasibility of in-season winter wheat mapping and investigating potential classification improvement by using SAR (Synthetic Aperture Radar) images, optical images, and the integration of both types of data in urban agricultural regions with complex planting structures in Southern China. Both SAR (Sentinel-1A) and optical (Landsat-8) data were acquired, and classification using different combinations of Sentinel-1A-derived information and optical images was performed using a support vector machine (SVM) and a random forest (RF) method. The interference coherence and texture images were obtained and used to assess the effect of adding them to the backscatter intensity images on the classification accuracy. The results showed that the use of four Sentinel-1A images acquired before the jointing period of winter wheat can provide satisfactory winter wheat classification accuracy, with an F1 measure of 87.89%. The combination of SAR and optical images for winter wheat mapping achieved the best F1 measure-up to 98.06%. The SVM was superior to RF in terms of the overall accuracy and the kappa coefficient, and was faster than RF, while the RF classifier was slightly better than SVM in terms of the F1 measure. In addition, the classification accuracy can be effectively improved by adding the texture and coherence images to the backscatter intensity data.

Precision Rectification of Airborne SAR Image

DEFF Research Database (Denmark)

Dall, Jørgen; Liao, M.; Zhang, Zhe

1997-01-01

A simple and direct procedure for the rectification of a certain class of airborne SAR data is presented. The relief displacements of SAR data are effectively removed by means of a digital elevation model and the image is transformed to the ground coordinate system. SAR data from the Danish EMISAR...

Use of SAR data for proliferation monitoring

International Nuclear Information System (INIS)

Lafitte, M.; Robin, J.P.

2013-01-01

Synthetic Aperture Radar (SAR) is an active and coherent system. SAR images are complex data which contain both amplitude and phase information. The analysis of single SAR data required a very good experience and a good understanding of SAR geometry regarding layover, shadowing, texture and speckle. Image analyst can depicts and describes most of the facilities related to nuclear proliferation and weapons of mass destruction (WMD). The Amplitude Change Detection (ACD) technique consists of a combination of two or three SAR amplitude data acquired with similar orbit and frequency parameters on different dates. That technique provides a very good overview of the changes and particularly regarding vehicles activity and constructions ongoing within the area of interest over the monitoring period. One of the particularities of the SAR systems is to be coherent. The phase of a single image is not exploitable. Thus when two or more SAR data have been acquired with identical orbit and frequency parameters, the phases shift are indicators of changes such as structural changes, terrain subsidence or motion. The Multi-Temporal Coherence (MTC) product merged the two type of information previously detailed: the ACD and coherence analysis. It consists of the combination of two amplitude images and the corresponding coherence computed image. The MTC image may highlights changes between two states of a target which on the ACD analysis appeared unchanged. EUSC uses the difference interferometry techniques in order to estimate volumes that have changed between two acquisition dates. The paper is followed by the slides of the presentation. (A.C.)

Analysis of the fractal dimension of volcano geomorphology through Synthetic Aperture Radar (SAR) amplitude images acquired in C and X band.

Science.gov (United States)

Pepe, S.; Di Martino, G.; Iodice, A.; Manzo, M.; Pepe, A.; Riccio, D.; Ruello, G.; Sansosti, E.; Tizzani, P.; Zinno, I.

2012-04-01

In the last two decades several aspects relevant to volcanic activity have been analyzed in terms of fractal parameters that effectively describe natural objects geometry. More specifically, these researches have been aimed at the identification of (1) the power laws that governed the magma fragmentation processes, (2) the energy of explosive eruptions, and (3) the distribution of the associated earthquakes. In this paper, the study of volcano morphology via satellite images is dealt with; in particular, we use the complete forward model developed by some of the authors (Di Martino et al., 2012) that links the stochastic characterization of amplitude Synthetic Aperture Radar (SAR) images to the fractal dimension of the imaged surfaces, modelled via fractional Brownian motion (fBm) processes. Based on the inversion of such a model, a SAR image post-processing has been implemented (Di Martino et al., 2010), that allows retrieving the fractal dimension of the observed surfaces, dictating the distribution of the roughness over different spatial scales. The fractal dimension of volcanic structures has been related to the specific nature of materials and to the effects of active geodynamic processes. Hence, the possibility to estimate the fractal dimension from a single amplitude-only SAR image is of fundamental importance for the characterization of volcano structures and, moreover, can be very helpful for monitoring and crisis management activities in case of eruptions and other similar natural hazards. The implemented SAR image processing performs the extraction of the point-by-point fractal dimension of the scene observed by the sensor, providing - as an output product - the map of the fractal dimension of the area of interest. In this work, such an analysis is performed on Cosmo-SkyMed, ERS-1/2 and ENVISAT images relevant to active stratovolcanoes in different geodynamic contexts, such as Mt. Somma-Vesuvio, Mt. Etna, Vulcano and Stromboli in Southern Italy, Shinmoe

Improved SAR Image Coregistration Using Pixel-Offset Series

KAUST Repository

Wang, Teng

2014-03-14

Synthetic aperture radar (SAR) image coregistration is a key procedure before interferometric SAR (InSAR) time-series analysis can be started. However, many geophysical data sets suffer from severe decorrelation problems due to a variety of reasons, making precise coregistration a nontrivial task. Here, we present a new strategy that uses a pixel-offset series of detected subimage patches dominated by point-like targets (PTs) to improve SAR image coregistrations. First, all potentially coherent image pairs are coregistered in a conventional way. In this step, we propose a coregistration quality index for each image to rank its relative “significance” within the data set and to select a reference image for the SAR data set. Then, a pixel-offset series of detected PTs is made from amplitude maps to improve the geometrical mapping functions. Finally, all images are resampled depending on the pixel offsets calculated from the updated geometrical mapping functions. We used images from a rural region near the North Anatolian Fault in eastern Turkey to test the proposed method, and clear coregistration improvements were found based on amplitude stability. This enhanced the fact that the coregistration strategy should therefore lead to improved InSAR time-series analysis results.

Improved SAR Image Coregistration Using Pixel-Offset Series

KAUST Repository

Wang, Teng; Jonsson, Sigurjon; Hanssen, Ramon F.

2014-01-01

Synthetic aperture radar (SAR) image coregistration is a key procedure before interferometric SAR (InSAR) time-series analysis can be started. However, many geophysical data sets suffer from severe decorrelation problems due to a variety of reasons, making precise coregistration a nontrivial task. Here, we present a new strategy that uses a pixel-offset series of detected subimage patches dominated by point-like targets (PTs) to improve SAR image coregistrations. First, all potentially coherent image pairs are coregistered in a conventional way. In this step, we propose a coregistration quality index for each image to rank its relative “significance” within the data set and to select a reference image for the SAR data set. Then, a pixel-offset series of detected PTs is made from amplitude maps to improve the geometrical mapping functions. Finally, all images are resampled depending on the pixel offsets calculated from the updated geometrical mapping functions. We used images from a rural region near the North Anatolian Fault in eastern Turkey to test the proposed method, and clear coregistration improvements were found based on amplitude stability. This enhanced the fact that the coregistration strategy should therefore lead to improved InSAR time-series analysis results.

Combining TerraSAR-X and Landsat Images for Emergency Response in Urban Environments

Directory of Open Access Journals (Sweden)

Shiran Havivi

2018-05-01

Full Text Available Rapid damage mapping following a disaster event, especially in an urban environment, is critical to ensure that the emergency response in the affected area is rapid and efficient. This work presents a new method for mapping damage assessment in urban environments. Based on combining SAR and optical data, the method is applicable as support during initial emergency planning and rescue operations. The study focuses on the urban areas affected by the Tohoku earthquake and subsequent tsunami event in Japan that occurred on 11 March 2011. High-resolution TerraSAR-X (TSX images of before and after the event, and a Landsat 5 image before the event were acquired. The affected areas were analyzed with the SAR data using only one interferometric SAR (InSAR coherence map. To increase the damage mapping accuracy, the normalized difference vegetation index (NDVI was applied. The generated map, with a grid size of 50 m, provides a quantitative assessment of the nature and distribution of the damage. The damage mapping shows detailed information about the affected area, with high overall accuracy (89%, and high Kappa coefficient (82% and, as expected, it shows total destruction along the coastline compared to the inland region.

Fast Superpixel Segmentation Algorithm for PolSAR Images

Directory of Open Access Journals (Sweden)

Zhang Yue

2017-10-01

Full Text Available As a pre-processing technique, superpixel segmentation algorithms should be of high computational efficiency, accurate boundary adherence and regular shape in homogeneous regions. A fast superpixel segmentation algorithm based on Iterative Edge Refinement (IER has shown to be applicable on optical images. However, it is difficult to obtain the ideal results when IER is applied directly to PolSAR images due to the speckle noise and small or slim regions in PolSAR images. To address these problems, in this study, the unstable pixel set is initialized as all the pixels in the PolSAR image instead of the initial grid edge pixels. In the local relabeling of the unstable pixels, the fast revised Wishart distance is utilized instead of the Euclidean distance in CIELAB color space. Then, a post-processing procedure based on dissimilarity measure is empolyed to remove isolated small superpixels as well as to retain the strong point targets. Finally, extensive experiments based on a simulated image and a real-world PolSAR image from Airborne Synthetic Aperture Radar (AirSAR are conducted, showing that the proposed algorithm, compared with three state-of-the-art methods, performs better in terms of several commonly used evaluation criteria with high computational efficiency, accurate boundary adherence, and homogeneous regularity.

Automatic Coregistration for Multiview SAR Images in Urban Areas

Science.gov (United States)

Xiang, Y.; Kang, W.; Wang, F.; You, H.

2017-09-01

Due to the high resolution property and the side-looking mechanism of SAR sensors, complex buildings structures make the registration of SAR images in urban areas becomes very hard. In order to solve the problem, an automatic and robust coregistration approach for multiview high resolution SAR images is proposed in the paper, which consists of three main modules. First, both the reference image and the sensed image are segmented into two parts, urban areas and nonurban areas. Urban areas caused by double or multiple scattering in a SAR image have a tendency to show higher local mean and local variance values compared with general homogeneous regions due to the complex structural information. Based on this criterion, building areas are extracted. After obtaining the target regions, L-shape structures are detected using the SAR phase congruency model and Hough transform. The double bounce scatterings formed by wall and ground are shown as strong L- or T-shapes, which are usually taken as the most reliable indicator for building detection. According to the assumption that buildings are rectangular and flat models, planimetric buildings are delineated using the L-shapes, then the reconstructed target areas are obtained. For the orignal areas and the reconstructed target areas, the SAR-SIFT matching algorithm is implemented. Finally, correct corresponding points are extracted by the fast sample consensus (FSC) and the transformation model is also derived. The experimental results on a pair of multiview TerraSAR images with 1-m resolution show that the proposed approach gives a robust and precise registration performance, compared with the orignal SAR-SIFT method.

Prototype Theory Based Feature Representation for PolSAR Images

OpenAIRE

Huang Xiaojing; Yang Xiangli; Huang Pingping; Yang Wen

2016-01-01

This study presents a new feature representation approach for Polarimetric Synthetic Aperture Radar (PolSAR) image based on prototype theory. First, multiple prototype sets are generated using prototype theory. Then, regularized logistic regression is used to predict similarities between a test sample and each prototype set. Finally, the PolSAR image feature representation is obtained by ensemble projection. Experimental results of an unsupervised classification of PolSAR images show that our...

Polarimetric SAR image classification based on discriminative dictionary learning model

Science.gov (United States)

Sang, Cheng Wei; Sun, Hong

2018-03-01

Polarimetric SAR (PolSAR) image classification is one of the important applications of PolSAR remote sensing. It is a difficult high-dimension nonlinear mapping problem, the sparse representations based on learning overcomplete dictionary have shown great potential to solve such problem. The overcomplete dictionary plays an important role in PolSAR image classification, however for PolSAR image complex scenes, features shared by different classes will weaken the discrimination of learned dictionary, so as to degrade classification performance. In this paper, we propose a novel overcomplete dictionary learning model to enhance the discrimination of dictionary. The learned overcomplete dictionary by the proposed model is more discriminative and very suitable for PolSAR classification.

A novel ship CFAR detection algorithm based on adaptive parameter enhancement and wake-aided detection in SAR images

Science.gov (United States)

Meng, Siqi; Ren, Kan; Lu, Dongming; Gu, Guohua; Chen, Qian; Lu, Guojun

2018-03-01

Synthetic aperture radar (SAR) is an indispensable and useful method for marine monitoring. With the increase of SAR sensors, high resolution images can be acquired and contain more target structure information, such as more spatial details etc. This paper presents a novel adaptive parameter transform (APT) domain constant false alarm rate (CFAR) to highlight targets. The whole method is based on the APT domain value. Firstly, the image is mapped to the new transform domain by the algorithm. Secondly, the false candidate target pixels are screened out by the CFAR detector to highlight the target ships. Thirdly, the ship pixels are replaced by the homogeneous sea pixels. And then, the enhanced image is processed by Niblack algorithm to obtain the wake binary image. Finally, normalized Hough transform (NHT) is used to detect wakes in the binary image, as a verification of the presence of the ships. Experiments on real SAR images validate that the proposed transform does enhance the target structure and improve the contrast of the image. The algorithm has a good performance in the ship and ship wake detection.

SAR image effects on coherence and coherence estimation.

Energy Technology Data Exchange (ETDEWEB)

Bickel, Douglas Lloyd

2014-01-01

Radar coherence is an important concept for imaging radar systems such as synthetic aperture radar (SAR). This document quantifies some of the effects in SAR which modify the coherence. Although these effects can disrupt the coherence within a single SAR image, this report will focus on the coherence between separate images, such as for coherent change detection (CCD) processing. There have been other presentations on aspects of this material in the past. The intent of this report is to bring various issues that affect the coherence together in a single report to support radar engineers in making decisions about these matters.

AUTOMATIC COREGISTRATION FOR MULTIVIEW SAR IMAGES IN URBAN AREAS

Directory of Open Access Journals (Sweden)

Y. Xiang

2017-09-01

Full Text Available Due to the high resolution property and the side-looking mechanism of SAR sensors, complex buildings structures make the registration of SAR images in urban areas becomes very hard. In order to solve the problem, an automatic and robust coregistration approach for multiview high resolution SAR images is proposed in the paper, which consists of three main modules. First, both the reference image and the sensed image are segmented into two parts, urban areas and nonurban areas. Urban areas caused by double or multiple scattering in a SAR image have a tendency to show higher local mean and local variance values compared with general homogeneous regions due to the complex structural information. Based on this criterion, building areas are extracted. After obtaining the target regions, L-shape structures are detected using the SAR phase congruency model and Hough transform. The double bounce scatterings formed by wall and ground are shown as strong L- or T-shapes, which are usually taken as the most reliable indicator for building detection. According to the assumption that buildings are rectangular and flat models, planimetric buildings are delineated using the L-shapes, then the reconstructed target areas are obtained. For the orignal areas and the reconstructed target areas, the SAR-SIFT matching algorithm is implemented. Finally, correct corresponding points are extracted by the fast sample consensus (FSC and the transformation model is also derived. The experimental results on a pair of multiview TerraSAR images with 1-m resolution show that the proposed approach gives a robust and precise registration performance, compared with the orignal SAR-SIFT method.

Synthetic aperture design for increased SAR image rate

Science.gov (United States)

Bielek, Timothy P [Albuquerque, NM; Thompson, Douglas G [Albuqerque, NM; Walker, Bruce C [Albuquerque, NM

2009-03-03

High resolution SAR images of a target scene at near video rates can be produced by using overlapped, but nevertheless, full-size synthetic apertures. The SAR images, which respectively correspond to the apertures, can be analyzed in sequence to permit detection of movement in the target scene.

Guided SAR image despeckling with probabilistic non local weights

Science.gov (United States)

Gokul, Jithin; Nair, Madhu S.; Rajan, Jeny

2017-12-01

SAR images are generally corrupted by granular disturbances called speckle, which makes visual analysis and detail extraction a difficult task. Non Local despeckling techniques with probabilistic similarity has been a recent trend in SAR despeckling. To achieve effective speckle suppression without compromising detail preservation, we propose an improvement for the existing Generalized Guided Filter with Bayesian Non-Local Means (GGF-BNLM) method. The proposed method (Guided SAR Image Despeckling with Probabilistic Non Local Weights) replaces parametric constants based on heuristics in GGF-BNLM method with dynamically derived values based on the image statistics for weight computation. Proposed changes make GGF-BNLM method adaptive and as a result, significant improvement is achieved in terms of performance. Experimental analysis on SAR images shows excellent speckle reduction without compromising feature preservation when compared to GGF-BNLM method. Results are also compared with other state-of-the-art and classic SAR depseckling techniques to demonstrate the effectiveness of the proposed method.

ANALYSIS OF MULTIPATH PIXELS IN SAR IMAGES

Directory of Open Access Journals (Sweden)

J. W. Zhao

2016-06-01

Full Text Available As the received radar signal is the sum of signal contributions overlaid in one single pixel regardless of the travel path, the multipath effect should be seriously tackled as the multiple bounce returns are added to direct scatter echoes which leads to ghost scatters. Most of the existing solution towards the multipath is to recover the signal propagation path. To facilitate the signal propagation simulation process, plenty of aspects such as sensor parameters, the geometry of the objects (shape, location, orientation, mutual position between adjacent buildings and the physical parameters of the surface (roughness, correlation length, permittivitywhich determine the strength of radar signal backscattered to the SAR sensor should be given in previous. However, it's not practical to obtain the highly detailed object model in unfamiliar area by field survey as it's a laborious work and time-consuming. In this paper, SAR imaging simulation based on RaySAR is conducted at first aiming at basic understanding of multipath effects and for further comparison. Besides of the pre-imaging simulation, the product of the after-imaging, which refers to radar images is also taken into consideration. Both Cosmo-SkyMed ascending and descending SAR images of Lupu Bridge in Shanghai are used for the experiment. As a result, the reflectivity map and signal distribution map of different bounce level are simulated and validated by 3D real model. The statistic indexes such as the phase stability, mean amplitude, amplitude dispersion, coherence and mean-sigma ratio in case of layover are analyzed with combination of the RaySAR output.

AN UNSUPERVISED CHANGE DETECTION BASED ON TEST STATISTIC AND KI FROM MULTI-TEMPORAL AND FULL POLARIMETRIC SAR IMAGES

Directory of Open Access Journals (Sweden)

J. Q. Zhao

2016-06-01

Full Text Available Accurate and timely change detection of Earth’s surface features is extremely important for understanding relationships and interactions between people and natural phenomena. Many traditional methods of change detection only use a part of polarization information and the supervised threshold selection. Those methods are insufficiency and time-costing. In this paper, we present a novel unsupervised change-detection method based on quad-polarimetric SAR data and automatic threshold selection to solve the problem of change detection. First, speckle noise is removed for the two registered SAR images. Second, the similarity measure is calculated by the test statistic, and automatic threshold selection of KI is introduced to obtain the change map. The efficiency of the proposed method is demonstrated by the quad-pol SAR images acquired by Radarsat-2 over Wuhan of China.

New challenges for a SAR toolbox

International Nuclear Information System (INIS)

Loreaux, P.; Quin, G.

2013-01-01

High resolution multi-frequency synthetic aperture radar (SAR) imagery, available since early 2008, brings all weather capability and day/night operability in support of safeguards verification. Today, a combined approach of high resolution optical and radar imagery in monitoring exercise would enable looking at any area of interest on daily basis. One of the challenges is the co-registration of SAR images acquired with different acquisition mode and also with different optical images. We show in this paper the on-going research work to find a general co-register method and an automatic tool to detect changes. Before having an operational co-register tool, a method to find automatically tie points between SAR images acquired with different acquisition mode and with optical images has to be developed. Concerning an automatic change detection method we can conclude that the study of the Harmonic mean, Geometric mean and Arithmetic mean, enables several applications like change detection for SAR imagery. Thus, we developed the MAGMA (Method for Arithmetic and Geometric Means Analysis) change detection method. As shown in this paper, the MAGMA method improves the Maximum Likelihood techniques like GLRT, using Information-Theory concepts to detect changes between SAR amplitude images. The major improvement consists in a lower false detection rate, especially in low amplitude areas. The second improvement consists in a better location of the changes in clearly delimited areas, which enables precise interpretations. Results presented here reveal the potential of high resolution radar imagery for a baseline description of some sites, change detection based on repeat pass imagery acquisitions and site specific constraints in coherent change detection due to cover conditions. (A.C.)

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...

Object Georeferencing in UAV-Based SAR Terrain Images

Directory of Open Access Journals (Sweden)

Łabowski Michał

2016-12-01

Full Text Available Synthetic aperture radars (SAR allow to obtain high resolution terrain images comparable with the resolution of optical methods. Radar imaging is independent on the weather conditions and the daylight. The process of analysis of the SAR images consists primarily of identifying of interesting objects. The ability to determine their geographical coordinates can increase usability of the solution from a user point of view. The paper presents a georeferencing method of the radar terrain images. The presented images were obtained from the SAR system installed on board an Unmanned Aerial Vehicle (UAV. The system was developed within a project under acronym WATSAR realized by the Military University of Technology and WB Electronics S.A. The source of the navigation data was an INS/GNSS system integrated by the Kalman filter with a feed-backward correction loop. The paper presents the terrain images obtained during flight tests and results of selected objects georeferencing with an assessment of the accuracy of the method.

Multi-look polarimetric SAR image filtering using simulated annealing

DEFF Research Database (Denmark)

Schou, Jesper

2000-01-01

Based on a previously published algorithm capable of estimating the radar cross-section in synthetic aperture radar (SAR) intensity images, a new filter is presented utilizing multi-look polarimetric SAR images. The underlying mean covariance matrix is estimated from the observed sample covariance...

Multi-Pixel Simultaneous Classification of PolSAR Image Using Convolutional Neural Networks

Science.gov (United States)

Xu, Xin; Gui, Rong; Pu, Fangling

2018-01-01

Convolutional neural networks (CNN) have achieved great success in the optical image processing field. Because of the excellent performance of CNN, more and more methods based on CNN are applied to polarimetric synthetic aperture radar (PolSAR) image classification. Most CNN-based PolSAR image classification methods can only classify one pixel each time. Because all the pixels of a PolSAR image are classified independently, the inherent interrelation of different land covers is ignored. We use a fixed-feature-size CNN (FFS-CNN) to classify all pixels in a patch simultaneously. The proposed method has several advantages. First, FFS-CNN can classify all the pixels in a small patch simultaneously. When classifying a whole PolSAR image, it is faster than common CNNs. Second, FFS-CNN is trained to learn the interrelation of different land covers in a patch, so it can use the interrelation of land covers to improve the classification results. The experiments of FFS-CNN are evaluated on a Chinese Gaofen-3 PolSAR image and other two real PolSAR images. Experiment results show that FFS-CNN is comparable with the state-of-the-art PolSAR image classification methods. PMID:29510499

Fusion method of SAR and optical images for urban object extraction

Science.gov (United States)

Jia, Yonghong; Blum, Rick S.; Li, Fangfang

2007-11-01

A new image fusion method of SAR, Panchromatic (Pan) and multispectral (MS) data is proposed. First of all, SAR texture is extracted by ratioing the despeckled SAR image to its low pass approximation, and is used to modulate high pass details extracted from the available Pan image by means of the á trous wavelet decomposition. Then, high pass details modulated with the texture is applied to obtain the fusion product by HPFM (High pass Filter-based Modulation) fusion method. A set of image data including co-registered Landsat TM, ENVISAT SAR and SPOT Pan is used for the experiment. The results demonstrate accurate spectral preservation on vegetated regions, bare soil, and also on textured areas (buildings and road network) where SAR texture information enhances the fusion product, and the proposed approach is effective for image interpret and classification.

SIMULATION OF SHIP GENERATED TURBULENT AND VORTICAL WAKE IMAGING BY SAR

Institute of Scientific and Technical Information of China (English)

Wang Aiming; Zhu Minhui

2004-01-01

Synthetic Aperture Radar (SAR) imaging of ocean surface features is studied. The simulation of the turbulent and vortical features generated by a moving ship and SAR imaging of these wakes is carried out. The turbulent wake damping the ocean surface capillary waves may be partially responsible for the suppression of surface waves near the ship track. The vortex pair generating a change in the lateral flow field behind the ship may be partially responsible for an enhancement of the waves near the edges of the smooth area. These hydrodynamic phenomena as well as the changes of radar backscatter generated by turbulence and vortex are simulated.An SAR imaging model is then used on such ocean surface features to provide SAR images.Comparison of two ships' simulated SAR images shows that the wake features are different for various ship parameters.

Exploitation of Amplitude and Phase of Satellite SAR Images for Landslide Mapping: The Case of Montescaglioso (South Italy

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Federico Raspini

2015-11-01

Full Text Available Pre- event and event landslide deformations have been detected and measured for the landslide that occurred on 3 December 2013 on the south-western slope of the Montescaglioso village (Basilicata Region, southern Italy. In this paper, ground displacements have been mapped through an integrated analysis based on a series of high resolution SAR (Synthetic Aperture Radar images acquired by the Italian constellation of satellites COSMO-SkyMed. Analysis has been performed by exploiting both phase (through multi-image SAR interferometry and amplitude information (through speckle tracking techniques of the satellite images. SAR Interferometry, applied to images taken before the event, revealed a general pre-event movement, in the order of a few mm/yr, in the south-western slope of the Montescaglioso village. Highest pre-event velocities, ranging between 8 and 12 mm/yr, have been recorded in the sector of the slope where the first movement of the landslide took place. Speckle tracking, applied to images acquired before and after the event, allowed the retrieval of the 3D deformation field produced by the landslide. It also showed that ground displacements produced by the landslide have a dominant SSW component, with values exceeding 10 m for large sectors of the landslide area, with local peaks of 20 m in its central and deposit areas. Two minor landslides with a dominant SSE direction, which were detected in the upper parts of the slope, likely also occurred as secondary phenomena as consequence of the SSW movement of the main Montescaglioso landslide.

Improved techniques to utilize remotely sensed data from multi-frequency imaging radar polarimeter; Tashuha tahenha SAR data no riyoho no kenkyu

Energy Technology Data Exchange (ETDEWEB)

Okada, K [Sumitomo Metal Mining Co. Ltd., Osaka (Japan); Maruyama, Y [Earth Remote Sensing Data Analysis Center, Tokyo (Japan); Tapley, I

1997-05-27

It was intended to serve for establishing specifications for a next generation SAR such as PALSAR through studying methods for evaluating and utilizing the multi-frequency, multi-polarized wave SAR data. Placing an emphasis on utilization of the NASA`s AIRSAR, identification was made on backscatter amount recorded on the SAR data, terrestrial constitutional substances, patterns of the ground surface, micro-topography and such terrestrial conditions as vegetation and land utilization. Their mutual relationships were also analyzed. A noise reduction method usable on multi-band data can be applied to the AIRSAR data, and can reduce noise effectively. Images with more volume of information can be acquired from multi-band images with the same polarization wave than from multi-polarization wave images with the same band. As a result of estimating terrestrial permitivity by using the method invented by Dubois and van Zyl, most of the subject area is judged to have terrestrial substances dried at the time of having acquired the images. A colluvium rich with exposed rock regions and gravels was identified as an area having higher permitivity than the former area. Images of terrestrial roughness were divided largely into smooth flat lands, sand and gravel distributed regions, exposed rock regions, and plant distributed regions along river basins. 3 refs., 2 figs., 1 tab.

Wavelet Filter Banks for Super-Resolution SAR Imaging

Science.gov (United States)

Sheybani, Ehsan O.; Deshpande, Manohar; Memarsadeghi, Nargess

2011-01-01

This paper discusses Innovative wavelet-based filter banks designed to enhance the analysis of super resolution Synthetic Aperture Radar (SAR) images using parametric spectral methods and signal classification algorithms, SAR finds applications In many of NASA's earth science fields such as deformation, ecosystem structure, and dynamics of Ice, snow and cold land processes, and surface water and ocean topography. Traditionally, standard methods such as Fast-Fourier Transform (FFT) and Inverse Fast-Fourier Transform (IFFT) have been used to extract Images from SAR radar data, Due to non-parametric features of these methods and their resolution limitations and observation time dependence, use of spectral estimation and signal pre- and post-processing techniques based on wavelets to process SAR radar data has been proposed. Multi-resolution wavelet transforms and advanced spectral estimation techniques have proven to offer efficient solutions to this problem.

Playback system designed for X-Band SAR

International Nuclear Information System (INIS)

Yuquan, Liu; Changyong, Dou

2014-01-01

SAR(Synthetic Aperture Radar) has extensive application because it is daylight and weather independent. In particular, X-Band SAR strip map, designed by Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, provides high ground resolution images, at the same time it has a large spatial coverage and a short acquisition time, so it is promising in multi-applications. When sudden disaster comes, the emergency situation acquires radar signal data and image as soon as possible, in order to take action to reduce loss and save lives in the first time. This paper summarizes a type of X-Band SAR playback processing system designed for disaster response and scientific needs. It describes SAR data workflow includes the payload data transmission and reception process. Playback processing system completes signal analysis on the original data, providing SAR level 0 products and quick image. Gigabit network promises radar signal transmission efficiency from recorder to calculation unit. Multi-thread parallel computing and ping pong operation can ensure computation speed. Through gigabit network, multi-thread parallel computing and ping pong operation, high speed data transmission and processing meet the SAR radar data playback real time requirement

Playback system designed for X-Band SAR

Science.gov (United States)

Yuquan, Liu; Changyong, Dou

2014-03-01

SAR(Synthetic Aperture Radar) has extensive application because it is daylight and weather independent. In particular, X-Band SAR strip map, designed by Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, provides high ground resolution images, at the same time it has a large spatial coverage and a short acquisition time, so it is promising in multi-applications. When sudden disaster comes, the emergency situation acquires radar signal data and image as soon as possible, in order to take action to reduce loss and save lives in the first time. This paper summarizes a type of X-Band SAR playback processing system designed for disaster response and scientific needs. It describes SAR data workflow includes the payload data transmission and reception process. Playback processing system completes signal analysis on the original data, providing SAR level 0 products and quick image. Gigabit network promises radar signal transmission efficiency from recorder to calculation unit. Multi-thread parallel computing and ping pong operation can ensure computation speed. Through gigabit network, multi-thread parallel computing and ping pong operation, high speed data transmission and processing meet the SAR radar data playback real time requirement.

Pixel Classification of SAR ice images using ANFIS-PSO Classifier

Directory of Open Access Journals (Sweden)

G. Vasumathi

2016-12-01

Full Text Available Synthetic Aperture Radar (SAR is playing a vital role in taking extremely high resolution radar images. It is greatly used to monitor the ice covered ocean regions. Sea monitoring is important for various purposes which includes global climate systems and ship navigation. Classification on the ice infested area gives important features which will be further useful for various monitoring process around the ice regions. Main objective of this paper is to classify the SAR ice image that helps in identifying the regions around the ice infested areas. In this paper three stages are considered in classification of SAR ice images. It starts with preprocessing in which the speckled SAR ice images are denoised using various speckle removal filters; comparison is made on all these filters to find the best filter in speckle removal. Second stage includes segmentation in which different regions are segmented using K-means and watershed segmentation algorithms; comparison is made between these two algorithms to find the best in segmenting SAR ice images. The last stage includes pixel based classification which identifies and classifies the segmented regions using various supervised learning classifiers. The algorithms includes Back propagation neural networks (BPN, Fuzzy Classifier, Adaptive Neuro Fuzzy Inference Classifier (ANFIS classifier and proposed ANFIS with Particle Swarm Optimization (PSO classifier; comparison is made on all these classifiers to propose which classifier is best suitable for classifying the SAR ice image. Various evaluation metrics are performed separately at all these three stages.

Restoration of polarimetric SAR images using simulated annealing

DEFF Research Database (Denmark)

Schou, Jesper; Skriver, Henning

2001-01-01

approach favoring one of the objectives. An algorithm for estimating the radar cross-section (RCS) for intensity SAR images has previously been proposed in the literature based on Markov random fields and the stochastic optimization method simulated annealing. A new version of the algorithm is presented......Filtering synthetic aperture radar (SAR) images ideally results in better estimates of the parameters characterizing the distributed targets in the images while preserving the structures of the nondistributed targets. However, these objectives are normally conflicting, often leading to a filtering...

G0-WISHART Distribution Based Classification from Polarimetric SAR Images

Science.gov (United States)

Hu, G. C.; Zhao, Q. H.

2017-09-01

Enormous scientific and technical developments have been carried out to further improve the remote sensing for decades, particularly Polarimetric Synthetic Aperture Radar(PolSAR) technique, so classification method based on PolSAR images has getted much more attention from scholars and related department around the world. The multilook polarmetric G0-Wishart model is a more flexible model which describe homogeneous, heterogeneous and extremely heterogeneous regions in the image. Moreover, the polarmetric G0-Wishart distribution dose not include the modified Bessel function of the second kind. It is a kind of simple statistical distribution model with less parameter. To prove its feasibility, a process of classification has been tested with the full-polarized Synthetic Aperture Radar (SAR) image by the method. First, apply multilook polarimetric SAR data process and speckle filter to reduce speckle influence for classification result. Initially classify the image into sixteen classes by H/A/α decomposition. Using the ICM algorithm to classify feature based on the G0-Wshart distance. Qualitative and quantitative results show that the proposed method can classify polaimetric SAR data effectively and efficiently.

The Generalized Gamma-DBN for High-Resolution SAR Image Classification

Directory of Open Access Journals (Sweden)

Zhiqiang Zhao

2018-06-01

Full Text Available With the increase of resolution, effective characterization of synthetic aperture radar (SAR image becomes one of the most critical problems in many earth observation applications. Inspired by deep learning and probability mixture models, a generalized Gamma deep belief network (g Γ-DBN is proposed for SAR image statistical modeling and land-cover classification in this work. Specifically, a generalized Gamma-Bernoulli restricted Boltzmann machine (gΓB-RBM is proposed to capture high-order statistical characterizes from SAR images after introducing the generalized Gamma distribution. After stacking the g Γ B-RBM and several standard binary RBMs in a hierarchical manner, a gΓ-DBN is constructed to learn high-level representation of different SAR land-covers. Finally, a discriminative neural network is constructed by adding an additional predict layer for different land-covers over the constructed deep structure. Performance of the proposed approach is evaluated via several experiments on some high-resolution SAR image patch sets and two large-scale scenes which are captured by ALOS PALSAR-2 and COSMO-SkyMed satellites respectively.

SAR Data Fusion Imaging Method Oriented to Target Feature Extraction

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Yang Wei

2015-02-01

Full Text Available To deal with the difficulty for target outlines extracting precisely due to neglect of target scattering characteristic variation during the processing of high-resolution space-borne SAR data, a novel fusion imaging method is proposed oriented to target feature extraction. Firstly, several important aspects that affect target feature extraction and SAR image quality are analyzed, including curved orbit, stop-and-go approximation, atmospheric delay, and high-order residual phase error. Furthermore, the corresponding compensation methods are addressed as well. Based on the analysis, the mathematical model of SAR echo combined with target space-time spectrum is established for explaining the space-time-frequency change rule of target scattering characteristic. Moreover, a fusion imaging strategy and method under high-resolution and ultra-large observation angle range conditions are put forward to improve SAR quality by fusion processing in range-doppler and image domain. Finally, simulations based on typical military targets are used to verify the effectiveness of the fusion imaging method.

An Advanced Rotation Invariant Descriptor for SAR Image Registration

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Yuming Xiang

2017-07-01

Full Text Available The Scale-Invariant Feature Transform (SIFT algorithm and its many variants have been widely used in Synthetic Aperture Radar (SAR image registration. The SIFT-like algorithms maintain rotation invariance by assigning a dominant orientation for each keypoint, while the calculation of dominant orientation is not robust due to the effect of speckle noise in SAR imagery. In this paper, we propose an advanced local descriptor for SAR image registration to achieve rotation invariance without assigning a dominant orientation. Based on the improved intensity orders, we first divide a circular neighborhood into several sub-regions. Second, rotation-invariant ratio orientation histograms of each sub-region are proposed by accumulating the ratio values of different directions in a rotation-invariant coordinate system. The proposed descriptor is composed of the concatenation of the histograms of each sub-region. In order to increase the distinctiveness of the proposed descriptor, multiple image neighborhoods are aggregated. Experimental results on several satellite SAR images have shown an improvement in the matching performance over other state-of-the-art algorithms.

SAR image classification based on CNN in real and simulation datasets

Science.gov (United States)

Peng, Lijiang; Liu, Ming; Liu, Xiaohua; Dong, Liquan; Hui, Mei; Zhao, Yuejin

2018-04-01

Convolution neural network (CNN) has made great success in image classification tasks. Even in the field of synthetic aperture radar automatic target recognition (SAR-ATR), state-of-art results has been obtained by learning deep representation of features on the MSTAR benchmark. However, the raw data of MSTAR have shortcomings in training a SAR-ATR model because of high similarity in background among the SAR images of each kind. This indicates that the CNN would learn the hierarchies of features of backgrounds as well as the targets. To validate the influence of the background, some other SAR images datasets have been made which contains the simulation SAR images of 10 manufactured targets such as tank and fighter aircraft, and the backgrounds of simulation SAR images are sampled from the whole original MSTAR data. The simulation datasets contain the dataset that the backgrounds of each kind images correspond to the one kind of backgrounds of MSTAR targets or clutters and the dataset that each image shares the random background of whole MSTAR targets or clutters. In addition, mixed datasets of MSTAR and simulation datasets had been made to use in the experiments. The CNN architecture proposed in this paper are trained on all datasets mentioned above. The experimental results shows that the architecture can get high performances on all datasets even the backgrounds of the images are miscellaneous, which indicates the architecture can learn a good representation of the targets even though the drastic changes on background.

Accelerating Spaceborne SAR Imaging Using Multiple CPU/GPU Deep Collaborative Computing

Directory of Open Access Journals (Sweden)

Fan Zhang

2016-04-01

Full Text Available With the development of synthetic aperture radar (SAR technologies in recent years, the huge amount of remote sensing data brings challenges for real-time imaging processing. Therefore, high performance computing (HPC methods have been presented to accelerate SAR imaging, especially the GPU based methods. In the classical GPU based imaging algorithm, GPU is employed to accelerate image processing by massive parallel computing, and CPU is only used to perform the auxiliary work such as data input/output (IO. However, the computing capability of CPU is ignored and underestimated. In this work, a new deep collaborative SAR imaging method based on multiple CPU/GPU is proposed to achieve real-time SAR imaging. Through the proposed tasks partitioning and scheduling strategy, the whole image can be generated with deep collaborative multiple CPU/GPU computing. In the part of CPU parallel imaging, the advanced vector extension (AVX method is firstly introduced into the multi-core CPU parallel method for higher efficiency. As for the GPU parallel imaging, not only the bottlenecks of memory limitation and frequent data transferring are broken, but also kinds of optimized strategies are applied, such as streaming, parallel pipeline and so on. Experimental results demonstrate that the deep CPU/GPU collaborative imaging method enhances the efficiency of SAR imaging on single-core CPU by 270 times and realizes the real-time imaging in that the imaging rate outperforms the raw data generation rate.

Accelerating Spaceborne SAR Imaging Using Multiple CPU/GPU Deep Collaborative Computing.

Science.gov (United States)

Zhang, Fan; Li, Guojun; Li, Wei; Hu, Wei; Hu, Yuxin

2016-04-07

With the development of synthetic aperture radar (SAR) technologies in recent years, the huge amount of remote sensing data brings challenges for real-time imaging processing. Therefore, high performance computing (HPC) methods have been presented to accelerate SAR imaging, especially the GPU based methods. In the classical GPU based imaging algorithm, GPU is employed to accelerate image processing by massive parallel computing, and CPU is only used to perform the auxiliary work such as data input/output (IO). However, the computing capability of CPU is ignored and underestimated. In this work, a new deep collaborative SAR imaging method based on multiple CPU/GPU is proposed to achieve real-time SAR imaging. Through the proposed tasks partitioning and scheduling strategy, the whole image can be generated with deep collaborative multiple CPU/GPU computing. In the part of CPU parallel imaging, the advanced vector extension (AVX) method is firstly introduced into the multi-core CPU parallel method for higher efficiency. As for the GPU parallel imaging, not only the bottlenecks of memory limitation and frequent data transferring are broken, but also kinds of optimized strategies are applied, such as streaming, parallel pipeline and so on. Experimental results demonstrate that the deep CPU/GPU collaborative imaging method enhances the efficiency of SAR imaging on single-core CPU by 270 times and realizes the real-time imaging in that the imaging rate outperforms the raw data generation rate.

Automatic Detection and Positioning of Ground Control Points Using TerraSAR-X Multiaspect Acquisitions

Science.gov (United States)

Montazeri, Sina; Gisinger, Christoph; Eineder, Michael; Zhu, Xiao xiang

2018-05-01

Geodetic stereo Synthetic Aperture Radar (SAR) is capable of absolute three-dimensional localization of natural Persistent Scatterer (PS)s which allows for Ground Control Point (GCP) generation using only SAR data. The prerequisite for the method to achieve high precision results is the correct detection of common scatterers in SAR images acquired from different viewing geometries. In this contribution, we describe three strategies for automatic detection of identical targets in SAR images of urban areas taken from different orbit tracks. Moreover, a complete work-flow for automatic generation of large number of GCPs using SAR data is presented and its applicability is shown by exploiting TerraSAR-X (TS-X) high resolution spotlight images over the city of Oulu, Finland and a test site in Berlin, Germany.

An Unsupervised Change Detection Method Using Time-Series of PolSAR Images from Radarsat-2 and GaoFen-3.

Science.gov (United States)

Liu, Wensong; Yang, Jie; Zhao, Jinqi; Shi, Hongtao; Yang, Le

2018-02-12

The traditional unsupervised change detection methods based on the pixel level can only detect the changes between two different times with same sensor, and the results are easily affected by speckle noise. In this paper, a novel method is proposed to detect change based on time-series data from different sensors. Firstly, the overall difference image of the time-series PolSAR is calculated by omnibus test statistics, and difference images between any two images in different times are acquired by R j test statistics. Secondly, the difference images are segmented with a Generalized Statistical Region Merging (GSRM) algorithm which can suppress the effect of speckle noise. Generalized Gaussian Mixture Model (GGMM) is then used to obtain the time-series change detection maps in the final step of the proposed method. To verify the effectiveness of the proposed method, we carried out the experiment of change detection using time-series PolSAR images acquired by Radarsat-2 and Gaofen-3 over the city of Wuhan, in China. Results show that the proposed method can not only detect the time-series change from different sensors, but it can also better suppress the influence of speckle noise and improve the overall accuracy and Kappa coefficient.

CFAR Edge Detector for Polarimetric SAR Images

DEFF Research Database (Denmark)

Schou, Jesper; Skriver, Henning; Nielsen, Allan Aasbjerg

2003-01-01

Finding the edges between different regions in an image is one of the fundamental steps of image analysis, and several edge detectors suitable for the special statistics of synthetic aperture radar (SAR) intensity images have previously been developed. In this paper, a new edge detector for polar...

Two dimensional estimates from ocean SAR images

Directory of Open Access Journals (Sweden)

J. M. Le Caillec

1996-01-01

Full Text Available Synthetic Aperture Radar (SAR images of the ocean yield a lot of information on the sea-state surface providing that the mapping process between the surface and the image is clearly defined. However it is well known that SAR images exhibit non-gaussian statistics and that the motion of the scatterers on the surface, while the image is being formed, may yield to nonlinearities. The detection and quantification of these nonlinearities are made possible by using Higher Order Spectra (HOS methods and more specifically, bispectrum estimation. The development of the latter method allowed us to find phase relations between different parts of the image and to recognise their level of coupling, i.e. if and how waves of different wavelengths interacted nonlinearly. This information is quite important as the usual models assume strong nonlinearities when the waves are propagating in the azimuthal direction (i.e. along the satellite track and almost no nonlinearities when propagating in the range direction. In this paper, the mapping of the ocean surface to the SAR image is reinterpreted and a specific model (i.e. a Second Order Volterra Model is introduced. The nonlinearities are thus explained as either produced by a nonlinear system or due to waves propagating into selected directions (azimuth or range and interacting during image formation. It is shown that quadratic nonlinearities occur for waves propagating near the range direction while for those travelling in the azimuthal direction the nonlinearities, when present, are mostly due to wave interactions but are almost completely removed by the filtering effect coming from the surface motion itself (azimuth cut-off. An inherent quadratic interaction filtering (azimuth high pass filter is also present. But some other effects, apparently nonlinear, are not detected with the methods described here, meaning that either the usual relation developed for the Ocean-to-SAR transform is somewhat incomplete

Spacial Variation in SAR Images of Different Resolution for Agricultural Fields

DEFF Research Database (Denmark)

Sandholt, Inge; Skriver, Henning

1999-01-01

The spatial variation in two types of Synthetic Aperture Radar (SAR) images covering agricultural fields is analysed. C-band polarimetric SAR data from the Danish airborne SAR, EMISAR, have been compared to space based ERS-1 C-band SAR with respect to scale and effect of polarization. The general...

Classification of sea-ice types in SAR imagery

International Nuclear Information System (INIS)

Baraldi, A.; Parmiggiani, F.

2001-01-01

It is presented a supervised three-stage classification (labeling) scheme applied to SAR images of polar regions for detecting different sea-ice types. The three-stage labeling procedure consists of: 1) a speckle noise filtering stage, based on a sequence of contour detection, segmentation and filtering steps, which removes SAR speckle noise (and texture information as well) without losing spatial details; 2) a second stage providing Bayesian, maximum-α-posteriori, hierarchical (coarse-to-fine), adaptive (data-driven) and contextual labeling of piecewise constant intensity images featuring little useful texture information; and 3) an output stage providing a many-to-one relationship between second stage output categories (types or clusters) and desired output classes. Modules 1) and 2), which demonstrated their validity in several applications in the existing literature, are briefly recalled in the current paper. The proposed labeling scheme features some interesting functional properties when applied to sea-ice SAR images: it is easy to use, i.e., it requires minor user interaction, is robust to changes in input conditions and performs better than a non-contextual (per-pixel) classifier. Application results are presented and discussed for a pair of SAR images extracted, respectively, from an ERS-1 scene acquired on November 1992 over the Bellingshausen Sea (Antarctica) and from an ERS-2 scene of the East Greenland Sea acquired on March 1997 when a field experiment by the research vessel Jan Ma yen was conducted in the same area

Mapping and monitoring renewable resources with space SAR

Science.gov (United States)

Ulaby, F. T.; Brisco, B.; Dobson, M. C.; Moezzi, S.

1983-01-01

The SEASAT-A SAR and SIR-A imagery was examined to evaluate the quality and type of information that can be extracted and used to monitor renewable resources on Earth. Two tasks were carried out: (1) a land cover classification study which utilized two sets of imagery acquired by the SEASAT-A SAR, one set by SIR-A, and one LANDSAT set (4 bands); and (2) a change detection to examine differences between pairs of SEASAT-A SAR images and relates them to hydrologic and/or agronomic variations in the scene.

The Establishment of the SAR images database System Based on Oracle and ArcSDE

International Nuclear Information System (INIS)

Zhou, Jijin; Li, Zhen; Chen, Quan; Tian, Bangsen

2014-01-01

Synthetic aperture radar is a kind of microwave imaging system, and has the advantages of multi-band, multi-polarization and multi-angle. At present, there is no SAR images database system based on typical features. For solving problems in interpretation and identification, a new SAR images database system of the typical features is urgent in the current development need. In this article, a SAR images database system based on Oracle and ArcSDE was constructed. The main works involving are as follows: (1) SAR image data was calibrated and corrected geometrically and geometrically. Besides, the fully polarimetric image was processed as the coherency matrix[T] to preserve the polarimetric information. (2) After analyzing multiple space borne SAR images, the metadata table was defined as: IMAGEID; Name of features; Latitude and Longitude; Sensor name; Range and Azimuth resolution etc. (3) Through the comparison between GeoRaster and ArcSDE, result showed ArcSDE is a more appropriate technology to store images in a central database. The System stores and manages multisource SAR image data well, reflects scattering, geometry, polarization, band and angle characteristics, and combines with analysis of the managed objects and service objects of the database as well as focuses on constructing SAR image system in the aspects of data browse and data retrieval. According the analysis of characteristics of SAR images such as scattering, polarization, incident angle and wave band information, different weights can be given to these characteristics. Then an interpreted tool is formed to provide an efficient platform for interpretation

MAXIMUM LIKELIHOOD CLASSIFICATION OF HIGH-RESOLUTION SAR IMAGES IN URBAN AREA

Directory of Open Access Journals (Sweden)

M. Soheili Majd

2012-09-01

Full Text Available In this work, we propose a state-of-the-art on statistical analysis of polarimetric synthetic aperture radar (SAR data, through the modeling of several indices. We concentrate on eight ground classes which have been carried out from amplitudes, co-polarisation ratio, depolarization ratios, and other polarimetric descriptors. To study their different statistical behaviours, we consider Gauss, log- normal, Beta I, Weibull, Gamma, and Fisher statistical models and estimate their parameters using three methods: method of moments (MoM, maximum-likelihood (ML methodology, and log-cumulants method (MoML. Then, we study the opportunity of introducing this information in an adapted supervised classification scheme based on Maximum–Likelihood and Fisher pdf. Our work relies on an image of a suburban area, acquired by the airborne RAMSES SAR sensor of ONERA. The results prove the potential of such data to discriminate urban surfaces and show the usefulness of adapting any classical classification algorithm however classification maps present a persistant class confusion between flat gravelled or concrete roofs and trees.

SAR target recognition using behaviour library of different shapes in different incidence angles and polarisations

Science.gov (United States)

Fallahpour, Mojtaba Behzad; Dehghani, Hamid; Jabbar Rashidi, Ali; Sheikhi, Abbas

2018-05-01

Target recognition is one of the most important issues in the interpretation of the synthetic aperture radar (SAR) images. Modelling, analysis, and recognition of the effects of influential parameters in the SAR can provide a better understanding of the SAR imaging systems, and therefore facilitates the interpretation of the produced images. Influential parameters in SAR images can be divided into five general categories of radar, radar platform, channel, imaging region, and processing section, each of which has different physical, structural, hardware, and software sub-parameters with clear roles in the finally formed images. In this paper, for the first time, a behaviour library that includes the effects of polarisation, incidence angle, and shape of targets, as radar and imaging region sub-parameters, in the SAR images are extracted. This library shows that the created pattern for each of cylindrical, conical, and cubic shapes is unique, and due to their unique properties these types of shapes can be recognised in the SAR images. This capability is applied to data acquired with the Canadian RADARSAT1 satellite.

Near Surface Soil Moisture Estimation Using SAR Images: A Case Study in the Mediterranean Area of Catalonia

Science.gov (United States)

Reppucci, Antonio; Moreno, Laura

2010-12-01

Information on Soil moisture spatial and temporal evolution is of great importance for managing the utilization of soils and vegetation, in particular in environments where the water resources are scarce. In-situ measurement of soil moisture are costly and not able to sample the spatial behaviour of a whole region. Thanks to their all weather capability and wide coverage, Synthetic Aperture Radar (SAR) images offer the opportunity to monitor large area with high resolution. This study presents the results of a project, partially founded by the Catalan government, to improve the monitoring of soil moisture using Earth Observation data. In particular the project is focused on the calibration of existing semi-empirical algorithm in the area of study. This will be done using co-located SAR and in-situ measurements acquired during several field campaigns. Observed deviations between SAR measurements and in-situ measurement are discussed.

Enhancement of SAR images using fuzzy shrinkage technique

Indian Academy of Sciences (India)

This paper presents speckle noise reduction in SAR images using a combination of curvelet and fuzzy logic technique to restore speckle-affected images. This method overcomes the limitation of discontinuity in hard threshold and permanent deviation in soft threshold. First, it decomposes noise image into different ...

Azimuth Ambiguities Removal in Littoral Zones Based on Multi-Temporal SAR Images

Directory of Open Access Journals (Sweden)

Xiangguang Leng

2017-08-01

Full Text Available Synthetic aperture radar (SAR is one of the most important techniques for ocean monitoring. Azimuth ambiguities are a real problem in SAR images today, which can cause performance degradation in SAR ocean applications. In particular, littoral zones can be strongly affected by land-based sources, whereas they are usually regions of interest (ROI. Given the presence of complexity and diversity in littoral zones, azimuth ambiguities removal is a tough problem. As SAR sensors can have a repeat cycle, multi-temporal SAR images provide new insight into this problem. A method for azimuth ambiguities removal in littoral zones based on multi-temporal SAR images is proposed in this paper. The proposed processing chain includes co-registration, local correlation, binarization, masking, and restoration steps. It is designed to remove azimuth ambiguities caused by fixed land-based sources. The idea underlying the proposed method is that sea surface is dynamic, whereas azimuth ambiguities caused by land-based sources are constant. Thus, the temporal consistence of azimuth ambiguities is higher than sea clutter. It opens up the possibilities to use multi-temporal SAR data to remove azimuth ambiguities. The design of the method and the experimental procedure are based on images from the Sentinel data hub of Europe Space Agency (ESA. Both Interferometric Wide Swath (IW and Stripmap (SM mode images are taken into account to validate the proposed method. This paper also presents two RGB composition methods for better azimuth ambiguities visualization. Experimental results show that the proposed method can remove azimuth ambiguities in littoral zones effectively.

Tie Points Extraction for SAR Images Based on Differential Constraints

Science.gov (United States)

Xiong, X.; Jin, G.; Xu, Q.; Zhang, H.

2018-04-01

Automatically extracting tie points (TPs) on large-size synthetic aperture radar (SAR) images is still challenging because the efficiency and correct ratio of the image matching need to be improved. This paper proposes an automatic TPs extraction method based on differential constraints for large-size SAR images obtained from approximately parallel tracks, between which the relative geometric distortions are small in azimuth direction and large in range direction. Image pyramids are built firstly, and then corresponding layers of pyramids are matched from the top to the bottom. In the process, the similarity is measured by the normalized cross correlation (NCC) algorithm, which is calculated from a rectangular window with the long side parallel to the azimuth direction. False matches are removed by the differential constrained random sample consensus (DC-RANSAC) algorithm, which appends strong constraints in azimuth direction and weak constraints in range direction. Matching points in the lower pyramid images are predicted with the local bilinear transformation model in range direction. Experiments performed on ENVISAT ASAR and Chinese airborne SAR images validated the efficiency, correct ratio and accuracy of the proposed method.

Rapid Flood Map Generation from Spaceborne SAR Observations

Science.gov (United States)

Yun, S. H.; Liang, C.; Manipon, G.; Jung, J.; Gurrola, E. M.; Owen, S. E.; Hua, H.; Agram, P. S.; Webb, F.; Sacco, G. F.; Rosen, P. A.; Simons, M.

2016-12-01

The Advanced Rapid Imaging and Analysis (ARIA) team has responded to the January 2016 US Midwest Floods along the Mississippi River. Daily teleconferences with FEMA, NOAA, NGA, and USGS, provided information on precipitation and flood crest migration, based on which we coordinated with the Japanese Aerospace Exploration Agency (JAXA) through NASA headquarters for JAXA's ALOS-2 timely tasking over two paths. We produced flood extent maps using ALOS-2 SM3 mode Level 1.5 data that were provided through the International Charter and stored at the US Geological Survey's Hazards Data Distribution System (HDDS) archive. On January 6, the first four frames (70 km x 240 km) were acquired, which included the City of Memphis. We registered post-event SAR images to pre-event images, applied radiometric calibration, took a logarithm of the ratio of the two images. Two thresholds were applied to represent flooded areas that became open water (colored in blue) and flooded areas with tall vegetation (colored in red). The second path was acquired on January 11 further down along the Mississippi River. Seven frames (70 km x 420 km) were acquired and flood maps were created in the similar fashion. The maps were delivered to the FEMA as well as posted on ARIA's public website. The FEMA stated that SAR provides inspection priority for optical imagery and ground response. The ALOS-2 data and the products have been a very important source of information during this response as the flood crest has moved down stream. The SAR data continue to be an important resource during times when optical observations are often not useful. In close collaboration with FEMA and USGS, we also work on other flood events including June 2016 China Floods using European Space Agency's (ESA's) Sentienl-1 data, to produce flood extent maps and identify algorithmic needs and ARIA system's requirements to automate and rapidly produce and deliver flood maps for future events. With the addition of Sentinel-1B

On Signal Modeling of Moon-Based Synthetic Aperture Radar (SAR Imaging of Earth

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Zhen Xu

2018-03-01

Full Text Available The Moon-based Synthetic Aperture Radar (Moon-Based SAR, using the Moon as a platform, has a great potential to offer global-scale coverage of the earth’s surface with a high revisit cycle and is able to meet the scientific requirements for climate change study. However, operating in the lunar orbit, Moon-Based SAR imaging is confined within a complex geometry of the Moon-Based SAR, Moon, and Earth, where both rotation and revolution have effects. The extremely long exposure time of Moon-Based SAR presents a curved moving trajectory and the protracted time-delay in propagation makes the “stop-and-go” assumption no longer valid. Consequently, the conventional SAR imaging technique is no longer valid for Moon-Based SAR. This paper develops a Moon-Based SAR theory in which a signal model is derived. The Doppler parameters in the context of lunar revolution with the removal of ‘stop-and-go’ assumption are first estimated, and then characteristics of Moon-Based SAR imaging’s azimuthal resolution are analyzed. In addition, a signal model of Moon-Based SAR and its two-dimensional (2-D spectrum are further derived. Numerical simulation using point targets validates the signal model and enables Doppler parameter estimation for image focusing.

A Least Square Approach for Joining Persistent Scatterer InSAR Time Series Acquired by Different Satellites

Science.gov (United States)

Caro Cuenca, Miguel; Esfahany, Sami Samiei; Hanssen, Ramon F.

2010-12-01

Persistent scatterer Radar Interferometry (PSI) can provide with a wealth of information on surface motion. These methods overcome the major limitations of the antecessor technique, interferometric SAR (InSAR), such as atmospheric disturbances, by detecting the scatterers which are slightly affected by noise. The time span that surface deformation processes are observed is limited by the satellite lifetime, which is usually less than 10 years. However most of deformation phenomena last longer. In order to fully monitor and comprehend the observed signal, acquisitions from different sensors can be merged. This is a complex task for one main reason. PSI methods provide with estimations that are relative in time to one of the acquisitions which is referred to as master or reference image. Therefore, time series acquired by different sensors will have different reference images and cannot be directly compared or joint unless they are set to the same time reference system. In global terms, the operation of translating from one to another reference systems consist of calculating a vertical offset, which is the total deformation that occurs between the two master times. To estimate this offset, different strategies can be applied, for example, using additional data such as leveling or GPS measurements. In this contribution we propose to use a least squares to merge PSI time series without any ancillary information. This method treats the time series individually, i.e. per PS, and requires some knowledge of the deformation signal, for example, if a polynomial would fairly describe the expected behavior. To test the proposed approach, we applied it to the southern Netherlands, where the surface is affected by ground water processes in abandoned mines. The time series were obtained after processing images provided by ERS1/2 and Envisat. The results were validated using in-situ water measurements, which show very high correlation with deformation time series.

Satellite sar detection of hurricane helene (2006)

DEFF Research Database (Denmark)

Ju, Lian; Cheng, Yongcun; Xu, Qing

2013-01-01

In this paper, the wind structure of hurricane Helene (2006) over the Atlantic Ocean is investigated from a C-band RADARSAT-1 synthetic aperture radar (SAR) image acquired on 20 September 2006. First, the characteristics, e.g., the center, scale and area of the hurricane eye (HE) are determined. ...... observations from the stepped frequency microwave radiometer (SFMR) on NOAA P3 aircraft. All the results show the capability of hurricane monitoring by satellite SAR. Copyright © 2013 by the International Society of Offshore and Polar Engineers (ISOPE)....

Hybrid Geometric Calibration Method for Multi-Platform Spaceborne SAR Image with Sparse Gcps

Science.gov (United States)

Lv, G.; Tang, X.; Ai, B.; Li, T.; Chen, Q.

2018-04-01

Geometric calibration is able to provide high-accuracy geometric coordinates of spaceborne SAR image through accurate geometric parameters in the Range-Doppler model by ground control points (GCPs). However, it is very difficult to obtain GCPs that covering large-scale areas, especially in the mountainous regions. In addition, the traditional calibration method is only used for single platform SAR images and can't support the hybrid geometric calibration for multi-platform images. To solve the above problems, a hybrid geometric calibration method for multi-platform spaceborne SAR images with sparse GCPs is proposed in this paper. First, we calibrate the master image that contains GCPs. Secondly, the point tracking algorithm is used to obtain the tie points (TPs) between the master and slave images. Finally, we calibrate the slave images using TPs as the GCPs. We take the Beijing-Tianjin- Hebei region as an example to study SAR image hybrid geometric calibration method using 3 TerraSAR-X images, 3 TanDEM-X images and 5 GF-3 images covering more than 235 kilometers in the north-south direction. Geometric calibration of all images is completed using only 5 GCPs. The GPS data extracted from GNSS receiver are used to assess the plane accuracy after calibration. The results after geometric calibration with sparse GCPs show that the geometric positioning accuracy is 3 m for TSX/TDX images and 7.5 m for GF-3 images.

C/X-band SAR interferometry applied to ground monitoring: examples and new potential

Science.gov (United States)

Nutricato, Raffaele; Nitti, Davide O.; Bovenga, Fabio; Refice, Alberto; Wasowski, Janusz; Chiaradia, Maria T.

2013-10-01

Classical applications of the MTInSAR techniques have been carried out in the past on medium resolution data acquired by the ERS, Envisat (ENV) and Radarsat sensors. The new generation of high-resolution X-Band SAR sensors, such as TerraSAR-X (TSX) and the COSMO-SkyMed (CSK) constellation allows acquiring data with spatial resolution reaching metric/submetric values. Thanks to the finer spatial resolution with respect to C-band data, X-band InSAR applications result very promising for monitoring single man-made structures (buildings, bridges, railways and highways), as well as landslides. This is particularly relevant where C-band data show low density of coherent scatterers. Moreover, thanks again to the higher resolution, it is possible to infer reliable estimates of the displacement rates with a number of SAR scenes significantly lower than in C-band within the same time span or by using more images acquired in a narrower time span. We present examples of the application of a Persistent Scatterers Interferometry technique, namely the SPINUA algorithm, to data acquired by ENV, TSX and CSK on selected number of sites. Different cases are considered concerning monitoring of both instable slopes and infrastructure. Results are compared and commented with particular attention paid to the advantages provided by the new generation of X-band high resolution space-borne SAR sensors.

TIE POINTS EXTRACTION FOR SAR IMAGES BASED ON DIFFERENTIAL CONSTRAINTS

Directory of Open Access Journals (Sweden)

X. Xiong

2018-04-01

Full Text Available Automatically extracting tie points (TPs on large-size synthetic aperture radar (SAR images is still challenging because the efficiency and correct ratio of the image matching need to be improved. This paper proposes an automatic TPs extraction method based on differential constraints for large-size SAR images obtained from approximately parallel tracks, between which the relative geometric distortions are small in azimuth direction and large in range direction. Image pyramids are built firstly, and then corresponding layers of pyramids are matched from the top to the bottom. In the process, the similarity is measured by the normalized cross correlation (NCC algorithm, which is calculated from a rectangular window with the long side parallel to the azimuth direction. False matches are removed by the differential constrained random sample consensus (DC-RANSAC algorithm, which appends strong constraints in azimuth direction and weak constraints in range direction. Matching points in the lower pyramid images are predicted with the local bilinear transformation model in range direction. Experiments performed on ENVISAT ASAR and Chinese airborne SAR images validated the efficiency, correct ratio and accuracy of the proposed method.

Information theoretic bounds for compressed sensing in SAR imaging

International Nuclear Information System (INIS)

Jingxiong, Zhang; Ke, Yang; Jianzhong, Guo

2014-01-01

Compressed sensing (CS) is a new framework for sampling and reconstructing sparse signals from measurements significantly fewer than those prescribed by Nyquist rate in the Shannon sampling theorem. This new strategy, applied in various application areas including synthetic aperture radar (SAR), relies on two principles: sparsity, which is related to the signals of interest, and incoherence, which refers to the sensing modality. An important question in CS-based SAR system design concerns sampling rate necessary and sufficient for exact or approximate recovery of sparse signals. In the literature, bounds of measurements (or sampling rate) in CS have been proposed from the perspective of information theory. However, these information-theoretic bounds need to be reviewed and, if necessary, validated for CS-based SAR imaging, as there are various assumptions made in the derivations of lower and upper bounds on sub-Nyquist sampling rates, which may not hold true in CS-based SAR imaging. In this paper, information-theoretic bounds of sampling rate will be analyzed. For this, the SAR measurement system is modeled as an information channel, with channel capacity and rate-distortion characteristics evaluated to enable the determination of sampling rates required for recovery of sparse scenes. Experiments based on simulated data will be undertaken to test the theoretic bounds against empirical results about sampling rates required to achieve certain detection error probabilities

Feature-Based Nonlocal Polarimetric SAR Filtering

Directory of Open Access Journals (Sweden)

Xiaoli Xing

2017-10-01

Full Text Available Polarimetric synthetic aperture radar (PolSAR images are inherently contaminated by multiplicative speckle noise, which complicates the image interpretation and image analyses. To reduce the speckle effect, several adaptive speckle filters have been developed based on the weighted average of the similarity measures commonly depending on the model or probability distribution, which are often affected by the distribution parameters and modeling texture components. In this paper, a novel filtering method introduces the coefficient of variance ( CV and Pauli basis (PB to measure the similarity, and the two features are combined with the framework of the nonlocal mean filtering. The CV is used to describe the complexity of various scenes and distinguish the scene heterogeneity; moreover, the Pauli basis is able to express the polarimetric information in PolSAR image processing. This proposed filtering combines the CV and Pauli basis to improve the estimation accuracy of the similarity weights. Then, the similarity of the features is deduced according to the test statistic. Subsequently, the filtering is proceeded by using the nonlocal weighted estimation. The performance of the proposed filter is tested with the simulated images and real PolSAR images, which are acquired by AIRSAR system and ESAR system. The qualitative and quantitative experiments indicate the validity of the proposed method by comparing with the widely-used despeckling methods.

Global Rapid Flood Mapping System with Spaceborne SAR Data

Science.gov (United States)

Yun, S. H.; Owen, S. E.; Hua, H.; Agram, P. S.; Fattahi, H.; Liang, C.; Manipon, G.; Fielding, E. J.; Rosen, P. A.; Webb, F.; Simons, M.

2017-12-01

As part of the Advanced Rapid Imaging and Analysis (ARIA) project for Natural Hazards, at NASA's Jet Propulsion Laboratory and California Institute of Technology, we have developed an automated system that produces derived products for flood extent map generation using spaceborne SAR data. The system takes user's input of area of interest polygons and time window for SAR data search (pre- and post-event). Then the system automatically searches and downloads SAR data, processes them to produce coregistered SAR image pairs, and generates log amplitude ratio images from each pair. Currently the system is automated to support SAR data from the European Space Agency's Sentinel-1A/B satellites. We have used the system to produce flood extent maps from Sentinel-1 SAR data for the May 2017 Sri Lanka floods, which killed more than 200 people and displaced about 600,000 people. Our flood extent maps were delivered to the Red Cross to support response efforts. Earlier we also responded to the historic August 2016 Louisiana floods in the United States, which claimed 13 people's lives and caused over $10 billion property damage. For this event, we made synchronized observations from space, air, and ground in close collaboration with USGS and NOAA. The USGS field crews acquired ground observation data, and NOAA acquired high-resolution airborne optical imagery within the time window of +/-2 hours of the SAR data acquisition by JAXA's ALOS-2 satellite. The USGS coordinates of flood water boundaries were used to calibrate our flood extent map derived from the ALOS-2 SAR data, and the map was delivered to FEMA for estimating the number of households affected. Based on the lessons learned from this response effort, we customized the ARIA system automation for rapid flood mapping and developed a mobile friendly web app that can easily be used in the field for data collection. Rapid automatic generation of SAR-based global flood maps calibrated with independent observations from

Exploiting Deep Matching and SAR Data for the Geo-Localization Accuracy Improvement of Optical Satellite Images

Directory of Open Access Journals (Sweden)

Nina Merkle

2017-06-01

Full Text Available Improving the geo-localization of optical satellite images is an important pre-processing step for many remote sensing tasks like monitoring by image time series or scene analysis after sudden events. These tasks require geo-referenced and precisely co-registered multi-sensor data. Images captured by the high resolution synthetic aperture radar (SAR satellite TerraSAR-X exhibit an absolute geo-location accuracy within a few decimeters. These images represent therefore a reliable source to improve the geo-location accuracy of optical images, which is in the order of tens of meters. In this paper, a deep learning-based approach for the geo-localization accuracy improvement of optical satellite images through SAR reference data is investigated. Image registration between SAR and optical images requires few, but accurate and reliable matching points. These are derived from a Siamese neural network. The network is trained using TerraSAR-X and PRISM image pairs covering greater urban areas spread over Europe, in order to learn the two-dimensional spatial shifts between optical and SAR image patches. Results confirm that accurate and reliable matching points can be generated with higher matching accuracy and precision with respect to state-of-the-art approaches.

Correcting Spatial Variance of RCM for GEO SAR Imaging Based on Time-Frequency Scaling

Science.gov (United States)

Yu, Ze; Lin, Peng; Xiao, Peng; Kang, Lihong; Li, Chunsheng

2016-01-01

Compared with low-Earth orbit synthetic aperture radar (SAR), a geosynchronous (GEO) SAR can have a shorter revisit period and vaster coverage. However, relative motion between this SAR and targets is more complicated, which makes range cell migration (RCM) spatially variant along both range and azimuth. As a result, efficient and precise imaging becomes difficult. This paper analyzes and models spatial variance for GEO SAR in the time and frequency domains. A novel algorithm for GEO SAR imaging with a resolution of 2 m in both the ground cross-range and range directions is proposed, which is composed of five steps. The first is to eliminate linear azimuth variance through the first azimuth time scaling. The second is to achieve RCM correction and range compression. The third is to correct residual azimuth variance by the second azimuth time-frequency scaling. The fourth and final steps are to accomplish azimuth focusing and correct geometric distortion. The most important innovation of this algorithm is implementation of the time-frequency scaling to correct high-order azimuth variance. As demonstrated by simulation results, this algorithm can accomplish GEO SAR imaging with good and uniform imaging quality over the entire swath. PMID:27428974

Individual Building Extraction from TerraSAR-X Images Based on Ontological Semantic Analysis

Directory of Open Access Journals (Sweden)

Rong Gui

2016-08-01

Full Text Available Accurate building information plays a crucial role for urban planning, human settlements and environmental management. Synthetic aperture radar (SAR images, which deliver images with metric resolution, allow for analyzing and extracting detailed information on urban areas. In this paper, we consider the problem of extracting individual buildings from SAR images based on domain ontology. By analyzing a building scattering model with different orientations and structures, the building ontology model is set up to express multiple characteristics of individual buildings. Under this semantic expression framework, an object-based SAR image segmentation method is adopted to provide homogeneous image objects, and three categories of image object features are extracted. Semantic rules are implemented by organizing image object features, and the individual building objects expression based on an ontological semantic description is formed. Finally, the building primitives are used to detect buildings among the available image objects. Experiments on TerraSAR-X images of Foshan city, China, with a spatial resolution of 1.25 m × 1.25 m, have shown the total extraction rates are above 84%. The results indicate the ontological semantic method can exactly extract flat-roof and gable-roof buildings larger than 250 pixels with different orientations.

SAR image formation with azimuth interpolation after azimuth transform

Science.gov (United States)

Doerry,; Armin W. , Martin; Grant D. , Holzrichter; Michael, W [Albuquerque, NM

2008-07-08

Two-dimensional SAR data can be processed into a rectangular grid format by subjecting the SAR data to a Fourier transform operation, and thereafter to a corresponding interpolation operation. Because the interpolation operation follows the Fourier transform operation, the interpolation operation can be simplified, and the effect of interpolation errors can be diminished. This provides for the possibility of both reducing the re-grid processing time, and improving the image quality.

AN AUTOMATIC OPTICAL AND SAR IMAGE REGISTRATION METHOD USING ITERATIVE MULTI-LEVEL AND REFINEMENT MODEL

Directory of Open Access Journals (Sweden)

C. Xu

2016-06-01

Full Text Available Automatic image registration is a vital yet challenging task, particularly for multi-sensor remote sensing images. Given the diversity of the data, it is unlikely that a single registration algorithm or a single image feature will work satisfactorily for all applications. Focusing on this issue, the mainly contribution of this paper is to propose an automatic optical-to-SAR image registration method using –level and refinement model: Firstly, a multi-level strategy of coarse-to-fine registration is presented, the visual saliency features is used to acquire coarse registration, and then specific area and line features are used to refine the registration result, after that, sub-pixel matching is applied using KNN Graph. Secondly, an iterative strategy that involves adaptive parameter adjustment for re-extracting and re-matching features is presented. Considering the fact that almost all feature-based registration methods rely on feature extraction results, the iterative strategy improve the robustness of feature matching. And all parameters can be automatically and adaptively adjusted in the iterative procedure. Thirdly, a uniform level set segmentation model for optical and SAR images is presented to segment conjugate features, and Voronoi diagram is introduced into Spectral Point Matching (VSPM to further enhance the matching accuracy between two sets of matching points. Experimental results show that the proposed method can effectively and robustly generate sufficient, reliable point pairs and provide accurate registration.

Enhancement of SAR images using fuzzy shrinkage technique in ...

Indian Academy of Sciences (India)

Shivakumara Swamy Puranik Math

2017-08-03

Aug 3, 2017 ... not use threshold approach only by proper selection of shrinking parameter the speckle in SAR image is ... but cost estimation of hyper-parameters will be high. The ..... To find the effectiveness of the proposed image in a.

Improvement of the Accuracy of InSAR Image Co-Registration Based On Tie Points – A Review

Directory of Open Access Journals (Sweden)

Xiaoli Ding

2009-02-01

Full Text Available Interferometric Synthetic Aperture Radar (InSAR is a new measurement technology, making use of the phase information contained in the Synthetic Aperture Radar (SAR images. InSAR has been recognized as a potential tool for the generation of digital elevation models (DEMs and the measurement of ground surface deformations. However, many critical factors affect the quality of InSAR data and limit its applications. One of the factors is InSAR data processing, which consists of image co-registration, interferogram generation, phase unwrapping and geocoding. The co-registration of InSAR images is the first step and dramatically influences the accuracy of InSAR products. In this paper, the principle and processing procedures of InSAR techniques are reviewed. One of important factors, tie points, to be considered in the improvement of the accuracy of InSAR image co-registration are emphatically reviewed, such as interval of tie points, extraction of feature points, window size for tie point matching and the measurement for the quality of an interferogram.

A Novel Sidelobe Reduction Algorithm Based on Two-Dimensional Sidelobe Correction Using D-SVA for Squint SAR Images

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Min Liu

2018-03-01

Full Text Available Sidelobe reduction is a very primary task for synthetic aperture radar (SAR images. Various methods have been proposed for broadside SAR, which can suppress the sidelobes effectively while maintaining high image resolution at the same time. Alternatively, squint SAR, especially highly squint SAR, has emerged as an important tool that provides more mobility and flexibility and has become a focus of recent research studies. One of the research challenges for squint SAR is how to resolve the severe range-azimuth coupling of echo signals. Unlike broadside SAR images, the range and azimuth sidelobes of the squint SAR images no longer locate on the principal axes with high probability. Thus the spatially variant apodization (SVA filters could hardly get all the sidelobe information, and hence the sidelobe reduction process is not optimal. In this paper, we present an improved algorithm called double spatially variant apodization (D-SVA for better sidelobe suppression. Satisfactory sidelobe reduction results are achieved with the proposed algorithm by comparing the squint SAR images to the broadside SAR images. Simulation results also demonstrate the reliability and efficiency of the proposed method.

Coupling SAR X-band and optical data for NDVI retrieval: model calibration and validation on two test areas

Science.gov (United States)

Capodici, Fulvio; D'Urso, Guido; Maltese, Antonino; Ciraolo, Giuseppe

2013-10-01

Sustainability of modern agro-hydrology requires the knowledge of spatial and temporal variability of vegetation biomass to optimize management of land and water resources. Diversely from optical imaging, temporal resolution of active sensors, such as SAR, is not limited by sky cloudiness; thus, they may be combined with optical imageries to provide a more continuous monitoring of land surfaces. Several new SAR missions (e.g., ALOS-PALSAR, COSMO-SkyMed 1 and 2, TerraSAR-X, TerraSAR-X2, Sentinel 1) acquiring at X-, C- and L-bands and dual polarization capability, are characterized by a short revisit time (from 12 h to ~10 days) and high spatial resolution (COSMOSkyMed images and 2 Landsat 7 SLC-off images were acquired in the southwestern part of Sicily (Italy) between 8 and 25 August 2011. Determination coefficients of the validation set were similar to those of the calibration set. Results confirm that VISAR obtained using the combined model is a suitable surrogate of VIopt if estimated at parcel scale.

Ground Subsidence over Beijing-Tianjin-Hebei Region during Three Periods of 1992 to 2014 Monitored by Interferometric SAR

Directory of Open Access Journals (Sweden)

ZHANG Yonghong

2016-09-01

Full Text Available The Beijing-Tianjin-Hebei region suffers the most serious ground subsidence in China, which has caused huge economic losses every year. Therefore, ground subsidence was listed as an important mission in the project of geographic conditions monitoring over Beijing-Tianjin-Hebei launched by the National Administration of Surveying, Mapping and Geoinformation in 2013. In this paper, we propose a methodology of ground subsidence monitoring over wide area, which is entitled "multiple master-image coherent target small-baseline interferometric SAR (MCTSB-InSAR". MCTSB-InSAR is an improved time series InSAR technique with some unique features. SAR datasets used for ground subsidence monitoring over the Beijing-Tianjin-Hebei region include ERS-1/2 SAR images acquired between 1992 to 2000, ENVISAT ASAR images acquired between 2003 to 2010 and RADARSAT-2 images acquired between 2012 to 2014. This research represents a first ever effort on mapping ground subsidence over Beijing-Tianjin-Hebei region and over such as a long time span in China. In comparison with more than 120 leveling measurements collected in Beijing and Tianjin, the derived subsidence velocity has the accuracy of 8.7mm/year (1992—2000, 4.7mm/year (2003—2010, and 5.4mm/year (2012—2014 respectively. The spatial-temporal characteristics of the development of ground subsidence in Beijing and Tianjin are analyzed. In general, ground subsidence in Beijing kept continuously expanding in the period of 1992 to 2014. While, ground subsidence in Tianjin had already been serious in 1990s, had dramatically expanded during 2000s, and started to alleviate in recent years. The monitoring result is of high significance for prevention and mitigation of ground subsidence disaster, for making development plan, for efficient and effective utilization of water resource, and for adjustment of economic framework of this region. The result also indicates the effectiveness and reliability of the MCTSB-InSAR

SAR image regularization with fast approximate discrete minimization.

Science.gov (United States)

Denis, Loïc; Tupin, Florence; Darbon, Jérôme; Sigelle, Marc

2009-07-01

Synthetic aperture radar (SAR) images, like other coherent imaging modalities, suffer from speckle noise. The presence of this noise makes the automatic interpretation of images a challenging task and noise reduction is often a prerequisite for successful use of classical image processing algorithms. Numerous approaches have been proposed to filter speckle noise. Markov random field (MRF) modelization provides a convenient way to express both data fidelity constraints and desirable properties of the filtered image. In this context, total variation minimization has been extensively used to constrain the oscillations in the regularized image while preserving its edges. Speckle noise follows heavy-tailed distributions, and the MRF formulation leads to a minimization problem involving nonconvex log-likelihood terms. Such a minimization can be performed efficiently by computing minimum cuts on weighted graphs. Due to memory constraints, exact minimization, although theoretically possible, is not achievable on large images required by remote sensing applications. The computational burden of the state-of-the-art algorithm for approximate minimization (namely the alpha -expansion) is too heavy specially when considering joint regularization of several images. We show that a satisfying solution can be reached, in few iterations, by performing a graph-cut-based combinatorial exploration of large trial moves. This algorithm is applied to joint regularization of the amplitude and interferometric phase in urban area SAR images.

Urban Monitoring Based on SENTINEL-1 Data Using Permanent Scatterer Interferometry and SAR Tomography

Science.gov (United States)

Crosetto, M.; Budillon, A.; Johnsy, A.; Schirinzi, G.; Devanthéry, N.; Monserrat, O.; Cuevas-González, M.

2018-04-01

A lot of research and development has been devoted to the exploitation of satellite SAR images for deformation measurement and monitoring purposes since Differential Interferometric Synthetic Apertura Radar (InSAR) was first described in 1989. In this work, we consider two main classes of advanced DInSAR techniques: Persistent Scatterer Interferometry and Tomographic SAR. Both techniques make use of multiple SAR images acquired over the same site and advanced procedures to separate the deformation component from the other phase components, such as the residual topographic component, the atmospheric component, the thermal expansion component and the phase noise. TomoSAR offers the advantage of detecting either single scatterers presenting stable proprieties over time (Persistent Scatterers) and multiple scatterers interfering within the same range-azimuth resolution cell, a significant improvement for urban areas monitoring. This paper addresses a preliminary inter-comparison of the results of both techniques, for a test site located in the metropolitan area of Barcelona (Spain), where interferometric Sentinel-1 data were analysed.

Crop Classification by Polarimetric SAR

DEFF Research Database (Denmark)

Skriver, Henning; Svendsen, Morten Thougaard; Nielsen, Flemming

1999-01-01

Polarimetric SAR-data of agricultural fields have been acquired by the Danish polarimetric L- and C-band SAR (EMISAR) during a number of missions at the Danish agricultural test site Foulum during 1995. The data are used to study the classification potential of polarimetric SAR data using...

Parametric estimation of time varying baselines in airborne interferometric SAR

DEFF Research Database (Denmark)

Mohr, Johan Jacob; Madsen, Søren Nørvang

1996-01-01

A method for estimation of time varying spatial baselines in airborne interferometric synthetic aperture radar (SAR) is described. The range and azimuth distortions between two images acquired with a non-linear baseline are derived. A parametric model of the baseline is then, in a least square...... sense, estimated from image shifts obtained by cross correlation of numerous small patches throughout the image. The method has been applied to airborne EMISAR imagery from the 1995 campaign over the Storstrommen Glacier in North East Greenland conducted by the Danish Center for Remote Sensing. This has...... reduced the baseline uncertainties from several meters to the centimeter level in a 36 km scene. Though developed for airborne SAR the method can easily be adopted to satellite data...

A NEW SAR CLASSIFICATION SCHEME FOR SEDIMENTS ON INTERTIDAL FLATS BASED ON MULTI-FREQUENCY POLARIMETRIC SAR IMAGERY

Directory of Open Access Journals (Sweden)

W. Wang

2017-11-01

Full Text Available We present a new classification scheme for muddy and sandy sediments on exposed intertidal flats, which is based on synthetic aperture radar (SAR data, and use ALOS-2 (L-band, Radarsat-2 (C-band and TerraSAR-X (X-band fully polarimetric SAR imagery to demonstrate its effectiveness. Four test sites on the German North Sea coast were chosen, which represent typical surface compositions of different sediments, vegetation, and habitats, and of which a large amount of SAR is used for our analyses. Both Freeman-Durden and Cloude-Pottier polarimetric decomposition are utilized, and an additional descriptor called Double-Bounce Eigenvalue Relative Difference (DERD is introduced into the feature sets instead of the original polarimetric intensity channels. The classification is conducted following Random Forest theory, and the results are verified using ground truth data from field campaigns and an existing classification based on optical imagery. In addition, the use of Kennaugh elements for classification purposes is demonstrated using both fully and dual-polarization multi-frequency and multi-temporal SAR data. Our results show that the proposed classification scheme can be applied for the discrimination of muddy and sandy sediments using L-, C-, and X-band SAR images, while SAR imagery acquired at short wavelengths (C- and X-band can also be used to detect more detailed features such as bivalve beds on intertidal flats.

Discrimination of Oil Slicks and Lookalikes in Polarimetric SAR Images Using CNN.

Science.gov (United States)

Guo, Hao; Wu, Danni; An, Jubai

2017-08-09

Oil slicks and lookalikes (e.g., plant oil and oil emulsion) all appear as dark areas in polarimetric Synthetic Aperture Radar (SAR) images and are highly heterogeneous, so it is very difficult to use a single feature that can allow classification of dark objects in polarimetric SAR images as oil slicks or lookalikes. We established multi-feature fusion to support the discrimination of oil slicks and lookalikes. In the paper, simple discrimination analysis is used to rationalize a preferred features subset. The features analyzed include entropy, alpha, and Single-bounce Eigenvalue Relative Difference (SERD) in the C-band polarimetric mode. We also propose a novel SAR image discrimination method for oil slicks and lookalikes based on Convolutional Neural Network (CNN). The regions of interest are selected as the training and testing samples for CNN on the three kinds of polarimetric feature images. The proposed method is applied to a training data set of 5400 samples, including 1800 crude oil, 1800 plant oil, and 1800 oil emulsion samples. In the end, the effectiveness of the method is demonstrated through the analysis of some experimental results. The classification accuracy obtained using 900 samples of test data is 91.33%. It is here observed that the proposed method not only can accurately identify the dark spots on SAR images but also verify the ability of the proposed algorithm to classify unstructured features.

STUDY ON THE CLASSIFICATION OF GAOFEN-3 POLARIMETRIC SAR IMAGES USING DEEP NEURAL NETWORK

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J. Zhang

2018-04-01

Full Text Available Polarimetric Synthetic Aperture Radar（POLSAR） imaging principle determines that the image quality will be affected by speckle noise. So the recognition accuracy of traditional image classification methods will be reduced by the effect of this interference. Since the date of submission, Deep Convolutional Neural Network impacts on the traditional image processing methods and brings the field of computer vision to a new stage with the advantages of a strong ability to learn deep features and excellent ability to fit large datasets. Based on the basic characteristics of polarimetric SAR images, the paper studied the types of the surface cover by using the method of Deep Learning. We used the fully polarimetric SAR features of different scales to fuse RGB images to the GoogLeNet model based on convolution neural network Iterative training, and then use the trained model to test the classification of data validation.First of all, referring to the optical image, we mark the surface coverage type of GF-3 POLSAR image with 8m resolution, and then collect the samples according to different categories. To meet the GoogLeNet model requirements of 256 × 256 pixel image input and taking into account the lack of full-resolution SAR resolution, the original image should be pre-processed in the process of resampling. In this paper, POLSAR image slice samples of different scales with sampling intervals of 2 m and 1 m to be trained separately and validated by the verification dataset. Among them, the training accuracy of GoogLeNet model trained with resampled 2-m polarimetric SAR image is 94.89 %, and that of the trained SAR image with resampled 1 m is 92.65 %.

Study on the Classification of GAOFEN-3 Polarimetric SAR Images Using Deep Neural Network

Science.gov (United States)

Zhang, J.; Zhang, J.; Zhao, Z.

2018-04-01

Polarimetric Synthetic Aperture Radar (POLSAR) imaging principle determines that the image quality will be affected by speckle noise. So the recognition accuracy of traditional image classification methods will be reduced by the effect of this interference. Since the date of submission, Deep Convolutional Neural Network impacts on the traditional image processing methods and brings the field of computer vision to a new stage with the advantages of a strong ability to learn deep features and excellent ability to fit large datasets. Based on the basic characteristics of polarimetric SAR images, the paper studied the types of the surface cover by using the method of Deep Learning. We used the fully polarimetric SAR features of different scales to fuse RGB images to the GoogLeNet model based on convolution neural network Iterative training, and then use the trained model to test the classification of data validation.First of all, referring to the optical image, we mark the surface coverage type of GF-3 POLSAR image with 8m resolution, and then collect the samples according to different categories. To meet the GoogLeNet model requirements of 256 × 256 pixel image input and taking into account the lack of full-resolution SAR resolution, the original image should be pre-processed in the process of resampling. In this paper, POLSAR image slice samples of different scales with sampling intervals of 2 m and 1 m to be trained separately and validated by the verification dataset. Among them, the training accuracy of GoogLeNet model trained with resampled 2-m polarimetric SAR image is 94.89 %, and that of the trained SAR image with resampled 1 m is 92.65 %.

Multi-linear sparse reconstruction for SAR imaging based on higher-order SVD

Science.gov (United States)

Gao, Yu-Fei; Gui, Guan; Cong, Xun-Chao; Yang, Yue; Zou, Yan-Bin; Wan, Qun

2017-12-01

This paper focuses on the spotlight synthetic aperture radar (SAR) imaging for point scattering targets based on tensor modeling. In a real-world scenario, scatterers usually distribute in the block sparse pattern. Such a distribution feature has been scarcely utilized by the previous studies of SAR imaging. Our work takes advantage of this structure property of the target scene, constructing a multi-linear sparse reconstruction algorithm for SAR imaging. The multi-linear block sparsity is introduced into higher-order singular value decomposition (SVD) with a dictionary constructing procedure by this research. The simulation experiments for ideal point targets show the robustness of the proposed algorithm to the noise and sidelobe disturbance which always influence the imaging quality of the conventional methods. The computational resources requirement is further investigated in this paper. As a consequence of the algorithm complexity analysis, the present method possesses the superiority on resource consumption compared with the classic matching pursuit method. The imaging implementations for practical measured data also demonstrate the effectiveness of the algorithm developed in this paper.

Autofocus algorithm for curvilinear SAR imaging

Science.gov (United States)

Bleszynski, E.; Bleszynski, M.; Jaroszewicz, T.

2012-05-01

We describe an approach to autofocusing for large apertures on curved SAR trajectories. It is a phase-gradient type method in which phase corrections compensating trajectory perturbations are estimated not directly from the image itself, but rather on the basis of partial" SAR data { functions of the slow and fast times { recon- structed (by an appropriate forward-projection procedure) from windowed scene patches, of sizes comparable to distances between distinct targets or localized features of the scene. The resulting partial data" can be shown to contain the same information on the phase perturbations as that in the original data, provided the frequencies of the perturbations do not exceed a quantity proportional to the patch size. The algorithm uses as input a sequence of conventional scene images based on moderate-size subapertures constituting the full aperture for which the phase corrections are to be determined. The subaperture images are formed with pixel sizes comparable to the range resolution which, for the optimal subaperture size, should be also approximately equal the cross-range resolution. The method does not restrict the size or shape of the synthetic aperture and can be incorporated in the data collection process in persistent sensing scenarios. The algorithm has been tested on the publicly available set of GOTCHA data, intentionally corrupted by random-walk-type trajectory uctuations (a possible model of errors caused by imprecise inertial navigation system readings) of maximum frequencies compatible with the selected patch size. It was able to eciently remove image corruption for apertures of sizes up to 360 degrees.

Effect of Antenna Pointing Errors on SAR Imaging Considering the Change of the Point Target Location

Science.gov (United States)

Zhang, Xin; Liu, Shijie; Yu, Haifeng; Tong, Xiaohua; Huang, Guoman

2018-04-01

Towards spaceborne spotlight SAR, the antenna is regulated by the SAR system with specific regularity, so the shaking of the internal mechanism is inevitable. Moreover, external environment also has an effect on the stability of SAR platform. Both of them will cause the jitter of the SAR platform attitude. The platform attitude instability will introduce antenna pointing error on both the azimuth and range directions, and influence the acquisition of SAR original data and ultimate imaging quality. In this paper, the relations between the antenna pointing errors and the three-axis attitude errors are deduced, then the relations between spaceborne spotlight SAR imaging of the point target and antenna pointing errors are analysed based on the paired echo theory, meanwhile, the change of the azimuth antenna gain is considered as the spotlight SAR platform moves ahead. The simulation experiments manifest the effects on spotlight SAR imaging caused by antenna pointing errors are related to the target location, that is, the pointing errors of the antenna beam will severely influence the area far away from the scene centre of azimuth direction in the illuminated scene.

RESEARCH ON COORDINATE TRANSFORMATION METHOD OF GB-SAR IMAGE SUPPORTED BY 3D LASER SCANNING TECHNOLOGY

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P. Wang

2018-04-01

Full Text Available In the image plane of GB-SAR, identification of deformation distribution is usually carried out by artificial interpretation. This method requires analysts to have adequate experience of radar imaging and target recognition, otherwise it can easily cause false recognition of deformation target or region. Therefore, it is very meaningful to connect two-dimensional (2D plane coordinate system with the common three-dimensional (3D terrain coordinate system. To improve the global accuracy and reliability of the transformation from 2D coordinates of GB-SAR images to local 3D coordinates, and overcome the limitation of traditional similarity transformation parameter estimation method, 3D laser scanning data is used to assist the transformation of GB-SAR image coordinates. A straight line fitting method for calculating horizontal angle was proposed in this paper. After projection into a consistent imaging plane, we can calculate horizontal rotation angle by using the linear characteristics of the structure in radar image and the 3D coordinate system. Aided by external elevation information by 3D laser scanning technology, we completed the matching of point clouds and pixels on the projection plane according to the geometric projection principle of GB-SAR imaging realizing the transformation calculation of GB-SAR image coordinates to local 3D coordinates. Finally, the effectiveness of the method is verified by the GB-SAR deformation monitoring experiment on the high slope of Geheyan dam.

Research on Coordinate Transformation Method of Gb-Sar Image Supported by 3d Laser Scanning Technology

Science.gov (United States)

Wang, P.; Xing, C.

2018-04-01

In the image plane of GB-SAR, identification of deformation distribution is usually carried out by artificial interpretation. This method requires analysts to have adequate experience of radar imaging and target recognition, otherwise it can easily cause false recognition of deformation target or region. Therefore, it is very meaningful to connect two-dimensional (2D) plane coordinate system with the common three-dimensional (3D) terrain coordinate system. To improve the global accuracy and reliability of the transformation from 2D coordinates of GB-SAR images to local 3D coordinates, and overcome the limitation of traditional similarity transformation parameter estimation method, 3D laser scanning data is used to assist the transformation of GB-SAR image coordinates. A straight line fitting method for calculating horizontal angle was proposed in this paper. After projection into a consistent imaging plane, we can calculate horizontal rotation angle by using the linear characteristics of the structure in radar image and the 3D coordinate system. Aided by external elevation information by 3D laser scanning technology, we completed the matching of point clouds and pixels on the projection plane according to the geometric projection principle of GB-SAR imaging realizing the transformation calculation of GB-SAR image coordinates to local 3D coordinates. Finally, the effectiveness of the method is verified by the GB-SAR deformation monitoring experiment on the high slope of Geheyan dam.

SAR Imaging through the Earth’s Ionosphere

Science.gov (United States)

2013-11-06

Xiaoqing Pi, Anthony Freeman, Bruce Chapman, Paul Rosen, and Zhenhong Li . Imaging ionospheric inhomogeneities using spaceborne synthetic aperture radar. J...resolution SAR phase correction. IEEE Trans. Aerosp. Electron. Syst., 30(3):827–835, 1994. [30] Lianlin Li and Fang Li . Ionosphere tomography based on...Manduchi and G. A. Mian . Accuracy analysis for correlation-based image registartion algorithms. In Proceedings of the 1993 IEEE International

Fast iterative censoring CFAR algorithm for ship detection from SAR images

Science.gov (United States)

Gu, Dandan; Yue, Hui; Zhang, Yuan; Gao, Pengcheng

2017-11-01

Ship detection is one of the essential techniques for ship recognition from synthetic aperture radar (SAR) images. This paper presents a fast iterative detection procedure to eliminate the influence of target returns on the estimation of local sea clutter distributions for constant false alarm rate (CFAR) detectors. A fast block detector is first employed to extract potential target sub-images; and then, an iterative censoring CFAR algorithm is used to detect ship candidates from each target blocks adaptively and efficiently, where parallel detection is available, and statistical parameters of G0 distribution fitting local sea clutter well can be quickly estimated based on an integral image operator. Experimental results of TerraSAR-X images demonstrate the effectiveness of the proposed technique.

Estimation of Boreal Forest Biomass Using Spaceborne SAR Systems

Science.gov (United States)

Saatchi, Sassan; Moghaddam, Mahta

1995-01-01

In this paper, we report on the use of a semiempirical algorithm derived from a two layer radar backscatter model for forest canopies. The model stratifies the forest canopy into crown and stem layers, separates the structural and biometric attributes of the canopy. The structural parameters are estimated by training the model with polarimetric SAR (synthetic aperture radar) data acquired over homogeneous stands with known above ground biomass. Given the structural parameters, the semi-empirical algorithm has four remaining parameters, crown biomass, stem biomass, surface soil moisture, and surface rms height that can be estimated by at least four independent SAR measurements. The algorithm has been used to generate biomass maps over the entire images acquired by JPL AIRSAR and SIR-C SAR systems. The semi-empirical algorithms are then modified to be used by single frequency radar systems such as ERS-1, JERS-1, and Radarsat. The accuracy. of biomass estimation from single channel radars is compared with the case when the channels are used together in synergism or in a polarimetric system.

Potential of TCPInSAR in Monitoring Linear Infrastructure with a Small Dataset of SAR Images: Application of the Donghai Bridge, China

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Lei Zhang

2018-03-01

Full Text Available Reliably monitoring deformation associated with linear infrastructures, such as long-span bridges, is vitally important to assess their structural health. In this paper, we attempt to employ satellite interferometric synthetic aperture radar (InSAR to map the deformation of Donghai Bridge over a half of an annual cycle. The bridge, as the fourth longest cross-sea bridge in the world, located in the north of Hangzhou Bay, East China Sea where the featureless sea surface largely occupied the radar image raises challenges to accurately co-register the coherent points along the bridge. To tackle the issues due to co-registration and the limited number of synthetic aperture radar (SAR images, we adopt the termed temporarily-coherent point (TCP InSAR (TCPInSAR technique to process the radar images. TCPs that are not necessarily coherent during the whole observation period can be identified within every two SAR acquisitions during the co-registration procedure based on the statistics of azimuth and range offsets. In the process, co-registration is performed only using the offsets of these TCPs, leading to improved interferometric phases and the local Delaunay triangulation is used to construct point pairs to reduce the atmospheric artifacts along the bridge. With the TCPInSAR method the deformation rate along the bridge is estimated with no need of phase unwrapping. The achieved result reveals that the Donghai Bridge suffered a line-of-sight (LOS deformation rate up to −2.3 cm/year from January 2009 to July 2009 at the cable-stayed part, which is likely due to the thermal expansion of cables.

Synthetic SAR Image Generation using Sensor, Terrain and Target Models

DEFF Research Database (Denmark)

Kusk, Anders; Abulaitijiang, Adili; Dall, Jørgen

2016-01-01

A tool to generate synthetic SAR images of objects set on a clutter background is described. The purpose is to generate images for training Automatic Target Recognition and Identification algorithms. The tool employs a commercial electromagnetic simulation program to calculate radar cross section...

Improved spatial mapping of rainfall events with spaceborne SAR imagery

Science.gov (United States)

Ulaby, F. T.; Brisco, B.; Dobson, C.

1983-01-01

The Seasat satellite acquired the first spaceborne synthetic-aperture radar (SAR) images of the earth's surface, in 1978, at a frequency of 1.275 GHz (L-band) in a like-polarization mode at incidence angles of 23 + or - 3 deg. Although this may not be the optimum system configuration for radar remote sensing of soil moisture, interpretation of two Seasat images of Iowa demonstrates the sensitivity of microwave backscatter to soil moisture content. In both scenes, increased image brightness, which represents more radar backscatter, can be related to previous rainfall activity in the two areas. Comparison of these images with ground-based rainfall observations illustrates the increased spatial coverage of the rainfall event that can be obtained from the satellite SAR data. These data can then be color-enhanced by a digital computer to produce aesthetically pleasing output products for the user community.

First Image Products from EcoSAR - Osa Peninsula, Costa Rica

Science.gov (United States)

Osmanoglu, Batuhan; Lee, SeungKuk; Rincon, Rafael; Fatuyinbo, Lola; Bollian, Tobias; Ranson, Jon

2016-01-01

Designed especially for forest ecosystem studies, EcoSAR employs state-of-the-art digital beamforming technology to generate wide-swath, high-resolution imagery. EcoSARs dual antenna single-pass imaging capability eliminates temporal decorrelation from polarimetric and interferometric analysis, increasing the signal strength and simplifying models used to invert forest structure parameters. Antennae are physically separated by 25 meters providing single pass interferometry. In this mode the radar is most sensitive to topography. With 32 active transmit and receive channels, EcoSARs digital beamforming is an order of magnitude more versatile than the digital beamforming employed on the upcoming NISAR mission. EcoSARs long wavelength (P-band, 435 MHz, 69 cm) measurements can be used to simulate data products for ESAs future BIOMASS mission, allowing scientists to develop algorithms before the launch of the satellite. EcoSAR can also be deployed to collect much needed data where BIOMASS satellite wont be allowed to collect data (North America, Europe and Arctic), filling in the gaps to keep a watchful eye on the global carbon cycle. EcoSAR can play a vital role in monitoring, reporting and verification schemes of internationals programs such as UN-REDD (United Nations Reducing Emissions from Deforestation and Degradation) benefiting global society. EcoSAR was developed and flown with support from NASA Earth Sciences Technology Offices Instrument Incubator Program.

Discrimination of Oil Slicks and Lookalikes in Polarimetric SAR Images Using CNN

OpenAIRE

Guo, Hao; Wu, Danni; An, Jubai

2017-01-01

Oil slicks and lookalikes (e.g., plant oil and oil emulsion) all appear as dark areas in polarimetric Synthetic Aperture Radar (SAR) images and are highly heterogeneous, so it is very difficult to use a single feature that can allow classification of dark objects in polarimetric SAR images as oil slicks or lookalikes. We established multi-feature fusion to support the discrimination of oil slicks and lookalikes. In the paper, simple discrimination analysis is used to rationalize a preferred f...

Extraction of lead and ridge characteristics from SAR images of sea ice

Science.gov (United States)

Vesecky, John F.; Smith, Martha P.; Samadani, Ramin

1990-01-01

Image-processing techniques for extracting the characteristics of lead and pressure ridge features in SAR images of sea ice are reported. The methods are applied to a SAR image of the Beaufort Sea collected from the Seasat satellite on October 3, 1978. Estimates of lead and ridge statistics are made, e.g., lead and ridge density (number of lead or ridge pixels per unit area of image) and the distribution of lead area and orientation as well as ridge length and orientation. The information derived is useful in both ice science and polar operations for such applications as albedo and heat and momentum transfer estimates, as well as ship routing and offshore engineering.

Ka-band InSAR Imaging and Analysis Based on IMU Data

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Shi Jun

2014-02-01

Full Text Available Compared with other bands, the millimeter wave Interferometric Synthetic Aperture Radar (InSAR has high accuracy and small size, which is a hot topic in InSAR research. On the other hand, shorter wavelength causes difficulties in 2D imaging and interferometric phase extraction. In this study, the imaging and phase performance of the streaming Back Projection (BP method combined with IMU data are analyzed and discussed on the basis of actual Ka-band InSAR data. It is found that because the wavelength of the Ka-band is short, it is more sensitive to the antenna phase-center history. To ensure the phase-preserving capacity, the IMU data must be used with accurate motion error compensation. Furthermore, during data processing, we verify the flat-earth-removing capacity of the BP algorithm that calculates and compensates the master and slave antenna phase centers individually.

APPLICATION OF FUSION WITH SAR AND OPTICAL IMAGES IN LAND USE CLASSIFICATION BASED ON SVM

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C. Bao

2012-07-01

Full Text Available As the increment of remote sensing data with multi-space resolution, multi-spectral resolution and multi-source, data fusion technologies have been widely used in geological fields. Synthetic Aperture Radar (SAR and optical camera are two most common sensors presently. The multi-spectral optical images express spectral features of ground objects, while SAR images express backscatter information. Accuracy of the image classification could be effectively improved fusing the two kinds of images. In this paper, Terra SAR-X images and ALOS multi-spectral images were fused for land use classification. After preprocess such as geometric rectification, radiometric rectification noise suppression and so on, the two kind images were fused, and then SVM model identification method was used for land use classification. Two different fusion methods were used, one is joining SAR image into multi-spectral images as one band, and the other is direct fusing the two kind images. The former one can raise the resolution and reserve the texture information, and the latter can reserve spectral feature information and improve capability of identifying different features. The experiment results showed that accuracy of classification using fused images is better than only using multi-spectral images. Accuracy of classification about roads, habitation and water bodies was significantly improved. Compared to traditional classification method, the method of this paper for fused images with SVM classifier could achieve better results in identifying complicated land use classes, especially for small pieces ground features.

Robust adaptive multichannel SAR processing based on covariance matrix reconstruction

Science.gov (United States)

Tan, Zhen-ya; He, Feng

2018-04-01

With the combination of digital beamforming (DBF) processing, multichannel synthetic aperture radar(SAR) systems in azimuth promise well in high-resolution and wide-swath imaging, whereas conventional processing methods don't take the nonuniformity of scattering coefficient into consideration. This paper brings up a robust adaptive Multichannel SAR processing method which utilizes the Capon spatial spectrum estimator to obtain the spatial spectrum distribution over all ambiguous directions first, and then the interference-plus-noise covariance Matrix is reconstructed based on definition to acquire the Multichannel SAR processing filter. The performance of processing under nonuniform scattering coefficient is promoted by this novel method and it is robust again array errors. The experiments with real measured data demonstrate the effectiveness and robustness of the proposed method.

The use of the DInSAR method in the monitoring of road damage caused by mining activities

Science.gov (United States)

Murdzek, Radosław; Malik, Hubert; Leśniak, Andrzej

2018-04-01

This paper reviews existing remote sensing methods of road damage detection and demonstrates the possibility of using DInSAR (Differential Interferometry SAR) method to identify endangered road sections. In this study two radar images collected by Sentinel-1 satellite have been used. Images were acquired with 24 days interval in 2015. The analysis allowed to estimate the scale of the post-mining deformation that occurred in Upper Silesia and to indicate areas where road infrastructure is particularly vulnerable to damage.

The Performance Analysis Based on SAR Sample Covariance Matrix

Directory of Open Access Journals (Sweden)

Esra Erten

2012-03-01

Full Text Available Multi-channel systems appear in several fields of application in science. In the Synthetic Aperture Radar (SAR context, multi-channel systems may refer to different domains, as multi-polarization, multi-interferometric or multi-temporal data, or even a combination of them. Due to the inherent speckle phenomenon present in SAR images, the statistical description of the data is almost mandatory for its utilization. The complex images acquired over natural media present in general zero-mean circular Gaussian characteristics. In this case, second order statistics as the multi-channel covariance matrix fully describe the data. For practical situations however, the covariance matrix has to be estimated using a limited number of samples, and this sample covariance matrix follow the complex Wishart distribution. In this context, the eigendecomposition of the multi-channel covariance matrix has been shown in different areas of high relevance regarding the physical properties of the imaged scene. Specifically, the maximum eigenvalue of the covariance matrix has been frequently used in different applications as target or change detection, estimation of the dominant scattering mechanism in polarimetric data, moving target indication, etc. In this paper, the statistical behavior of the maximum eigenvalue derived from the eigendecomposition of the sample multi-channel covariance matrix in terms of multi-channel SAR images is simplified for SAR community. Validation is performed against simulated data and examples of estimation and detection problems using the analytical expressions are as well given.

Imaging manifestations of the cavitation in pulmonary parenchyma of SARS

International Nuclear Information System (INIS)

Yuan Chunwang; Zhao Dawei; Wang Wei; Jia Cuiyu; Bai Chunsheng

2004-01-01

Objective: To investigate the imaging appearances of cavitation in pulmonary parenchyma and the clinical features of the cases of SARS. Methods: Chest imaging films and clinical data of 180 patients with clinically confirmed SARS were analyzed retrospectively. The imaging manifestations of cavitation and the clinical features of the patients were observed and evaluated. Results: Of 180 patients, cavitations were showed in 5 (2.8%), which were all found through X-ray or CT scanning. Most of them were round or irregular, and had thick wall. The 5 patients all had been in hospital and treated with more dosage antibiotics, antivirus medicines and glucocorticoid for long time, the glucocorticoid was used for 25-65 d, and in the first 10-15 days the dosage was 160-240 mg per day. In hospitalization, one of them had been diagnosed diabetes mellitus, four had increased fasting blood sugar, the counts of white blood cells [(14.1-20.4) x 10 9 /L] increased significantly, the percent of neutrophils might increased also. Meanwhile, there was a continue increase of lactate dehydrogenase (228.00-475.00 U/L), glutamic dehydrogenase (10.08-60.00 U/L) and hydroxybutyrate dehydrogenase (190.00-444.00 U/L) in lab examination. Conclusion: SARS can cause cavitation in pulmonary parenchyma in posterior process of the disease. CT scanning can find the cavitation earlier and accurately, catching the imaging features of them is helpful in differential diagnosis, guiding therapy and estimating prognosis

Research on the method of extracting DEM based on GBInSAR

Science.gov (United States)

Yue, Jianping; Yue, Shun; Qiu, Zhiwei; Wang, Xueqin; Guo, Leping

2016-05-01

Precise topographical information has a very important role in geology, hydrology, natural resources survey and deformation monitoring. The extracting DEM technology based on synthetic aperture radar interferometry (InSAR) obtains the three-dimensional elevation of the target area through the phase information of the radar image data. The technology has large-scale, high-precision, all-weather features. By changing track in the location of the ground radar system up and down, it can form spatial baseline. Then we can achieve the DEM of the target area by acquiring image data from different angles. Three-dimensional laser scanning technology can quickly, efficiently and accurately obtain DEM of target area, which can verify the accuracy of DEM extracted by GBInSAR. But research on GBInSAR in extracting DEM of the target area is a little. For lack of theory and lower accuracy problems in extracting DEM based on GBInSAR now, this article conducted research and analysis on its principle deeply. The article extracted the DEM of the target area, combined with GBInSAR data. Then it compared the DEM obtained by GBInSAR with the DEM obtained by three-dimensional laser scan data and made statistical analysis and normal distribution test. The results showed the DEM obtained by GBInSAR was broadly consistent with the DEM obtained by three-dimensional laser scanning. And its accuracy is high. The difference of both DEM approximately obeys normal distribution. It indicated that extracting the DEM of target area based on GBInSAR is feasible and provided the foundation for the promotion and application of GBInSAR.

3D Tomographic SAR Imaging in Densely Vegetated Mountainous Rural Areas in China and Sweden

Science.gov (United States)

Feng, L.; Muller, J. P., , Prof

2017-12-01

3D SAR Tomography (TomoSAR) and 4D SAR Differential Tomography (Diff-TomoSAR) exploit multi-baseline SAR data stacks to create an important new innovation of SAR Interferometry, to unscramble complex scenes with multiple scatterers mapped into the same SAR cell. In addition to this 3-D shape reconstruction and deformation solution in complex urban/infrastructure areas, and recent cryospheric ice investigations, emerging tomographic remote sensing applications include forest applications, e.g. tree height and biomass estimation, sub-canopy topographic mapping, and even search, rescue and surveillance. However, these scenes are characterized by temporal decorrelation of scatterers, orbital, tropospheric and ionospheric phase distortion and an open issue regarding possible height blurring and accuracy losses for TomoSAR applications particularly in densely vegetated mountainous rural areas. Thus, it is important to develop solutions for temporal decorrelation, orbital, tropospheric and ionospheric phase distortion.We report here on 3D imaging (especially in vertical layers) over densely vegetated mountainous rural areas using 3-D SAR imaging (SAR tomography) derived from data stacks of X-band COSMO-SkyMed Spotlight and L band ALOS-1 PALSAR data stacks over Dujiangyan Dam, Sichuan, China and L and P band airborne SAR data (BioSAR 2008 - ESA) in the Krycklan river catchment, Northern Sweden. The new TanDEM-X 12m DEM is used to assist co - registration of all the data stacks over China first. Then, atmospheric correction is being assessed using weather model data such as ERA-I, MERRA, MERRA-2, WRF; linear phase-topography correction and MODIS spectrometer correction will be compared and ionospheric correction methods are discussed to remove tropospheric and ionospheric delay. Then the new TomoSAR method with the TanDEM-X 12m DEM is described to obtain the number of scatterers inside each pixel, the scattering amplitude and phase of each scatterer and finally extract

IMAGE ENHANCEMENT AND SPECKLE REDUCTION OF FULL POLARIMETRIC SAR DATA BY GAUSSIAN MARKOV RANDOM FIELD

Directory of Open Access Journals (Sweden)

M. Mahdian

2013-09-01

Full Text Available In recent years, the use of Polarimetric Synthetic Aperture Radar (PolSAR data in different applications dramatically has been increased. In SAR imagery an interference phenomenon with random behavior exists which is called speckle noise. The interpretation of data encounters some troubles due to the presence of speckle which can be considered as a multiplicative noise affecting all coherent imaging systems. Indeed, speckle degrade radiometric resolution of PolSAR images, therefore it is needful to perform speckle filtering on the SAR data type. Markov Random Field (MRF has proven to be a powerful method for drawing out eliciting contextual information from remotely sensed images. In the present paper, a probability density function (PDF, which is fitted well with the PolSAR data based on the goodness-of-fit test, is first obtained for the pixel-wise analysis. Then the contextual smoothing is achieved with the MRF method. This novel speckle reduction method combines an advanced statistical distribution with spatial contextual information for PolSAR data. These two parts of information are combined based on weighted summation of pixel-wise and contextual models. This approach not only preserves edge information in the images, but also improves signal-to-noise ratio of the results. The method maintains the mean value of original signal in the homogenous areas and preserves the edges of features in the heterogeneous regions. Experiments on real medium resolution ALOS data from Tehran, and also high resolution full polarimetric SAR data over the Oberpfaffenhofen test-site in Germany, demonstrate the effectiveness of the algorithm compared with well-known despeckling methods.

SAR Image Simulation of Ship Targets Based on Multi-Path Scattering

Science.gov (United States)

Guo, Y.; Wang, H.; Ma, H.; Li, K.; Xia, Z.; Hao, Y.; Guo, H.; Shi, H.; Liao, X.; Yue, H.

2018-04-01

Synthetic Aperture Radar (SAR) plays an important role in the classification and recognition of ship targets because of its all-weather working ability and fine resolution. In SAR images, besides the sea clutter, the influence of the sea surface on the radar echo is also known as the so-called multipath effect. These multipath effects will generate some extra "pseudo images", which may cause the distortion of the target image and affect the estimation of the characteristic parameters. In this paper,the multipath effect of rough sea surface and its influence on the estimation of ship characteristic parameters are studied. The imaging of the first and the secondary reflection of sea surface is presented . The artifacts not only overlap with the image of the target itself, but may also appear in the sea near the target area. It is difficult to distinguish them, and this artifact has an effect on the length and width of the ship.

The Radiometric Measurement Quantity for SAR Images

OpenAIRE

Döring, Björn J.; Schwerdt, Marco

2013-01-01

A Synthetic Aperture Radar (SAR) system measures among other quantities the terrain radar reflectivity. After image calibration, the pixel intensities are commonly expressed in terms of radar cross sections (for point targets) or as backscatter coefficients (for distributed targets), which are directly related. This paper argues that pixel intensities are not generally proportional to radar cross section or derived physical quantities. The paper further proposes to replace the inaccurate term...

Multichannel High Resolution Wide Swath SAR Imaging for Hypersonic Air Vehicle with Curved Trajectory

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Rui Zhou

2018-01-01

Full Text Available Synthetic aperture radar (SAR equipped on the hypersonic air vehicle in near space has many advantages over the conventional airborne SAR. However, its high-speed maneuvering characteristics with curved trajectory result in serious range migration, and exacerbate the contradiction between the high resolution and wide swath. To solve this problem, this paper establishes the imaging geometrical model matched with the flight trajectory of the hypersonic platform and the multichannel azimuth sampling model based on the displaced phase center antenna (DPCA technology. Furthermore, based on the multichannel signal reconstruction theory, a more efficient spectrum reconstruction model using discrete Fourier transform is proposed to obtain the azimuth uniform sampling data. Due to the high complexity of the slant range model, it is difficult to deduce the processing algorithm for SAR imaging. Thus, an approximate range model is derived based on the minimax criterion, and the optimal second-order approximate coefficients of cosine function are obtained using the two-population coevolutionary algorithm. On this basis, aiming at the problem that the traditional Omega-K algorithm cannot compensate the residual phase with the difficulty of Stolt mapping along the range frequency axis, this paper proposes an Exact Transfer Function (ETF algorithm for SAR imaging, and presents a method of range division to achieve wide swath imaging. Simulation results verify the effectiveness of the ETF imaging algorithm.

Multichannel High Resolution Wide Swath SAR Imaging for Hypersonic Air Vehicle with Curved Trajectory.

Science.gov (United States)

Zhou, Rui; Sun, Jinping; Hu, Yuxin; Qi, Yaolong

2018-01-31

Synthetic aperture radar (SAR) equipped on the hypersonic air vehicle in near space has many advantages over the conventional airborne SAR. However, its high-speed maneuvering characteristics with curved trajectory result in serious range migration, and exacerbate the contradiction between the high resolution and wide swath. To solve this problem, this paper establishes the imaging geometrical model matched with the flight trajectory of the hypersonic platform and the multichannel azimuth sampling model based on the displaced phase center antenna (DPCA) technology. Furthermore, based on the multichannel signal reconstruction theory, a more efficient spectrum reconstruction model using discrete Fourier transform is proposed to obtain the azimuth uniform sampling data. Due to the high complexity of the slant range model, it is difficult to deduce the processing algorithm for SAR imaging. Thus, an approximate range model is derived based on the minimax criterion, and the optimal second-order approximate coefficients of cosine function are obtained using the two-population coevolutionary algorithm. On this basis, aiming at the problem that the traditional Omega-K algorithm cannot compensate the residual phase with the difficulty of Stolt mapping along the range frequency axis, this paper proposes an Exact Transfer Function (ETF) algorithm for SAR imaging, and presents a method of range division to achieve wide swath imaging. Simulation results verify the effectiveness of the ETF imaging algorithm.

An ice-motion tracking system at the Alaska SAR facility

Science.gov (United States)

Kwok, Ronald; Curlander, John C.; Pang, Shirley S.; Mcconnell, Ross

1990-01-01

An operational system for extracting ice-motion information from synthetic aperture radar (SAR) imagery is being developed as part of the Alaska SAR Facility. This geophysical processing system (GPS) will derive ice-motion information by automated analysis of image sequences acquired by radars on the European ERS-1, Japanese ERS-1, and Canadian RADARSAT remote sensing satellites. The algorithm consists of a novel combination of feature-based and area-based techniques for the tracking of ice floes that undergo translation and rotation between imaging passes. The system performs automatic selection of the image pairs for input to the matching routines using an ice-motion estimator. It is designed to have a daily throughput of ten image pairs. A description is given of the GPS system, including an overview of the ice-motion-tracking algorithm, the system architecture, and the ice-motion products that will be available for distribution to geophysical data users.

THz-SAR Vibrating Target Imaging via the Bayesian Method

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Bin Deng

2017-01-01

Full Text Available Target vibration bears important information for target recognition, and terahertz, due to significant micro-Doppler effects, has strong advantages for remotely sensing vibrations. In this paper, the imaging characteristics of vibrating targets with THz-SAR are at first analyzed. An improved algorithm based on an excellent Bayesian approach, that is, the expansion-compression variance-component (ExCoV method, has been proposed for reconstructing scattering coefficients of vibrating targets, which provides more robust and efficient initialization and overcomes the deficiencies of sidelobes as well as artifacts arising from the traditional correlation method. A real vibration measurement experiment of idle cars was performed to validate the range model. Simulated SAR data of vibrating targets and a tank model in a real background in 220 GHz show good performance at low SNR. Rapidly evolving high-power terahertz devices will offer viable THz-SAR application at a distance of several kilometers.

Unsupervised DInSAR processing chain for multi-scale displacement analysis

Science.gov (United States)

Casu, Francesco; Manunta, Michele

2016-04-01

Earth Observation techniques can be very helpful for the estimation of several sources of ground deformation due to their characteristics of large spatial coverage, high resolution and cost effectiveness. In this scenario, Differential Synthetic Aperture Radar Interferometry (DInSAR) is one of the most effective methodologies for its capability to generate spatially dense deformation maps at both global and local spatial scale, with centimeter to millimeter accuracy. DInSAR exploits the phase difference (interferogram) between SAR image pairs relevant to acquisitions gathered at different times, but with the same illumination geometry and from sufficiently close flight tracks, whose separation is typically referred to as baseline. Among several, the SBAS algorithm is one of the most used DInSAR approaches and it is aimed at generating displacement time series at a multi-scale level by exploiting a set of small baseline interferograms. SBAS, and generally DInSAR, has taken benefit from the large availability of spaceborne SAR data collected along years by several satellite systems, with particular regard to the European ERS and ENVISAT sensors, which have acquired SAR images worldwide during approximately 20 years. Moreover, since 2014 the new generation of Copernicus Sentinel satellites has started to acquire data with a short revisit time (12 days) and a global coverage policy, thus flooding the scientific EO community with an unprecedent amount of data. To efficiently manage such amount of data, proper processing facilities (as those coming from the emerging Cloud Computing technologies) have to be used, as well as novel algorithms aimed at their efficient exploitation have to be developed. In this work we present a set of results achieved by exploiting a recently proposed implementation of the SBAS algorithm, namely Parallel-SBAS (P-SBAS), which allows us to effectively process, in an unsupervised way and in a limited time frame, a huge number of SAR images

Bistatic SAR: Proof of Concept.

Energy Technology Data Exchange (ETDEWEB)

Yocky, David A.; Doren, Neall E.; Bacon, Terry A.; Wahl, Daniel E.; Eichel, Paul H.; Jakowatz, Charles V,; Delaplain, Gilbert G.; Dubbert, Dale F.; Tise, Bertice L.; White, Kyle R.

2014-10-01

Typical synthetic aperture RADAR (SAR) imaging employs a co-located RADAR transmitter and receiver. Bistatic SAR imaging separates the transmitter and receiver locations. A bistatic SAR configuration allows for the transmitter and receiver(s) to be in a variety of geometric alignments. Sandia National Laboratories (SNL) / New Mexico proposed the deployment of a ground-based RADAR receiver. This RADAR receiver was coupled with the capability of digitizing and recording the signal collected. SNL proposed the possibility of creating an image of targets the illuminating SAR observes. This document describes the developed hardware, software, bistatic SAR configuration, and its deployment to test the concept of a ground-based bistatic SAR. In the proof-of-concept experiments herein, the RADAR transmitter will be a commercial SAR satellite and the RADAR receiver will be deployed at ground level, observing and capturing RADAR ground/targets illuminated by the satellite system.

Long term landslide monitoring with Ground Based SAR

Science.gov (United States)

Monserrat, Oriol; Crosetto, Michele; Luzi, Guido; Gili, Josep; Moya, Jose; Corominas, Jordi

2014-05-01

In the last decade, Ground-Based (GBSAR) has proven to be a reliable microwave Remote Sensing technique in several application fields, especially for unstable slopes monitoring. GBSAR can provide displacement measurements over few squared kilometres areas and with a very high spatial and temporal resolution. This work is focused on the use of GBSAR technique for long term landslide monitoring based on a particular data acquisition configuration, which is called discontinuous GBSAR (D-GBSAR). In the most commonly used GBSAR configuration, the radar is left installed in situ, acquiring data periodically, e.g. every few minutes. Deformations are estimated by processing sets of GBSAR images acquired during several weeks or months, without moving the system. By contrast, in the D-GBSAR the radar is installed and dismounted at each measurement campaign, revisiting a given site periodically. This configuration is useful to monitor slow deformation phenomena. In this work, two alternative ways for exploiting the D-GBSAR technique will be presented: the DInSAR technique and the Amplitude based Technique. The former is based on the exploitation of the phase component of the acquired SAR images and it allows providing millimetric precision on the deformation estimates. However, this technique presents several limitations like the reduction of measurable points with an increase in the period of observation, the ambiguous nature of the phase measurements, and the influence of the atmospheric phase component that can make it non applicable in some cases, specially when working in natural environments. The second approach, that is based on the use of the amplitude component of GB-SAR images combined with a image matching technique, will allow the estimation of the displacements over specific targets avoiding two of the limitations commented above: the phase unwrapping and atmosphere contribution but reducing the deformation measurement precision. Two successful examples of D

A comparative study on methods of improving SCR for ship detection in SAR image

Science.gov (United States)

Lang, Haitao; Shi, Hongji; Tao, Yunhong; Ma, Li

2017-10-01

Knowledge about ship positions plays a critical role in a wide range of maritime applications. To improve the performance of ship detector in SAR image, an effective strategy is improving the signal-to-clutter ratio (SCR) before conducting detection. In this paper, we present a comparative study on methods of improving SCR, including power-law scaling (PLS), max-mean and max-median filter (MMF1 and MMF2), method of wavelet transform (TWT), traditional SPAN detector, reflection symmetric metric (RSM), scattering mechanism metric (SMM). The ability of SCR improvement to SAR image and ship detection performance associated with cell- averaging CFAR (CA-CFAR) of different methods are evaluated on two real SAR data.

Change detection in polarimetric SAR images using complex Wishart distributed matrices

DEFF Research Database (Denmark)

Conradsen, Knut; Nielsen, Allan Aasbjerg; Skriver, Henning

In surveillance it is important to be able to detect natural or man-made changes e.g. based on sequences of satellite or air borne images of the same area taken at different times. The mapping capability of synthetic aperture radar (SAR) is independent of e.g. cloud cover, and thus this technology...... scattering matrix, and after suitable preprocessing the outcome at each picture element (pixel) may be represented as a 3 by 3 Hermitian matrix following a complex Wishart distribution. One approach to solving the change detection problem based on SAR images is therefore to apply suitable statistical tests...... in the complex Wishart distribution. We propose a set-up for a systematic solution to the (practical) problems using the likelihood ratio test statistics. We show some examples based on a time series of images with 1024 by 1024 pixels....

Regional SAR Image Segmentation Based on Fuzzy Clustering with Gamma Mixture Model

Science.gov (United States)

Li, X. L.; Zhao, Q. H.; Li, Y.

2017-09-01

Most of stochastic based fuzzy clustering algorithms are pixel-based, which can not effectively overcome the inherent speckle noise in SAR images. In order to deal with the problem, a regional SAR image segmentation algorithm based on fuzzy clustering with Gamma mixture model is proposed in this paper. First, initialize some generating points randomly on the image, the image domain is divided into many sub-regions using Voronoi tessellation technique. Each sub-region is regarded as a homogeneous area in which the pixels share the same cluster label. Then, assume the probability of the pixel to be a Gamma mixture model with the parameters respecting to the cluster which the pixel belongs to. The negative logarithm of the probability represents the dissimilarity measure between the pixel and the cluster. The regional dissimilarity measure of one sub-region is defined as the sum of the measures of pixels in the region. Furthermore, the Markov Random Field (MRF) model is extended from pixels level to Voronoi sub-regions, and then the regional objective function is established under the framework of fuzzy clustering. The optimal segmentation results can be obtained by the solution of model parameters and generating points. Finally, the effectiveness of the proposed algorithm can be proved by the qualitative and quantitative analysis from the segmentation results of the simulated and real SAR images.

Aircraft Segmentation in SAR Images Based on Improved Active Shape Model

Science.gov (United States)

Zhang, X.; Xiong, B.; Kuang, G.

2018-04-01

In SAR image interpretation, aircrafts are the important targets arousing much attention. However, it is far from easy to segment an aircraft from the background completely and precisely in SAR images. Because of the complex structure, different kinds of electromagnetic scattering take place on the aircraft surfaces. As a result, aircraft targets usually appear to be inhomogeneous and disconnected. It is a good idea to extract an aircraft target by the active shape model (ASM), since combination of the geometric information controls variations of the shape during the contour evolution. However, linear dimensionality reduction, used in classic ACM, makes the model rigid. It brings much trouble to segment different types of aircrafts. Aiming at this problem, an improved ACM based on ISOMAP is proposed in this paper. ISOMAP algorithm is used to extract the shape information of the training set and make the model flexible enough to deal with different aircrafts. The experiments based on real SAR data shows that the proposed method achieves obvious improvement in accuracy.

Probability Density Components Analysis: A New Approach to Treatment and Classification of SAR Images

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Osmar Abílio de Carvalho Júnior

2014-04-01

Full Text Available Speckle noise (salt and pepper is inherent to synthetic aperture radar (SAR, which causes a usual noise-like granular aspect and complicates the image classification. In SAR image analysis, the spatial information might be a particular benefit for denoising and mapping classes characterized by a statistical distribution of the pixel intensities from a complex and heterogeneous spectral response. This paper proposes the Probability Density Components Analysis (PDCA, a new alternative that combines filtering and frequency histogram to improve the classification procedure for the single-channel synthetic aperture radar (SAR images. This method was tested on L-band SAR data from the Advanced Land Observation System (ALOS Phased-Array Synthetic-Aperture Radar (PALSAR sensor. The study area is localized in the Brazilian Amazon rainforest, northern Rondônia State (municipality of Candeias do Jamari, containing forest and land use patterns. The proposed algorithm uses a moving window over the image, estimating the probability density curve in different image components. Therefore, a single input image generates an output with multi-components. Initially the multi-components should be treated by noise-reduction methods, such as maximum noise fraction (MNF or noise-adjusted principal components (NAPCs. Both methods enable reducing noise as well as the ordering of multi-component data in terms of the image quality. In this paper, the NAPC applied to multi-components provided large reductions in the noise levels, and the color composites considering the first NAPC enhance the classification of different surface features. In the spectral classification, the Spectral Correlation Mapper and Minimum Distance were used. The results obtained presented as similar to the visual interpretation of optical images from TM-Landsat and Google Maps.

Discrimination of land cover from a multiparameter SAR data set

International Nuclear Information System (INIS)

Pierdicca, N.; Castracane, P.; Basili, P.; Ciotti, P.; Marzano, F.S.

2001-01-01

The identification of the most valuable radar observation parameters (e.g., frequency, polarisation, incidence angle) is important both for designing non-redundant high-performance sensors (i.e. selection of frequency bands and polarizations) and for specifying mission operation requirements (i.e. temporal sampling, incidence angle). Moreover, the task of classifying multiparameter SAR images may require to adopt a strategy that implies the selection of a number of features among those available from this kind of sensors. In this paper it has performed this kind of analysis in a specific area of interest to account for the particular conditions in which remotely sensed data are going to be used. The paper summarises the results of the analysis of the radar data acquired during the MAC Europe '91 and X-SAR/SIR-C campaigns over the Montespertoli test site in Italy. The analysis is based mainly on a statistical approach aiming at demonstrating what is the contribution of different measurements performed by the polarimetric SAR for discriminating the surface coverage. The work is intended to furnish a guideline to develop an optimal strategy for acquiring and processing polarimetric data to be used for land classification

Strategy and technology to prevent hospital-acquired infections: Lessons from SARS, Ebola, and MERS in Asia and West Africa

OpenAIRE

Rajakaruna, Sanjeewa Jayachandra; Liu, Wen-Bin; Ding, Yi-Bo; Cao, Guang-Wen

2017-01-01

Hospital-acquired infections (HAIs) are serious problems for healthcare systems, especially in developing countries where public health infrastructure and technology for infection preventions remain undeveloped. Here, we characterized how strategy and technology could be mobilized to improve the effectiveness of infection prevention and control in hospitals during the outbreaks of Ebola, Middle East respiratory syndrome (MERS), and severe acute respiratory syndrome (SARS) in Asia and West Afr...

Time domain SAR raw data simulation using CST and image focusing of 3D objects

Science.gov (United States)

Saeed, Adnan; Hellwich, Olaf

2017-10-01

This paper presents the use of a general purpose electromagnetic simulator, CST, to simulate realistic synthetic aperture radar (SAR) raw data of three-dimensional objects. Raw data is later focused in MATLAB using range-doppler algorithm. Within CST Microwave Studio a replica of TerraSAR-X chirp signal is incident upon a modeled Corner Reflector (CR) whose design and material properties are identical to that of the real one. Defining mesh and other appropriate settings reflected wave is measured at several distant points within a line parallel to the viewing direction. This is analogous to an array antenna and is synthesized to create a long aperture for SAR processing. The time domain solver in CST is based on the solution of differential form of Maxwells equations. Exported data from CST is arranged into a 2-d matrix of axis range and azimuth. Hilbert transform is applied to convert the real signal to complex data with phase information. Range compression, range cell migration correction (RCMC), and azimuth compression are applied in time domain to obtain the final SAR image. This simulation can provide valuable information to clarify which real world objects cause images suitable for high accuracy identification in the SAR images.

Removal of Optically Thick Clouds from Multi-Spectral Satellite Images Using Multi-Frequency SAR Data

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Robert Eckardt

2013-06-01

Full Text Available This study presents a method for the reconstruction of pixels contaminated by optical thick clouds in multi-spectral Landsat images using multi-frequency SAR data. A number of reconstruction techniques have already been proposed in the scientific literature. However, all of the existing techniques have certain limitations. In order to overcome these limitations, we expose the Closest Spectral Fit (CSF method proposed by Meng et al. to a new, synergistic approach using optical and SAR data. Therefore, the term Closest Feature Vector (CFV is introduced. The technique facilitates an elegant way to avoid radiometric distortions in the course of image reconstruction. Furthermore the cloud cover removal is independent from underlying land cover types and assumptions on seasonality, etc. The methodology is applied to mono-temporal, multi-frequency SAR data from TerraSAR-X (X-Band, ERS (C-Band and ALOS Palsar (L-Band. This represents a way of thinking about Radar data not as foreign, but as additional data source in multi-spectral remote sensing. For the assessment of the image restoration performance, an experimental framework is established and a statistical evaluation protocol is designed. The results show the potential of a synergistic usage of multi-spectral and SAR data to overcome the loss of data due to cloud cover.

Two-dimensional Fast ESPRIT Algorithm for Linear Array SAR Imaging

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Zhao Yi-chao

2015-10-01

Full Text Available The linear array Synthetic Aperture Radar (SAR system is a popular research tool, because it can realize three-dimensional imaging. However, owning to limitations of the aircraft platform and actual conditions, resolution improvement is difficult in cross-track and along-track directions. In this study, a twodimensional fast Estimation of Signal Parameters by Rotational Invariance Technique (ESPRIT algorithm for linear array SAR imaging is proposed to overcome these limitations. This approach combines the Gerschgorin disks method and the ESPRIT algorithm to estimate the positions of scatterers in cross and along-rack directions. Moreover, the reflectivity of scatterers is obtained by a modified pairing method based on “region growing”, replacing the least-squares method. The simulation results demonstrate the applicability of the algorithm with high resolution, quick calculation, and good real-time response.

A SAR IMAGE REGISTRATION METHOD BASED ON SIFT ALGORITHM

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W. Lu

2017-09-01

Full Text Available In order to improve the stability and rapidity of synthetic aperture radar (SAR images matching, an effective method was presented. Firstly, the adaptive smoothing filtering was employed for image denoising in image processing based on Wallis filtering to avoid the follow-up noise is amplified. Secondly, feature points were extracted by a simplified SIFT algorithm. Finally, the exact matching of the images was achieved with these points. Compared with the existing methods, it not only maintains the richness of features, but a-lso reduces the noise of the image. The simulation results show that the proposed algorithm can achieve better matching effect.

An Efficient SAR Image Segmentation Framework Using Transformed Nonlocal Mean and Multi-Objective Clustering in Kernel Space

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Dongdong Yang

2015-02-01

Full Text Available Synthetic aperture radar (SAR image segmentation usually involves two crucial issues: suitable speckle noise removing technique and effective image segmentation methodology. Here, an efficient SAR image segmentation method considering both of the two aspects is presented. As for the first issue, the famous nonlocal mean (NLM filter is introduced in this study to suppress the multiplicative speckle noise in SAR image. Furthermore, to achieve a higher denoising accuracy, the local neighboring pixels in the searching window are projected into a lower dimensional subspace by principal component analysis (PCA. Thus, the nonlocal mean filter is implemented in the subspace. Afterwards, a multi-objective clustering algorithm is proposed using the principals of artificial immune system (AIS and kernel-induced distance measures. The multi-objective clustering has been shown to discover the data distribution with different characteristics and the kernel methods can improve its robustness to noise and outliers. Experiments demonstrate that the proposed method is able to partition the SAR image robustly and accurately than the conventional approaches.

UAVSAR and TerraSAR-X Based InSAR Detection of Localized Subsidence in the New Orleans Area

Science.gov (United States)

Blom, R. G.; An, K.; Jones, C. E.; Latini, D.

2014-12-01

Vulnerability of the US Gulf coast to inundation has received increased attention since hurricanes Katrina and Rita. Compounding effects of sea level rise, wetland loss, and regional and local subsidence makes flood protection a difficult challenge, and particularly for the New Orleans area. Key to flood protection is precise knowledge of elevations and elevation changes. Analysis of historical and continuing geodetic measurements show surprising complexity, including locations subsiding more rapidly than considered during planning of hurricane protection and coastal restoration projects. Combining traditional, precise geodetic data with interferometric synthetic aperture radar (InSAR) observations can provide geographically dense constraints on surface deformation. The Gulf Coast environment is challenging for InSAR techniques, especially with systems not designed for interferometry. We use two InSAR capable systems, the L- band (24 cm wavelength) airborne JPL/NASA UAVSAR, and the DLR/EADS Astrium spaceborne TerraSAR X-band (3 cm wavelength), and compare results. First, we are applying pair-wise InSAR to the longer wavelength UAVSAR data to detect localized elevation changes potentially impacting flood protection infrastructure from 2009 - 2014. We focus on areas on and near flood protection infrastructure to identify changes indicative of subsidence, structural deformation, and/or seepage. The Spaceborne TerraSAR X-band SAR system has relatively frequent observations, and dense persistent scatterers in urban areas, enabling measurement of very small displacements. We compare L-band UAVSAR results with permanent scatterer (PS-InSAR) and Short Baseline Subsets (SBAS) interferometric analyses of a stack composed by 28 TerraSAR X-band images acquired over the same period. Thus we can evaluate results from the different radar frequencies and analyses techniques. Preliminary results indicate subsidence features potentially of a variety of causes, including ground water

Performance Analysis of Ship Wake Detection on Sentinel-1 SAR Images

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Maria Daniela Graziano

2017-10-01

Full Text Available A novel technique for ship wake detection has been recently proposed and applied on X-band Synthetic Aperture Radar images provided by COSMO/SkyMed and TerraSAR-X. The approach shows that the vast majority of wake features are correctly detected and validated in critical situations. In this paper, the algorithm was applied to 28 wakes imaged by Sentinel-1 mission with different polarizations and incidence angles with the aim of testing the method’s robustness with reference to radar frequency and resolution. The detection process is properly modified. The results show that the features were correctly classified in 78.5% of cases, whereas false confirmations occur mainly on Kelvin cusps. Finally, the results were compared with the algorithm performance on X-band images, showing that no significant difference arises. In fact, the total false confirmations rate was 15.8% on X-band images and 18.5% on C-band images. Moreover, since the main criticality concerns again the false confirmation of Kelvin cusps, the same empirical criterion suggested for the X-band SAR images yielded a negligible 1.5% of false detection rate.

Doppler Spectrum-Based NRCS Estimation Method for Low-Scattering Areas in Ocean SAR Images

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Hui Meng

2017-02-01

Full Text Available The image intensities of low-backscattering areas in synthetic aperture radar (SAR images are often seriously contaminated by the system noise floor and azimuthal ambiguity signal from adjacent high-backscattering areas. Hence, the image intensity of low-backscattering areas does not correctly reflect the backscattering intensity, which causes confusion in subsequent image processing or interpretation. In this paper, a method is proposed to estimate the normalized radar cross-section (NRCS of low-backscattering area by utilizing the differences between noise, azimuthal ambiguity, and signal in the Doppler frequency domain of single-look SAR images; the aim is to eliminate the effect of system noise and azimuthal ambiguity. Analysis shows that, for a spaceborne SAR with a noise equivalent sigma zero (NESZ of −25 dB and a single-look pixel of 8 m × 5 m, the NRCS-estimation precision of this method can reach −38 dB at a resolution of 96 m × 100 m. Three examples are given to validate the advantages of this method in estimating the low NRCS and the filtering of the azimuthal ambiguity.

UTILIZING SAR AND MULTISPECTRAL INTEGRATED DATA FOR EMERGENCY RESPONSE

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S. Havivi

2016-06-01

Full Text Available Satellite images are used widely in the risk cycle to understand the exposure, refine hazard maps and quickly provide an assessment after a natural or man-made disaster. Though there are different types of satellite images (e.g. optical, radar these have not been combined for risk assessments. The characteristics of different remote sensing data type may be extremely valuable for monitoring and evaluating the impacts of disaster events, to extract additional information thus making it available for emergency situations. To base this approach, two different change detection methods, for two different sensor's data were used: Coherence Change Detection (CCD for SAR data and Covariance Equalization (CE for multispectral imagery. The CCD provides an identification of the stability of an area, and shows where changes have occurred. CCD shows subtle changes with an accuracy of several millimetres to centimetres. The CE method overcomes the atmospheric effects differences between two multispectral images, taken at different times. Therefore, areas that had undergone a major change can be detected. To achieve our goals, we focused on the urban areas affected by the tsunami event in Sendai, Japan that occurred on March 11, 2011 which affected the surrounding area, coastline and inland. High resolution TerraSAR-X (TSX and Landsat 7 images, covering the research area, were acquired for the period before and after the event. All pre-processed and processed according to each sensor. Both results, of the optical and SAR algorithms, were combined by resampling the spatial resolution of the Multispectral data to the SAR resolution. This was applied by spatial linear interpolation. A score representing the damage level in both products was assigned. The results of both algorithms, high level of damage is shown in the areas closer to the sea and shoreline. Our approach, combining SAR and multispectral images, leads to more reliable information and provides a

Discernibility of Burial Mounds in High-Resolution X-Band SAR Images for Archaeological Prospections in the Altai Mountains

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Timo Balz

2016-09-01

Full Text Available The Altai Mountains are a heritage-rich archaeological landscape with monuments in almost every valley. Modern nation state borders dissect the region and limit archaeological landscape analysis to intra-national areas of interest. Remote sensing can help to overcome these limitations. Due to its high precision, Synthetic Aperture Radar (SAR data can be a very useful tool for supporting archaeological prospections, but compared to optical imagery, the detectability of sites of archaeological interest is limited. We analyzed the limitations of SAR using TerraSAR-X images in different modes. Based on ground truth, the discernibility of burial mounds was analyzed in different SAR acquisition modes. We show that very-high-resolution TerraSAR-X staring spotlight images are very well suited for the task, with >75% of the larger mounds being discernible, while in images with a lower spatial resolution only a few large sites can be detected, at rates below 50%.

A Range Ambiguity Suppression Processing Method for Spaceborne SAR with Up and Down Chirp Modulation.

Science.gov (United States)

Wen, Xuejiao; Qiu, Xiaolan; Han, Bing; Ding, Chibiao; Lei, Bin; Chen, Qi

2018-05-07

Range ambiguity is one of the factors which affect the SAR image quality. Alternately transmitting up and down chirp modulation pulses is one of the methods used to suppress the range ambiguity. However, the defocusing range ambiguous signal can still hold the stronger backscattering intensity than the mainlobe imaging area in some case, which has a severe impact on visual effects and subsequent applications. In this paper, a novel hybrid range ambiguity suppression method for up and down chirp modulation is proposed. The method can obtain the ambiguity area image and reduce the ambiguity signal power appropriately, by applying pulse compression using a contrary modulation rate and CFAR detecting method. The effectiveness and correctness of the approach is demonstrated by processing the archive images acquired by Chinese Gaofen-3 SAR sensor in full-polarization mode.

Polarimetric SAR Image Classification Using Multiple-feature Fusion and Ensemble Learning

Directory of Open Access Journals (Sweden)

Sun Xun

2016-12-01

Full Text Available In this paper, we propose a supervised classification algorithm for Polarimetric Synthetic Aperture Radar (PolSAR images using multiple-feature fusion and ensemble learning. First, we extract different polarimetric features, including extended polarimetric feature space, Hoekman, Huynen, H/alpha/A, and fourcomponent scattering features of PolSAR images. Next, we randomly select two types of features each time from all feature sets to guarantee the reliability and diversity of later ensembles and use a support vector machine as the basic classifier for predicting classification results. Finally, we concatenate all prediction probabilities of basic classifiers as the final feature representation and employ the random forest method to obtain final classification results. Experimental results at the pixel and region levels show the effectiveness of the proposed algorithm.

Assessment of radargrammetric DSMs from TerraSAR-X Stripmap images in a mountainous relief area of the Amazon region

Science.gov (United States)

de Oliveira, Cleber Gonzales; Paradella, Waldir Renato; da Silva, Arnaldo de Queiroz

The Brazilian Amazon is a vast territory with an enormous need for mapping and monitoring of renewable and non-renewable resources. Due to the adverse environmental condition (rain, cloud, dense vegetation) and difficult access, topographic information is still poor, and when available needs to be updated or re-mapped. In this paper, the feasibility of using Digital Surface Models (DSMs) extracted from TerraSAR-X Stripmap stereo-pair images for detailed topographic mapping was investigated for a mountainous area in the Carajás Mineral Province, located on the easternmost border of the Brazilian Amazon. The quality of the radargrammetric DSMs was evaluated regarding field altimetric measurements. Precise topographic field information acquired from a Global Positioning System (GPS) was used as Ground Control Points (GCPs) for the modeling of the stereoscopic DSMs and as Independent Check Points (ICPs) for the calculation of elevation accuracies. The analysis was performed following two ways: (1) the use of Root Mean Square Error (RMSE) and (2) calculations of systematic error (bias) and precision. The test for significant systematic error was based on the Student's-t distribution and the test of precision was based on the Chi-squared distribution. The investigation has shown that the accuracy of the TerraSAR-X Stripmap DSMs met the requirements for 1:50,000 map (Class A) as requested by the Brazilian Standard for Cartographic Accuracy. Thus, the use of TerraSAR-X Stripmap images can be considered a promising alternative for detailed topographic mapping in similar environments of the Amazon region, where available topographic information is rare or presents low quality.

Improved GO/PO method and its application to wideband SAR image of conducting objects over rough surface

Science.gov (United States)

Jiang, Wang-Qiang; Zhang, Min; Nie, Ding; Jiao, Yong-Chang

2018-04-01

To simulate the multiple scattering effect of target in synthetic aperture radar (SAR) image, the hybrid method GO/PO method, which combines the geometrical optics (GO) and physical optics (PO), is employed to simulate the scattering field of target. For ray tracing is time-consuming, the Open Graphics Library (OpenGL) is usually employed to accelerate the process of ray tracing. Furthermore, the GO/PO method is improved for the simulation in low pixel situation. For the improved GO/PO method, the pixels are arranged corresponding to the rectangular wave beams one by one, and the GO/PO result is the sum of the contribution values of all the rectangular wave beams. To get high-resolution SAR image, the wideband echo signal is simulated which includes information of many electromagnetic (EM) waves with different frequencies. Finally, the improved GO/PO method is used to simulate the SAR image of targets above rough surface. And the effects of reflected rays and the size of pixel matrix on the SAR image are also discussed.

The Intercomparison of X-Band SAR Images from COSMO‑SkyMed and TerraSAR-X Satellites: Case Studies

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Simone Pettinato

2013-06-01

Full Text Available The analysis of experimental data collected by X-band SAR of COSMO-SkyMed (CSK® and TerraSAR-X (TSX images on the same surface types has shown significant differences in the signal level of the two sensors. In order to investigate the possibility of combining data from the two instruments, a study was carried out by comparing images collected with similar orbital and sensor parameters (e.g., incidence angle, polarization, look angle at approximately the same date on two Italian agricultural test sites. Several homogenous agricultural fields within the observed area common to the two sensors were selected. Some forest plots have also been considered and used as a reference target. Direct comparisons were then performed between CSK and TSX images in different acquisition modes. The analysis carried out on the agricultural fields showed that, in general, the backscattering coefficient is higher in TSX Stripmap images with respect to CSK-Himage (about 3 dB, while CSK-Ping Pong data showed values lower than TSX of about 4.8 dB. Finally, a difference in backscattering of about 2.5 dB was pointed out between CSK-Himage and Ping-Pong images on agricultural fields. These results, achieved on bare soils, have also been compared with simulations performed by using the Advanced Integral Equation Model (AIEM.

SAR and Infrared Image Fusion in Complex Contourlet Domain Based on Joint Sparse Representation

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Wu Yiquan

2017-08-01

Full Text Available To investigate the problems of the large grayscale difference between infrared and Synthetic Aperture Radar (SAR images and their fusion image not being fit for human visual perception, we propose a fusion method for SAR and infrared images in the complex contourlet domain based on joint sparse representation. First, we perform complex contourlet decomposition of the infrared and SAR images. Then, we employ the KSingular Value Decomposition (K-SVD method to obtain an over-complete dictionary of the low-frequency components of the two source images. Using a joint sparse representation model, we then generate a joint dictionary. We obtain the sparse representation coefficients of the low-frequency components of the source images in the joint dictionary by the Orthogonal Matching Pursuit (OMP method and select them using the selection maximization strategy. We then reconstruct these components to obtain the fused low-frequency components and fuse the high-frequency components using two criteria——the coefficient of visual sensitivity and the degree of energy matching. Finally, we obtain the fusion image by the inverse complex contourlet transform. Compared with the three classical fusion methods and recently presented fusion methods, e.g., that based on the Non-Subsampled Contourlet Transform (NSCT and another based on sparse representation, the method we propose in this paper can effectively highlight the salient features of the two source images and inherit their information to the greatest extent.

Change detection in multitemporal synthetic aperture radar images using dual-channel convolutional neural network

Science.gov (United States)

Liu, Tao; Li, Ying; Cao, Ying; Shen, Qiang

2017-10-01

This paper proposes a model of dual-channel convolutional neural network (CNN) that is designed for change detection in SAR images, in an effort to acquire higher detection accuracy and lower misclassification rate. This network model contains two parallel CNN channels, which can extract deep features from two multitemporal SAR images. For comparison and validation, the proposed method is tested along with other change detection algorithms on both simulated SAR images and real-world SAR images captured by different sensors. The experimental results demonstrate that the presented method outperforms the state-of-the-art techniques by a considerable margin.

Feature Matching for SAR and Optical Images Based on Gaussian-Gamma-shaped Edge Strength Map

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CHEN Min

2016-03-01

Full Text Available A matching method for SAR and optical images, robust to pixel noise and nonlinear grayscale differences, is presented. Firstly, a rough correction to eliminate rotation and scale change between images is performed. Secondly, features robust to speckle noise of SAR image are detected by improving the original phase congruency based method. Then, feature descriptors are constructed on the Gaussian-Gamma-shaped edge strength map according to the histogram of oriented gradient pattern. Finally, descriptor similarity and geometrical relationship are combined to constrain the matching processing.The experimental results demonstrate that the proposed method provides significant improvement in correct matches number and image registration accuracy compared with other traditional methods.

A Range Ambiguity Suppression Processing Method for Spaceborne SAR with Up and Down Chirp Modulation

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Xuejiao Wen

2018-05-01

Full Text Available Range ambiguity is one of the factors which affect the SAR image quality. Alternately transmitting up and down chirp modulation pulses is one of the methods used to suppress the range ambiguity. However, the defocusing range ambiguous signal can still hold the stronger backscattering intensity than the mainlobe imaging area in some case, which has a severe impact on visual effects and subsequent applications. In this paper, a novel hybrid range ambiguity suppression method for up and down chirp modulation is proposed. The method can obtain the ambiguity area image and reduce the ambiguity signal power appropriately, by applying pulse compression using a contrary modulation rate and CFAR detecting method. The effectiveness and correctness of the approach is demonstrated by processing the archive images acquired by Chinese Gaofen-3 SAR sensor in full-polarization mode.

Bone scintigraphy in post-SARS patients and compared with magnetic resonance imaging

International Nuclear Information System (INIS)

Wang Qian; Huang Lili; Qin Shuling

2004-01-01

Objective: To study the characteristics of bone scintigraphy in post-SARS patients and evaluate the usefulness of bone scintigraphy in the prediction of avascular osteonecrosis (AVN) comparing with the MR imaging.. Methods: Our study included 66 patients who were diagnosed as SARS based on the diagnostic criteria issued by the Ministry of Health of China (MHC), including 46 women and 20 men. Their ages ranged from 19 to 63 years (mean, 31.6±0.1 years). All of the patients were treated with methyprednisonlone, rabavirin, broad spectrum antimicrobials and supportive therapy. Dosage of methyprednisonlone was 80∼800 mg/d for 4-72 days. Of them, varied seat of joint pain occurred in 47 patients 3 to 18 weeks after the onset of SARS. Since multiple joints were involved in many patients, bone scintigraphy was performed for screening AVN. The other 19 patients without of evident joint pain were also examined as their demand. Informed consents were obtained in all of the examined patients. No previously joint pain or trauma history was found in this group of patients. Of the 66 patients, planer X-ray was performed in 34 of the symptomatic patients previous to the scintigraphy, but it was negative in all. MR examination was performed in 54 patients before or after the scintigraphy, and the interval between two the tests was average of 8 days (range, 0 to 30 days). In addition, 27 consecutive cases aged lower than 45 years (mean, 40.4±0.8 years) with breast cancer who underwent bone scintigraphy for screening metastastic disease and had negative results were also involved as a control group. Whole body skeletal scintigraphy was performed 3 hours after intravenous administration of technetium-99m methylene-diphosphonate 740 MBq. Increased uptake lesion seen in the limb joints was defined as positive, but 'hot patella' sign was considered to be non diagnostic value. When a lesion was found in the whole body imaging, corresponding regional image was further taken. Two

InSAR Deformation Time Series Processed On-Demand in the Cloud

Science.gov (United States)

Horn, W. B.; Weeden, R.; Dimarchi, H.; Arko, S. A.; Hogenson, K.

2017-12-01

During this past year, ASF has developed a cloud-based on-demand processing system known as HyP3 (http://hyp3.asf.alaska.edu/), the Hybrid Pluggable Processing Pipeline, for Synthetic Aperture Radar (SAR) data. The system makes it easy for a user who doesn't have the time or inclination to install and use complex SAR processing software to leverage SAR data in their research or operations. One such processing algorithm is generation of a deformation time series product, which is a series of images representing ground displacements over time, which can be computed using a time series of interferometric SAR (InSAR) products. The set of software tools necessary to generate this useful product are difficult to install, configure, and use. Moreover, for a long time series with many images, the processing of just the interferograms can take days. Principally built by three undergraduate students at the ASF DAAC, the deformation time series processing relies the new Amazon Batch service, which enables processing of jobs with complex interconnected dependencies in a straightforward and efficient manner. In the case of generating a deformation time series product from a stack of single-look complex SAR images, the system uses Batch to serialize the up-front processing, interferogram generation, optional tropospheric correction, and deformation time series generation. The most time consuming portion is the interferogram generation, because even for a fairly small stack of images many interferograms need to be processed. By using AWS Batch, the interferograms are all generated in parallel; the entire process completes in hours rather than days. Additionally, the individual interferograms are saved in Amazon's cloud storage, so that when new data is acquired in the stack, an updated time series product can be generated with minimal addiitonal processing. This presentation will focus on the development techniques and enabling technologies that were used in developing the time

A New Method Based on Two-Stage Detection Mechanism for Detecting Ships in High-Resolution SAR Images

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Xu Yongli

2017-01-01

Full Text Available Ship detection in synthetic aperture radar (SAR remote sensing images, being a fundamental but challenging problem in the field of satellite image analysis, plays an important role for a wide range of applications and is receiving significant attention in recent years. Aiming at the requirements of ship detection in high-resolution SAR images, the accuracy, the intelligent level, a better real-time operation and processing efficiency, The characteristics of ocean background and ship target in high-resolution SAR images were analyzed, we put forward a ship detection algorithm in high-resolution SAR images. The algorithm consists of two detection stages: The first step designs a pre-training classifier based on improved spectral residual visual model to obtain the visual salient regions containing ship targets quickly, then achieve the purpose of probably detection of ships. In the second stage, considering the Bayesian theory of binary hypothesis detection, a local maximum posterior probability (MAP classifier is designed for the classification of pixels. After the parameter estimation and judgment criterion, the classification of pixels are carried out in the target areas to achieve the classification of two types of pixels in the salient regions. In the paper, several types of satellite image data, such as TerraSAR-X (TS-X, Radarsat-2, are used to evaluate the performance of detection methods. Comparing with classical CFAR detection algorithms, experimental results show that the algorithm can achieve a better effect of suppressing false alarms, which caused by the speckle noise and ocean clutter background inhomogeneity. At the same time, the detection speed is increased by 25% to 45%.

An Adaptive Ship Detection Algorithm for Hrws SAR Images Under Complex Background: Application to SENTINEL1A Data

Science.gov (United States)

He, G.; Xia, Z.; Chen, H.; Li, K.; Zhao, Z.; Guo, Y.; Feng, P.

2018-04-01

Real-time ship detection using synthetic aperture radar (SAR) plays a vital role in disaster emergency and marine security. Especially the high resolution and wide swath (HRWS) SAR images, provides the advantages of high resolution and wide swath synchronously, significantly promotes the wide area ocean surveillance performance. In this study, a novel method is developed for ship target detection by using the HRWS SAR images. Firstly, an adaptive sliding window is developed to propose the suspected ship target areas, based upon the analysis of SAR backscattering intensity images. Then, backscattering intensity and texture features extracted from the training samples of manually selected ship and non-ship slice images, are used to train a support vector machine (SVM) to classify the proposed ship slice images. The approach is verified by using the Sentinl1A data working in interferometric wide swath mode. The results demonstrate the improvement performance of the proposed method over the constant false alarm rate (CFAR) method, where the classification accuracy improved from 88.5 % to 96.4 % and the false alarm rate mitigated from 11.5 % to 3.6 % compared with CFAR respectively.

Potential inundated coastal area estimation in Shanghai with multi-platform SAR and altimetry data

Science.gov (United States)

Ma, Guanyu; Yang, Tianliang; Zhao, Qing; Kubanek, Julia; Pepe, Antonio; Dong, Hongbin; Sun, Zhibin

2017-09-01

As global warming problem is becoming serious in recent decades, the global sea level is continuously rising. This will cause damages to the coastal deltas with the characteristics of low-lying land, dense population, and developed economy. Continuously reclamation costal intertidal and wetland areas are making Shanghai, the mega city of Yangtze River Delta, more vulnerable to sea level rise. In this paper, we investigate the land subsidence temporal evolution of patterns and processes on a stretch of muddy coast located between the Yangtze River Estuary and Hangzou Bay with differential synthetic aperture radar interferometry (DInSAR) analyses. By exploiting a set of 31 SAR images acquired by the ENVISAT/ASAR from February 2007 to May 2010 and a set of 48 SAR images acquired by the COSMO-SkyMed (CSK) sensors from December 2013 to March 2016, coherent point targets as long as land subsidence velocity maps and time series are identified by using the Small Baseline Subset (SBAS) algorithm. With the DInSAR constrained land subsidence model, we predict the land subsidence trend and the expected cumulative subsidence in 2020, 2025 and 2030. Meanwhile, we used altimetrydata and densely distributed in the coastal region are identified (EEMD) algorithm to obtain the average sea level rise rate in the East China Sea. With the land subsidence predictions, sea level rise predictions, and high-precision digital elevation model (DEM), we analyze the combined risk of land subsidence and sea level rise on the coastal areas of Shanghai. The potential inundated areas are mapped under different scenarios.

The retrieval of two-dimensional distribution of the earth's surface aerodynamic roughness using SAR image and TM thermal infrared image

Institute of Scientific and Technical Information of China (English)

ZHANG; Renhua; WANG; Jinfeng; ZHU; Caiying; SUN; Xiaomin

2004-01-01

After having analyzed the requirement on the aerodynamic earth's surface roughness in two-dimensional distribution in the research field of interaction between land surface and atmosphere, this paper presents a new way to calculate the aerodynamic roughness using the earth's surface geometric roughness retrieved from SAR (Synthetic Aperture Radar) and TM thermal infrared image data. On the one hand, the SPM (Small Perturbation Model) was used as a theoretical SAR backscattering model to describe the relationship between the SAR backscattering coefficient and the earth's surface geometric roughness and its dielectric constant retrieved from the physical model between the soil thermal inertia and the soil surface moisture with the simultaneous TM thermal infrared image data and the ground microclimate data. On the basis of the SAR image matching with the TM image, the non-volume scattering surface geometric information was obtained from the SPM model at the TM image pixel scale, and the ground pixel surface's equivalent geometric roughness-height standard RMS (Root Mean Square) was achieved from the geometric information by the transformation of the typical topographic factors. The vegetation (wheat, tree) height retrieved from spectrum model was also transferred into its equivalent geometric roughness. A completely two-dimensional distribution map of the equivalent geometric roughness over the experimental area was produced by the data mosaic technique. On the other hand, according to the atmospheric eddy currents theory, the aerodynamic surface roughness was iterated out with the atmosphere stability correction method using the wind and the temperature profiles data measured at several typical fields such as bare soil field and vegetation field. After having analyzed the effect of surface equivalent geometric roughness together with dynamic and thermodynamic factors on the aerodynamic surface roughness within the working area, this paper first establishes a scale

Large Oil Spill Classification Using SAR Images Based on Spatial Histogram

Science.gov (United States)

Schvartzman, I.; Havivi, S.; Maman, S.; Rotman, S. R.; Blumberg, D. G.

2016-06-01

Among the different types of marine pollution, oil spill is a major threat to the sea ecosystems. Remote sensing is used in oil spill response. Synthetic Aperture Radar (SAR) is an active microwave sensor that operates under all weather conditions and provides information about the surface roughness and covers large areas at a high spatial resolution. SAR is widely used to identify and track pollutants in the sea, which may be due to a secondary effect of a large natural disaster or by a man-made one . The detection of oil spill in SAR imagery relies on the decrease of the backscattering from the sea surface, due to the increased viscosity, resulting in a dark formation that contrasts with the brightness of the surrounding area. Most of the use of SAR images for oil spill detection is done by visual interpretation. Trained interpreters scan the image, and mark areas of low backscatter and where shape is a-symmetrical. It is very difficult to apply this method for a wide area. In contrast to visual interpretation, automatic detection algorithms were suggested and are mainly based on scanning dark formations, extracting features, and applying big data analysis. We propose a new algorithm that applies a nonlinear spatial filter that detects dark formations and is not susceptible to noises, such as internal or speckle. The advantages of this algorithm are both in run time and the results retrieved. The algorithm was tested in genesimulations as well as on COSMO-SkyMed images, detecting the Deep Horizon oil spill in the Gulf of Mexico (occurred on 20/4/2010). The simulation results show that even in a noisy environment, oil spill is detected. Applying the algorithm to the Deep Horizon oil spill, the algorithm classified the oil spill better than focusing on dark formation algorithm. Furthermore, the results were validated by the National Oceanic and Atmospheric Administration (NOAA) data.

LARGE OIL SPILL CLASSIFICATION USING SAR IMAGES BASED ON SPATIAL HISTOGRAM

Directory of Open Access Journals (Sweden)

I. Schvartzman

2016-06-01

Full Text Available Among the different types of marine pollution, oil spill is a major threat to the sea ecosystems. Remote sensing is used in oil spill response. Synthetic Aperture Radar (SAR is an active microwave sensor that operates under all weather conditions and provides information about the surface roughness and covers large areas at a high spatial resolution. SAR is widely used to identify and track pollutants in the sea, which may be due to a secondary effect of a large natural disaster or by a man-made one . The detection of oil spill in SAR imagery relies on the decrease of the backscattering from the sea surface, due to the increased viscosity, resulting in a dark formation that contrasts with the brightness of the surrounding area. Most of the use of SAR images for oil spill detection is done by visual interpretation. Trained interpreters scan the image, and mark areas of low backscatter and where shape is a-symmetrical. It is very difficult to apply this method for a wide area. In contrast to visual interpretation, automatic detection algorithms were suggested and are mainly based on scanning dark formations, extracting features, and applying big data analysis. We propose a new algorithm that applies a nonlinear spatial filter that detects dark formations and is not susceptible to noises, such as internal or speckle. The advantages of this algorithm are both in run time and the results retrieved. The algorithm was tested in genesimulations as well as on COSMO-SkyMed images, detecting the Deep Horizon oil spill in the Gulf of Mexico (occurred on 20/4/2010. The simulation results show that even in a noisy environment, oil spill is detected. Applying the algorithm to the Deep Horizon oil spill, the algorithm classified the oil spill better than focusing on dark formation algorithm. Furthermore, the results were validated by the National Oceanic and Atmospheric Administration (NOAA data.

Decomposition of Polarimetric SAR Images Based on Second- and Third-order Statics Analysis

Science.gov (United States)

Kojima, S.; Hensley, S.

2012-12-01

There are many papers concerning the research of the decomposition of polerimetric SAR imagery. Most of them are based on second-order statics analysis that Freeman and Durden [1] suggested for the reflection symmetry condition that implies that the co-polarization and cross-polarization correlations are close to zero. Since then a number of improvements and enhancements have been proposed to better understand the underlying backscattering mechanisms present in polarimetric SAR images. For example, Yamaguchi et al. [2] added the helix component into Freeman's model and developed a 4 component scattering model for the non-reflection symmetry condition. In addition, Arii et al. [3] developed an adaptive model-based decomposition method that could estimate both the mean orientation angle and a degree of randomness for the canopy scattering for each pixel in a SAR image without the reflection symmetry condition. This purpose of this research is to develop a new decomposition method based on second- and third-order statics analysis to estimate the surface, dihedral, volume and helix scattering components from polarimetric SAR images without the specific assumptions concerning the model for the volume scattering. In addition, we evaluate this method by using both simulation and real UAVSAR data and compare this method with other methods. We express the volume scattering component using the wire formula and formulate the relationship equation between backscattering echo and each component such as the surface, dihedral, volume and helix via linearization based on second- and third-order statics. In third-order statics, we calculate the correlation of the correlation coefficients for each polerimetric data and get one new relationship equation to estimate each polarization component such as HH, VV and VH for the volume. As a result, the equation for the helix component in this method is the same formula as one in Yamaguchi's method. However, the equation for the volume

Design and realization of an active SAR calibrator for TerraSAR-X

Science.gov (United States)

Dummer, Georg; Lenz, Rainer; Lutz, Benjamin; Kühl, Markus; Müller-Glaser, Klaus D.; Wiesbeck, Werner

2005-10-01

TerraSAR-X is a new earth observing satellite which will be launched in spring 2006. It carries a high resolution X-band SAR sensor. For high image data quality, accurate ground calibration targets are necessary. This paper describes a novel system concept for an active and highly integrated, digitally controlled SAR system calibrator. A total of 16 active transponder and receiver systems and 17 receiver only systems will be fabricated for a calibration campaign. The calibration units serve for absolute radiometric calibration of the SAR image data. Additionally, they are equipped with an extra receiver path for two dimensional satellite antenna pattern recognition. The calibrator is controlled by a dedicated digital Electronic Control Unit (ECU). The different voltages needed by the calibrator and the ECU are provided by the third main unit called Power Management Unit (PMU).

Bistatic sAR data processing algorithms

CERN Document Server

Qiu, Xiaolan; Hu, Donghui

2013-01-01

Synthetic Aperture Radar (SAR) is critical for remote sensing. It works day and night, in good weather or bad. Bistatic SAR is a new kind of SAR system, where the transmitter and receiver are placed on two separate platforms. Bistatic SAR is one of the most important trends in SAR development, as the technology renders SAR more flexible and safer when used in military environments. Imaging is one of the most difficult and important aspects of bistatic SAR data processing. Although traditional SAR signal processing is fully developed, bistatic SAR has a more complex system structure, so sign

Satellite on-board real-time SAR processor prototype

Science.gov (United States)

Bergeron, Alain; Doucet, Michel; Harnisch, Bernd; Suess, Martin; Marchese, Linda; Bourqui, Pascal; Desnoyers, Nicholas; Legros, Mathieu; Guillot, Ludovic; Mercier, Luc; Châteauneuf, François

2017-11-01

A Compact Real-Time Optronic SAR Processor has been successfully developed and tested up to a Technology Readiness Level of 4 (TRL4), the breadboard validation in a laboratory environment. SAR, or Synthetic Aperture Radar, is an active system allowing day and night imaging independent of the cloud coverage of the planet. The SAR raw data is a set of complex data for range and azimuth, which cannot be compressed. Specifically, for planetary missions and unmanned aerial vehicle (UAV) systems with limited communication data rates this is a clear disadvantage. SAR images are typically processed electronically applying dedicated Fourier transformations. This, however, can also be performed optically in real-time. Originally the first SAR images were optically processed. The optical Fourier processor architecture provides inherent parallel computing capabilities allowing real-time SAR data processing and thus the ability for compression and strongly reduced communication bandwidth requirements for the satellite. SAR signal return data are in general complex data. Both amplitude and phase must be combined optically in the SAR processor for each range and azimuth pixel. Amplitude and phase are generated by dedicated spatial light modulators and superimposed by an optical relay set-up. The spatial light modulators display the full complex raw data information over a two-dimensional format, one for the azimuth and one for the range. Since the entire signal history is displayed at once, the processor operates in parallel yielding real-time performances, i.e. without resulting bottleneck. Processing of both azimuth and range information is performed in a single pass. This paper focuses on the onboard capabilities of the compact optical SAR processor prototype that allows in-orbit processing of SAR images. Examples of processed ENVISAT ASAR images are presented. Various SAR processor parameters such as processing capabilities, image quality (point target analysis), weight and

MREG V1.1 : a multi-scale image registration algorithm for SAR applications.

Energy Technology Data Exchange (ETDEWEB)

Eichel, Paul H.

2013-08-01

MREG V1.1 is the sixth generation SAR image registration algorithm developed by the Signal Processing&Technology Department for Synthetic Aperture Radar applications. Like its predecessor algorithm REGI, it employs a powerful iterative multi-scale paradigm to achieve the competing goals of sub-pixel registration accuracy and the ability to handle large initial offsets. Since it is not model based, it allows for high fidelity tracking of spatially varying terrain-induced misregistration. Since it does not rely on image domain phase, it is equally adept at coherent and noncoherent image registration. This document provides a brief history of the registration processors developed by Dept. 5962 leading up to MREG V1.1, a full description of the signal processing steps involved in the algorithm, and a user's manual with application specific recommendations for CCD, TwoColor MultiView, and SAR stereoscopy.

Permanent scatterer InSAR processing: Forsmark

International Nuclear Information System (INIS)

Dehls, John F.

2006-04-01

It has been speculated that slow, aseismic movement may be occurring along some of the fracture zones crosscutting the Forsmark area. The purpose of this study is to determine if it is possible to measure such movement using dInSAR. Differential SAR Interferometry (DInSAR) is a technique that compares the phases of multiple radar images of an area to measure surface change. The method has the potential to detect millimetric surface deformation along the sensor - target line-of-sight. Differences in phase between two images are easily viewed by combining, or interfering, the two phase-images. In the resulting image, the waves will either reinforce or cancel one another, depending upon the relative phases. The resulting image is called an interferogram and contains concentric bands of colour, or fringes, that are related to topography and/or surface deformation. New algorithms use many images acquired over a long time period to determine the movement history of individual objects, referred to as permanent scatterers. In the current project, standard PSInSAR processing was performed on 40 ERS-1 and ERS-2 scenes. The total area processed is approximately 1,500 km 2 . Slightly less than 20,000 permanent scatterers were identified.The highest densities were obtained along the coast and on the islands, where natural outcrops are more abundant. Two main classes of objects act as permanent scatterers in this area. The first are natural reflectors, such as rocks. The second are man-made reflectors, such as parts of buildings. Numerous local movements were found in the study area, relating to building subsidence, or compaction of anthropogenic fill. The dataset was divided into three groups for analysis, based upon the location of regional lineaments provided by SKB. Both statistical and geostatistical techniques were used. The median velocity of the three blocks did not differ by more than 0.2 mm/yr. This is not considered significant, given the possible magnitude of errors

Permanent scatterer InSAR processing: Forsmark

Energy Technology Data Exchange (ETDEWEB)

Dehls, John F [Geological Survey of Norway, Trondheim (Norway)

2006-04-15

It has been speculated that slow, aseismic movement may be occurring along some of the fracture zones crosscutting the Forsmark area. The purpose of this study is to determine if it is possible to measure such movement using dInSAR. Differential SAR Interferometry (DInSAR) is a technique that compares the phases of multiple radar images of an area to measure surface change. The method has the potential to detect millimetric surface deformation along the sensor - target line-of-sight. Differences in phase between two images are easily viewed by combining, or interfering, the two phase-images. In the resulting image, the waves will either reinforce or cancel one another, depending upon the relative phases. The resulting image is called an interferogram and contains concentric bands of colour, or fringes, that are related to topography and/or surface deformation. New algorithms use many images acquired over a long time period to determine the movement history of individual objects, referred to as permanent scatterers. In the current project, standard PSInSAR processing was performed on 40 ERS-1 and ERS-2 scenes. The total area processed is approximately 1,500 km{sup 2}. Slightly less than 20,000 permanent scatterers were identified.The highest densities were obtained along the coast and on the islands, where natural outcrops are more abundant. Two main classes of objects act as permanent scatterers in this area. The first are natural reflectors, such as rocks. The second are man-made reflectors, such as parts of buildings. Numerous local movements were found in the study area, relating to building subsidence, or compaction of anthropogenic fill. The dataset was divided into three groups for analysis, based upon the location of regional lineaments provided by SKB. Both statistical and geostatistical techniques were used. The median velocity of the three blocks did not differ by more than 0.2 mm/yr. This is not considered significant, given the possible magnitude of

Flood Extent Mapping for Namibia Using Change Detection and Thresholding with SAR

Science.gov (United States)

Long, Stephanie; Fatoyinbo, Temilola E.; Policelli, Frederick

2014-01-01

A new method for flood detection change detection and thresholding (CDAT) was used with synthetic aperture radar (SAR) imagery to delineate the extent of flooding for the Chobe floodplain in the Caprivi region of Namibia. This region experiences annual seasonal flooding and has seen a recent renewal of severe flooding after a long dry period in the 1990s. Flooding in this area has caused loss of life and livelihoods for the surrounding communities and has caught the attention of disaster relief agencies. There is a need for flood extent mapping techniques that can be used to process images quickly, providing near real-time flooding information to relief agencies. ENVISAT/ASAR and Radarsat-2 images were acquired for several flooding seasons from February 2008 to March 2013. The CDAT method was used to determine flooding from these images and includes the use of image subtraction, decision based classification with threshold values, and segmentation of SAR images. The total extent of flooding determined for 2009, 2011 and 2012 was about 542 km2, 720 km2, and 673 km2 respectively. Pixels determined to be flooded in vegetation were typically flooding in vegetation was much greater (almost one third of the total flooded area). The time to maximum flooding for the 2013 flood season was determined to be about 27 days. Landsat water classification was used to compare the results from the new CDAT with SAR method; the results show good spatial agreement with Landsat scenes.

Basic to Advanced InSAR Processing: GMTSAR

Science.gov (United States)

Sandwell, D. T.; Xu, X.; Baker, S.; Hogrelius, A.; Mellors, R. J.; Tong, X.; Wei, M.; Wessel, P.

2017-12-01

Monitoring crustal deformation using InSAR is becoming a standard technique for the science and application communities. Optimal use of the new data streams from Sentinel-1 and NISAR will require open software tools as well as education on the strengths and limitations of the InSAR methods. Over the past decade we have developed freely available, open-source software for processing InSAR data. The software relies on the Generic Mapping Tools (GMT) for the back-end data analysis and display and is thus called GMTSAR. With startup funding from NSF, we accelerated the development of GMTSAR to include more satellite data sources and provide better integration and distribution with GMT. In addition, with support from UNAVCO we have offered 6 GMTSAR short courses to educate mostly novice InSAR users. Currently, the software is used by hundreds of scientists and engineers around the world to study deformation at more than 4300 different sites. The most challenging aspect of the recent software development was the transition from image alignment using the cross-correlation method to a completely new alignment algorithm that uses only the precise orbital information to geometrically align images to an accuracy of better than 7 cm. This development was needed to process a new data type that is being acquired by the Sentinel-1A/B satellites. This combination of software and open data is transforming radar interferometry from a research tool into a fully operational time series analysis tool. Over the next 5 years we are planning to continue to broaden the user base through: improved software delivery methods; code hardening; better integration with data archives; support for high level products being developed for NISAR; and continued education and outreach.

A neural network detection model of spilled oil based on the texture analysis of SAR image

Science.gov (United States)

An, Jubai; Zhu, Lisong

2006-01-01

A Radial Basis Function Neural Network (RBFNN) Model is investigated for the detection of spilled oil based on the texture analysis of SAR imagery. In this paper, to take the advantage of the abundant texture information of SAR imagery, the texture features are extracted by both wavelet transform and the Gray Level Co-occurrence matrix. The RBFNN Model is fed with a vector of these texture features. The RBFNN Model is trained and tested by the sample data set of the feature vectors. Finally, a SAR image is classified by this model. The classification results of a spilled oil SAR image show that the classification accuracy for oil spill is 86.2 by the RBFNN Model using both wavelet texture and gray texture, while the classification accuracy for oil spill is 78.0 by same RBFNN Model using only wavelet texture as the input of this RBFNN model. The model using both wavelet transform and the Gray Level Co-occurrence matrix is more effective than that only using wavelet texture. Furthermore, it keeps the complicated proximity and has a good performance of classification.

SAR Imagery Simulation of Ship Based on Electromagnetic Calculations and Sea Clutter Modelling for Classification Applications

International Nuclear Information System (INIS)

Ji, K F; Zhao, Z; Xing, X W; Zou, H X; Zhou, S L

2014-01-01

Ship detection and classification with space-borne SAR has many potential applications within the maritime surveillance, fishery activity management, monitoring ship traffic, and military security. While ship detection techniques with SAR imagery are well established, ship classification is still an open issue. One of the main reasons may be ascribed to the difficulties on acquiring the required quantities of real data of vessels under different observation and environmental conditions with precise ground truth. Therefore, simulation of SAR images with high scenario flexibility and reasonable computation costs is compulsory for ship classification algorithms development. However, the simulation of SAR imagery of ship over sea surface is challenging. Though great efforts have been devoted to tackle this difficult problem, it is far from being conquered. This paper proposes a novel scheme for SAR imagery simulation of ship over sea surface. The simulation is implemented based on high frequency electromagnetic calculations methods of PO, MEC, PTD and GO. SAR imagery of sea clutter is modelled by the representative K-distribution clutter model. Then, the simulated SAR imagery of ship can be produced by inserting the simulated SAR imagery chips of ship into the SAR imagery of sea clutter. The proposed scheme has been validated with canonical and complex ship targets over a typical sea scene

Investigation on the separability of slums by multi-aspect TerraSAR-X dual-co-polarized high resolution spotlight images based on the multi-scale evaluation of local distributions

Science.gov (United States)

Schmitt, Andreas; Sieg, Tobias; Wurm, Michael; Taubenböck, Hannes

2018-02-01

Following recent advances in distinguishing settlements vs. non-settlement areas from latest SAR data, the question arises whether a further automatic intra-urban delineation and characterization of different structural types is possible. This paper studies the appearance of the structural type ;slums; in high resolution SAR images. Geocoded Kennaugh elements are used as backscatter information and Schmittlet indices as descriptor of local texture. Three cities with a significant share of slums (Cape Town, Manila, Mumbai) are chosen as test sites. These are imaged by TerraSAR-X in the dual-co-polarized high resolution spotlight mode in any available aspect angle. Representative distributions are estimated and fused by a robust approach. Our observations identify a high similarity of slums throughout all three test sites. The derived similarity maps are validated with reference data sets from visual interpretation and ground truth. The final validation strategy is based on completeness and correctness versus other classes in relation to the similarity. High accuracies (up to 87%) in identifying morphologic slums are reached for Cape Town. For Manila (up to 60%) and Mumbai (up to 54%), the distinction is more difficult due to their complex structural configuration. Concluding, high resolution SAR data can be suitable to automatically trace potential locations of slums. Polarimetric information and the incidence angle seem to have a negligible impact on the results whereas the intensity patterns and the passing direction of the satellite are playing a key role. Hence, the combination of intensity images (brightness) acquired from ascending and descending orbits together with Schmittlet indices (spatial pattern) promises best results. The transfer from the automatically recognized physical similarity to the semantic interpretation remains challenging.

SAR Target Recognition via Supervised Discriminative Dictionary Learning and Sparse Representation of the SAR-HOG Feature

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Shengli Song

2016-08-01

Full Text Available Automatic target recognition (ATR in synthetic aperture radar (SAR images plays an important role in both national defense and civil applications. Although many methods have been proposed, SAR ATR is still very challenging due to the complex application environment. Feature extraction and classification are key points in SAR ATR. In this paper, we first design a novel feature, which is a histogram of oriented gradients (HOG-like feature for SAR ATR (called SAR-HOG. Then, we propose a supervised discriminative dictionary learning (SDDL method to learn a discriminative dictionary for SAR ATR and propose a strategy to simplify the optimization problem. Finally, we propose a SAR ATR classifier based on SDDL and sparse representation (called SDDLSR, in which both the reconstruction error and the classification error are considered. Extensive experiments are performed on the MSTAR database under standard operating conditions and extended operating conditions. The experimental results show that SAR-HOG can reliably capture the structures of targets in SAR images, and SDDL can further capture subtle differences among the different classes. By virtue of the SAR-HOG feature and SDDLSR, the proposed method achieves the state-of-the-art performance on MSTAR database. Especially for the extended operating conditions (EOC scenario “Training 17 ∘ —Testing 45 ∘ ”, the proposed method improves remarkably with respect to the previous works.

Observations and Mitigation of RFI in ALOS PALSAR SAR Data; Implications for the Desdyni Mission

Science.gov (United States)

Rosen, Paul A.; Hensley, Scott; Le, Charles

2008-01-01

Initial examination of ALOS PALSAR synthetic aperture radar (SAR) data has indicated significant radio frequency interference (RFI) in several geographic locations around the world. RFI causes significant reduction in image contrast, introduces periodic and quasi-periodic image artifacts, and introduces significant phase noise in repeat pass interferometric data reduction. The US National Research Council Decadal Survey of Earth Science has recommended DESDynI, a Deformation, Ecosystems, and Dynamics of Ice satellite mission comprising an L-band polarimetric radar configured for repeat pass interferometry. There is considerable interest internationally in other future L-band and lower frequency systems as well. Therefore the issues of prevalence and possibilities of mitigation of RFI in these crowded frequency bands is of considerable interest. RFI is observed in ALOS PALSAR in California, USA, and in southern Egypt in data examined to date. Application of several techniques for removing it from the data prior to SAR image formation, ranging from straightforward spectral normalization to time-domain, multi-phase filtering techniques are considered. Considerable experience has been gained from the removal of RFI from P-band acquired by the GeoSAR system. These techniques applied to the PALSAR data are most successful when the bandwidth of any particular spectral component of the RFI is narrow. Performance impacts for SAR imagery and interferograms are considered in the context of DESDynI measurement requirements.

Multi-Frequency Polarimetric SAR Classification Based on Riemannian Manifold and Simultaneous Sparse Representation

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Fan Yang

2015-07-01

Full Text Available Normally, polarimetric SAR classification is a high-dimensional nonlinear mapping problem. In the realm of pattern recognition, sparse representation is a very efficacious and powerful approach. As classical descriptors of polarimetric SAR, covariance and coherency matrices are Hermitian semidefinite and form a Riemannian manifold. Conventional Euclidean metrics are not suitable for a Riemannian manifold, and hence, normal sparse representation classification cannot be applied to polarimetric SAR directly. This paper proposes a new land cover classification approach for polarimetric SAR. There are two principal novelties in this paper. First, a Stein kernel on a Riemannian manifold instead of Euclidean metrics, combined with sparse representation, is employed for polarimetric SAR land cover classification. This approach is named Stein-sparse representation-based classification (SRC. Second, using simultaneous sparse representation and reasonable assumptions of the correlation of representation among different frequency bands, Stein-SRC is generalized to simultaneous Stein-SRC for multi-frequency polarimetric SAR classification. These classifiers are assessed using polarimetric SAR images from the Airborne Synthetic Aperture Radar (AIRSAR sensor of the Jet Propulsion Laboratory (JPL and the Electromagnetics Institute Synthetic Aperture Radar (EMISAR sensor of the Technical University of Denmark (DTU. Experiments on single-band and multi-band data both show that these approaches acquire more accurate classification results in comparison to many conventional and advanced classifiers.

A Novel 3D Imaging Method for Airborne Downward-Looking Sparse Array SAR Based on Special Squint Model

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Xiaozhen Ren

2014-01-01

Full Text Available Three-dimensional (3D imaging technology based on antenna array is one of the most important 3D synthetic aperture radar (SAR high resolution imaging modes. In this paper, a novel 3D imaging method is proposed for airborne down-looking sparse array SAR based on the imaging geometry and the characteristic of echo signal. The key point of the proposed algorithm is the introduction of a special squint model in cross track processing to obtain accurate focusing. In this special squint model, point targets with different cross track positions have different squint angles at the same range resolution cell, which is different from the conventional squint SAR. However, after theory analysis and formulation deduction, the imaging procedure can be processed with the uniform reference function, and the phase compensation factors and algorithm realization procedure are demonstrated in detail. As the method requires only Fourier transform and multiplications and thus avoids interpolations, it is computationally efficient. Simulations with point scatterers are used to validate the method.

SAR Raw Data Generation for Complex Airport Scenes

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Jia Li

2014-10-01

Full Text Available The method of generating the SAR raw data of complex airport scenes is studied in this paper. A formulation of the SAR raw signal model of airport scenes is given. Via generating the echoes from the background, aircrafts and buildings, respectively, the SAR raw data of the unified SAR imaging geometry is obtained from their vector additions. The multipath scattering and the shadowing between the background and different ground covers of standing airplanes and buildings are analyzed. Based on the scattering characteristics, coupling scattering models and SAR raw data models of different targets are given, respectively. A procedure is given to generate the SAR raw data of airport scenes. The SAR images from the simulated raw data demonstrate the validity of the proposed method.

GOTCHA experience report: three-dimensional SAR imaging with complete circular apertures

Science.gov (United States)

Ertin, Emre; Austin, Christian D.; Sharma, Samir; Moses, Randolph L.; Potter, Lee C.

2007-04-01

We study circular synthetic aperture radar (CSAR) systems collecting radar backscatter measurements over a complete circular aperture of 360 degrees. This study is motivated by the GOTCHA CSAR data collection experiment conducted by the Air Force Research Laboratory (AFRL). Circular SAR provides wide-angle information about the anisotropic reflectivity of the scattering centers in the scene, and also provides three dimensional information about the location of the scattering centers due to a non planar collection geometry. Three dimensional imaging results with single pass circular SAR data reveals that the 3D resolution of the system is poor due to the limited persistence of the reflectors in the scene. We present results on polarimetric processing of CSAR data and illustrate reasoning of three dimensional shape from multi-view layover using prior information about target scattering mechanisms. Next, we discuss processing of multipass (CSAR) data and present volumetric imaging results with IFSAR and three dimensional backprojection techniques on the GOTCHA data set. We observe that the volumetric imaging with GOTCHA data is degraded by aliasing and high sidelobes due to nonlinear flightpaths and sparse and unequal sampling in elevation. We conclude with a model based technique that resolves target features and enhances the volumetric imagery by extrapolating the phase history data using the estimated model.

An Adaptive Ship Detection Scheme for Spaceborne SAR Imagery

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Xiangguang Leng

2016-08-01

Full Text Available With the rapid development of spaceborne synthetic aperture radar (SAR and the increasing need of ship detection, research on adaptive ship detection in spaceborne SAR imagery is of great importance. Focusing on practical problems of ship detection, this paper presents a highly adaptive ship detection scheme for spaceborne SAR imagery. It is able to process a wide range of sensors, imaging modes and resolutions. Two main stages are identified in this paper, namely: ship candidate detection and ship discrimination. Firstly, this paper proposes an adaptive land masking method using ship size and pixel size. Secondly, taking into account the imaging mode, incidence angle, and polarization channel of SAR imagery, it implements adaptive ship candidate detection in spaceborne SAR imagery by applying different strategies to different resolution SAR images. Finally, aiming at different types of typical false alarms, this paper proposes a comprehensive ship discrimination method in spaceborne SAR imagery based on confidence level and complexity analysis. Experimental results based on RADARSAT-1, RADARSAT-2, TerraSAR-X, RS-1, and RS-3 images demonstrate that the adaptive scheme proposed in this paper is able to detect ship targets in a fast, efficient and robust way.

Ship Detection in Gaofen-3 SAR Images Based on Sea Clutter Distribution Analysis and Deep Convolutional Neural Network.

Science.gov (United States)

An, Quanzhi; Pan, Zongxu; You, Hongjian

2018-01-24

Target detection is one of the important applications in the field of remote sensing. The Gaofen-3 (GF-3) Synthetic Aperture Radar (SAR) satellite launched by China is a powerful tool for maritime monitoring. This work aims at detecting ships in GF-3 SAR images using a new land masking strategy, the appropriate model for sea clutter and a neural network as the discrimination scheme. Firstly, the fully convolutional network (FCN) is applied to separate the sea from the land. Then, by analyzing the sea clutter distribution in GF-3 SAR images, we choose the probability distribution model of Constant False Alarm Rate (CFAR) detector from K-distribution, Gamma distribution and Rayleigh distribution based on a tradeoff between the sea clutter modeling accuracy and the computational complexity. Furthermore, in order to better implement CFAR detection, we also use truncated statistic (TS) as a preprocessing scheme and iterative censoring scheme (ICS) for boosting the performance of detector. Finally, we employ a neural network to re-examine the results as the discrimination stage. Experiment results on three GF-3 SAR images verify the effectiveness and efficiency of this approach.

Phi-s correlation and dynamic time warping - Two methods for tracking ice floes in SAR images

Science.gov (United States)

Mcconnell, Ross; Kober, Wolfgang; Kwok, Ronald; Curlander, John C.; Pang, Shirley S.

1991-01-01

The authors present two algorithms for performing shape matching on ice floe boundaries in SAR (synthetic aperture radar) images. These algorithms quickly produce a set of ice motion and rotation vectors that can be used to guide a pixel value correlator. The algorithms match a shape descriptor known as the Phi-s curve. The first algorithm uses normalized correlation to match the Phi-s curves, while the second uses dynamic programming to compute an elastic match that better accommodates ice floe deformation. Some empirical data on the performance of the algorithms on Seasat SAR images are presented.

Road detection in SAR images using a tensor voting algorithm

Science.gov (United States)

Shen, Dajiang; Hu, Chun; Yang, Bing; Tian, Jinwen; Liu, Jian

2007-11-01

In this paper, the problem of the detection of road networks in Synthetic Aperture Radar (SAR) images is addressed. Most of the previous methods extract the road by detecting lines and network reconstruction. Traditional algorithms such as MRFs, GA, Level Set, used in the progress of reconstruction are iterative. The tensor voting methodology we proposed is non-iterative, and non-sensitive to initialization. Furthermore, the only free parameter is the size of the neighborhood, related to the scale. The algorithm we present is verified to be effective when it's applied to the road extraction using the real Radarsat Image.

Covariance estimation for dInSAR surface deformation measurements in the presence of anisotropic atmospheric noise

KAUST Repository

Knospe, Steffen H G

2010-04-01

We study anisotropic spatial autocorrelation in differential synthetic aperture radar interferometric (dInSAR) measurements and its impact on geophysical parameter estimations. The dInSAR phase acquired by the satellite sensor is a superposition of different contributions, and when studying geophysical processes, we are usually only interested in the surface deformation part of the signal. Therefore, to obtain high-quality results, we would like to characterize and/or remove other phase components. A stochastic model has been found to be appropriate to describe atmospheric phase delay in dInSAR images. However, these phase delays are usually modeled as being isotropic, which is a simplification, because InSAR images often show directional atmospheric anomalies. Here, we analyze anisotropic structures and show validation results using both real and simulated data. We calculate experimental semivariograms of the dInSAR phase in several European Remote Sensing satellite-1/2 tandem interferograms. Based on the theory of random functions (RFs), we then fit anisotropic variogram models in the spatial domain, employing Matérn-and Bessel-family correlation functions in nested models to represent complex dInSAR covariance structures. The presented covariance function types, in the statistical framework of stationary RFs, are consistent with tropospheric delay models. We find that by using anisotropic data covariance information to weight dInSAR measurements, we can significantly improve both the precision and accuracy of geophysical parameter estimations. Furthermore, the improvement is dependent on how similar the deformation pattern is to the dominant structure of the anisotropic atmospheric signals. © 2009 IEEE.

Irrigated Grassland Monitoring Using a Time Series of TerraSAR-X and COSMO-SkyMed X-Band SAR Data

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Mohammad El Hajj

2014-10-01

Full Text Available The objective of this study was to analyze the sensitivity of radar signals in the X-band in irrigated grassland conditions. The backscattered radar signals were analyzed according to soil moisture and vegetation parameters using linear regression models. A time series of radar (TerraSAR-X and COSMO-SkyMed and optical (SPOT and LANDSAT images was acquired at a high temporal frequency in 2013 over a small agricultural region in southeastern France. Ground measurements were conducted simultaneously with the satellite data acquisitions during several grassland growing cycles to monitor the evolution of the soil and vegetation characteristics. The comparison between the Normalized Difference Vegetation Index (NDVI computed from optical images and the in situ Leaf Area Index (LAI showed a logarithmic relationship with a greater scattering for the dates corresponding to vegetation well developed before the harvest. The correlation between the NDVI and the vegetation parameters (LAI, vegetation height, biomass, and vegetation water content was high at the beginning of the growth cycle. This correlation became insensitive at a certain threshold corresponding to high vegetation (LAI ~2.5 m2/m2. Results showed that the radar signal depends on variations in soil moisture, with a higher sensitivity to soil moisture for biomass lower than 1 kg/m². HH and HV polarizations had approximately similar sensitivities to soil moisture. The penetration depth of the radar wave in the X-band was high, even for dense and high vegetation; flooded areas were visible in the images with higher detection potential in HH polarization than in HV polarization, even for vegetation heights reaching 1 m. Lower sensitivity was observed at the X-band between the radar signal and the vegetation parameters with very limited potential of the X-band to monitor grassland growth. These results showed that it is possible to track gravity irrigation and soil moisture variations from SAR

Power Transmission Tower Series Extraction in PolSAR Image Based on Time-Frequency Analysis and A-Contrario Theory

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Dongqing Peng

2016-11-01

Full Text Available Based on Time-Frequency (TF analysis and a-contrario theory, this paper presents a new approach for extraction of linear arranged power transmission tower series in Polarimetric Synthetic Aperture Radar (PolSAR images. Firstly, the PolSAR multidimensional information is analyzed using a linear TF decomposition approach. The stationarity of each pixel is assessed by testing the maximum likelihood ratio statistics of the coherency matrix. Then, based on the maximum likelihood log-ratio image, a Cell-Averaging Constant False Alarm Rate (CA-CFAR detector with Weibull clutter background and a post-processing operator is used to detect point-like targets in the image. Finally, a searching approach based on a-contrario theory is applied to extract the linear arranged targets from detected point-like targets. The experimental results on three sets of PolSAR data verify the effectiveness of this approach.

Automated Waterline Detection in the Wadden Sea Using High-Resolution TerraSAR-X Images

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Stefan Wiehle

2015-01-01

Full Text Available We present an algorithm for automatic detection of the land-water-line from TerraSAR-X images acquired over the Wadden Sea. In this coastal region of the southeastern North Sea, a strip of up to 20 km of seabed falls dry during low tide, revealing mudflats and tidal creeks. The tidal currents transport sediments and can change the coastal shape with erosion rates of several meters per month. This rate can be strongly increased by storm surges which also cause flooding of usually dry areas. Due to the high number of ships traveling through the Wadden Sea to the largest ports of Germany, frequent monitoring of the bathymetry is also an important task for maritime security. For such an extended area and the required short intervals of a few months, only remote sensing methods can perform this task efficiently. Automating the waterline detection in weather-independent radar images provides a fast and reliable way to spot changes in the coastal topography. The presented algorithm first performs smoothing, brightness thresholding, and edge detection. In the second step, edge drawing and flood filling are iteratively performed to determine optimal thresholds for the edge drawing. In the last step, small misdetections are removed.

Modulation of Tidal Channel Signatures on SAR Images Over Gyeonggi Bay in Relation to Environmental Factors

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Tae-Sung Kim

2018-04-01

Full Text Available In this study, variations of radar backscatter features of the tidal channel in Gyeonggi Bay in the Eastern Yellow Sea were investigated using spaceborne synthetic aperture radar (SAR images. Consistent quasi-linear bright features appeared on the SAR images. Examining the detailed local bathymetry chart, we found that the features were co-located with the major axis of the tidal channel in the region. It was also shown that modulation of the radar backscatter features changed according to the environmental conditions at the time of imaging. For the statistical analysis, the bathymetric features over the tidal channel were extracted by an objective method. In terms of shape, the extracted features had higher variability in width than in length. The analysis of the variation in intensity with the coinciding bathymetric distribution confirmed that the quasi-linear bright features on the SAR images are fundamentally imprinted due to the surface current convergence and divergence caused by the bathymetry-induced tidal current variation. Furthermore, the contribution of environmental factors to the intensity modulation was quantitatively analyzed. A comparison of the variation in normalized radar cross section (NRCS with tidal current showed a positive correlation only with the perpendicular component of tidal current (r= 0.47. This implies that the modulation in intensity of the tidal channel signatures is mainly affected by the interaction with cross-current flow. On the other hand, the modulation of the NRCS over the tidal channel tended to be degraded as wind speed increased (r= −0.65. Considering the environmental circumstances in the study area, it can be inferred that the imaging capability of SAR for the detection of tidal channel signatures mainly relies on wind speed.

Change Detection with Polarimetric SAR Imagery for Nuclear Verification

International Nuclear Information System (INIS)

Canty, M.

2015-01-01

This paper investigates the application of multivariate statistical change detection with high-resolution polarimetric SAR imagery acquired from commercial satellite platforms for observation and verification of nuclear activities. A prototype software tool comprising a processing chain starting from single look complex (SLC) multitemporal data through to change detection maps is presented. Multivariate change detection algorithms applied to polarimetric SAR data are not common. This is because, up until recently, not many researchers or practitioners have had access to polarimetric data. However with the advent of several spaceborne polarimetric SAR instruments such as the Japanese ALOS, the Canadian Radarsat-2, the German TerraSAR-X, the Italian COSMO-SkyMed missions and the European Sentinal SAR platform, the situation has greatly improved. There is now a rich source of weather-independent satellite radar data which can be exploited for Nuclear Safeguards purposes. The method will also work for univariate data, that is, it is also applicable to scalar or single polarimetric SAR data. The change detection procedure investigated here exploits the complex Wishart distribution of dual and quad polarimetric imagery in look-averaged covariance matrix format in order to define a per-pixel change/no-change hypothesis test. It includes approximations for the probability distribution of the test statistic, and so permits quantitative significance levels to be quoted for change pixels. The method has been demonstrated previously with polarimetric images from the airborne EMISAR sensor, but is applied here for the first time to satellite platforms. In addition, an improved multivariate method is used to estimate the so-called equivalent number of looks (ENL), which is a critical parameter of the hypothesis test. (author)

Program Merges SAR Data on Terrain and Vegetation Heights

Science.gov (United States)

Siqueira, Paul; Hensley, Scott; Rodriguez, Ernesto; Simard, Marc

2007-01-01

X/P Merge is a computer program that estimates ground-surface elevations and vegetation heights from multiple sets of data acquired by the GeoSAR instrument [a terrain-mapping synthetic-aperture radar (SAR) system that operates in the X and bands]. X/P Merge software combines data from X- and P-band digital elevation models, SAR backscatter magnitudes, and interferometric correlation magnitudes into a simplified set of output topographical maps of ground-surface elevation and tree height.

Infrastructure monitoring with spaceborne SAR sensors

CERN Document Server

ANGHEL, ANDREI; CACOVEANU, REMUS

2017-01-01

This book presents a novel non-intrusive infrastructure monitoring technique based on the detection and tracking of scattering centers in spaceborne SAR images. The methodology essentially consists of refocusing each available SAR image on an imposed 3D point cloud associated to the envisaged infrastructure element and identifying the reliable scatterers to be monitored by means of four dimensional (4D) tomography. The methodology described in this book provides a new perspective on infrastructure monitoring with spaceborne SAR images, is based on a standalone processing chain, and brings innovative technical aspects relative to conventional approaches. The book is intended primarily for professionals and researchers working in the area of critical infrastructure monitoring by radar remote sensing.

GROUND SUBSIDENCE ALONG SHANGHAI METRO LINE 6 BY PS-InSAR METHOD

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J. Wu

2018-04-01

Full Text Available With the rapid development of urban economy, convenient, safe, and efficient urban rail transit has become the preferred method for people to travel. In order to ensure the safety and sustainable development of urban rail transit, the PS-InSAR technology with millimeter deformation measurement accuracy has been widely applied to monitor the deformation of urban rail transit. In this paper, 32 scenes of COSMO-SkyMed descending images and 23 scenes of Envisat ASAR images covering the Shanghai Metro Line 6 acquired from 2008 to 2010 are used to estimate the average deformation rate along line-of-sight (LOS direction by PS-InSAR method. The experimental results show that there are two main subsidence areas along the Shanghai Metro Line 6, which are located between Wuzhou Avenue Station to Wulian Road Station and West Gaoke Road Station to Gaoqing Road Station. Between Wuzhou Avenue Station and Wulian Road Station, the maximum displacement rate in the vertical direction of COSMO-SkyMed images is −9.92 mm/year, and the maximum displacement rate in the vertical direction of Envisat ASAR images is −8.53 mm/year. From the West Gaoke Road Station to the Gaoqing Road Station, the maximum displacement rate in the vertical direction of COSMO-SkyMed images is −15.53 mm/year, and the maximum displacement rate in the vertical direction of Envisat ASAR images is −17.9 mm/year. The results show that the ground deformation rates obtained by two SAR platforms with different wavelengths, different sensors and different incident angles have good consistence with each other, and also that of spirit leveling.

Mapping Regional Inundation with Spaceborne L-Band SAR

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Bruce Chapman

2015-04-01

Full Text Available Shortly after the launch of ALOS PALSAR L-band SAR by the Japan Space Exploration Agency (JAXA, a program to develop an Earth Science Data Record (ESDR for inundated wetlands was funded by NASA. Using established methodologies, extensive multi-temporal L-band ALOS ScanSAR data acquired bi-monthly by the PALSAR instrument onboard ALOS were used to classify the inundation state for South America for delivery as a component of this Inundated Wetlands ESDR (IW-ESDR and in collaboration with JAXA’s ALOS Kyoto and Carbon Initiative science programme. We describe these methodologies and the final classification of the inundation state, then compared this with results derived from dual-season data acquired by the JERS-1 L-band SAR mission in 1995 and 1996, as well as with estimates of surface water extent measured globally every 10 days by coarser resolution sensors. Good correspondence was found when comparing open water extent classified from multi-temporal ALOS ScanSAR data with surface water fraction identified from coarse resolution sensors, except in those regions where there may be differences in sensitivity to widespread and shallow seasonal flooding event, or in areas that could be excluded through use of a continental-scale inundatable mask. It was found that the ALOS ScanSAR classification of inundated vegetation was relatively insensitive to inundated herbaceous vegetation. Inundation dynamics were examined using the multi-temporal ALOS ScanSAR acquisitions over the Pacaya-Samiria and surrounding areas in the Peruvian Amazon.

A first comparison of Cosmo-Skymed and TerraSAR-X data over Chamonix Mont-Blanc test-site

OpenAIRE

Nicolas , Jean-Marie; Trouvé , Emmanuel; Fallourd , Renaud; Vernier , Flavien; Tupin , Florence; Harant , Olivier; Gay , Michel; Moreau , Luc

2012-01-01

International audience; This paper presents the first results obtained with satellite im- age time series (SITS) acquired by Cosmo-SkyMed (CSK) over the Chamonix Mont-Blanc test-site. A CSK SITS made of 39 images is merged with a TerraSAR-X SITS made of 26 images by using the orbital information and co-registration tools developed in the EFIDIR project. The results are illus- trated by the computation of speckle-free images by temporal averaging, by the generation and comparison of topographi...

Speckle Filtering of GF-3 Polarimetric SAR Data with Joint Restriction Principle.

Science.gov (United States)

Xie, Jinwei; Li, Zhenfang; Zhou, Chaowei; Fang, Yuyuan; Zhang, Qingjun

2018-05-12

Polarimetric SAR (PolSAR) scattering characteristics of imagery are always obtained from the second order moments estimation of multi-polarization data, that is, the estimation of covariance or coherency matrices. Due to the extra-paths that signal reflected from separate scatterers within the resolution cell has to travel, speckle noise always exists in SAR images and has a severe impact on the scattering performance, especially on single look complex images. In order to achieve high accuracy in estimating covariance or coherency matrices, three aspects are taken into consideration: (1) the edges and texture of the scene are distinct after speckle filtering; (2) the statistical characteristic should be similar to the object pixel; and (3) the polarimetric scattering signature should be preserved, in addition to speckle reduction. In this paper, a joint restriction principle is proposed to meet the requirement. Three different restriction principles are introduced to the processing of speckle filtering. First, a new template, which is more suitable for the point or line targets, is designed to ensure the morphological consistency. Then, the extent sigma filter is used to restrict the pixels in the template aforementioned to have an identical statistic characteristic. At last, a polarimetric similarity factor is applied to the same pixels above, to guarantee the similar polarimetric features amongst the optional pixels. This processing procedure is named as speckle filtering with joint restriction principle and the approach is applied to GF-3 polarimetric SAR data acquired in San Francisco, CA, USA. Its effectiveness of keeping the image sharpness and preserving the scattering mechanism as well as speckle reduction is validated by the comparison with boxcar filters and refined Lee filter.

Assessing ScanSAR Interferometry for Deformation Studies

Science.gov (United States)

Buckley, S. M.; Gudipati, K.

2007-12-01

There is a trend in civil satellite SAR mission design to implement an imaging strategy that incorporates both stripmap mode and ScanSAR imaging. This represents a compromise between high resolution data collection and a desire for greater spatial coverage and more frequent revisit times. However, mixed mode imaging can greatly reduce the number of stripmap images available for measuring subtle ground deformation. Although ScanSAR-ScanSAR and ScanSAR-stripmap repeat-pass interferometry have been demonstrated, these approaches are infrequently used for single interferogram formation and nonexistent for InSAR time series analysis. For future mission design, e.g., a dedicated US InSAR mission, the effect of various ScanSAR system parameter choices on InSAR time series analysis also remains unexplored. Our objective is to determine the utility of ScanSAR differential interferometry. We will demonstrate the use of ScanSAR interferograms for several previous deformation studies: localized and broad-scale urban land subsidence, tunneling, volcanic surface movements and several examples associated with the seismic cycle. We also investigate the effect of various ScanSAR burst synchronization levels on our ability to detect and make quality measurements of deformation. To avoid the issues associated with Envisat ScanSAR burst alignment and to exploit a decade of InSAR measurements, we simulate ScanSAR data by bursting (throwing away range lines of) ERS-1/2 data. All the burst mode datasets are processed using a Modified SPECAN algorithm. To investigate the effects of burst misalignment, a number of cases with varying degrees of burst overlap are considered. In particular, we look at phase decorrelation as a function of percentage of burst overlap. Coherence clearly reduces as the percentage of overlap decreases and we find a useful threshold of 40-70% burst overlap depending on the study site. In order to get a more generalized understanding for different surface conditions

Imaging Formation Algorithm of the Ground and Space-Borne Hybrid BiSAR Based on Parameters Estimation from Direct Signal

Directory of Open Access Journals (Sweden)

Qingjun Zhang

2014-01-01

Full Text Available This paper proposes a novel image formation algorithm for the bistatic synthetic aperture radar (BiSAR with the configuration of a noncooperative transmitter and a stationary receiver in which the traditional imaging algorithm failed because the necessary imaging parameters cannot be estimated from the limited information from the noncooperative data provider. In the new algorithm, the essential parameters for imaging, such as squint angle, Doppler centroid, and Doppler chirp-rate, will be estimated by full exploration of the recorded direct signal (direct signal is the echo from satellite to stationary receiver directly from the transmitter. The Doppler chirp-rate is retrieved by modeling the peak phase of direct signal as a quadratic polynomial. The Doppler centroid frequency and the squint angle can be derived from the image contrast optimization. Then the range focusing, the range cell migration correction (RCMC, and the azimuth focusing are implemented by secondary range compression (SRC and the range cell migration, respectively. At last, the proposed algorithm is validated by imaging of the BiSAR experiment configured with china YAOGAN 10 SAR as the transmitter and the receiver platform located on a building at a height of 109 m in Jiangsu province. The experiment image with geometric correction shows good accordance with local Google images.

Coarse Resolution SAR Imagery to Support Flood Inundation Models in Near Real Time

Science.gov (United States)

Di Baldassarre, Giuliano; Schumann, Guy; Brandimarte, Luigia; Bates, Paul

2009-11-01

In recent years, the availability of new emerging data (e.g. remote sensing, intelligent wireless sensors, etc) has led to a sudden shift from a data-sparse to a data-rich environment for hydrological and hydraulic modelling. Furthermore, the increased socioeconomic relevance of river flood studies has motivated the development of complex methodologies for the simulation of the hydraulic behaviour of river systems. In this context, this study aims at assessing the capability of coarse resolution SAR (Synthetic Aperture Radar) imagery to support and quickly validate flood inundation models in near real time. A hydraulic model of a 98km reach of the River Po (Italy), previously calibrated on a high-magnitude flood event with extensive and high quality field data, is tested using a SAR flood image, acquired and processed in near real time, during the June 2008 low-magnitude event. Specifically, the image is an acquisition by the ENVISAT-ASAR sensor in wide swath mode and has been provided through ESA (European Space Agency) Fast Registration system at no cost 24 hours after the acquisition. The study shows that the SAR image enables validation and improvement of the model in a time shorter than the flood travel time. This increases the reliability of model predictions (e.g. water elevation and inundation width along the river reach) and, consequently, assists flood management authorities in undertaking the necessary prevention activities.

An Empirical Algorithm for Wave Retrieval from Co-Polarization X-Band SAR Imagery

Directory of Open Access Journals (Sweden)

Weizeng Shao

2017-07-01

Full Text Available In this study, we proposed an empirical algorithm for significant wave height (SWH retrieval from TerraSAR-X/TanDEM (TS-X/TD-X X-band synthetic aperture radar (SAR co-polarization (vertical-vertical (VV and horizontal-horizontal (HH images. As the existing empirical algorithm at X-band, i.e., XWAVE, is applied for wave retrieval from HH-polarization TS-X/TD-X image, polarization ratio (PR has to be used for inverting wind speed, which is treated as an input in XWAVE. Wind speed encounters saturation in tropical cyclone. In our work, wind speed is replaced by normalized radar cross section (NRCS to avoiding using SAR-derived wind speed, which does not work in high winds, and the empirical algorithm can be conveniently implemented without converting NRCS in HH-polarization to NRCS in VV-polarization by using X-band PR. A total of 120 TS-X/TD-X images, 60 in VV-polarization and 60 in HH-polarization, with homogenous wave patterns, and the coincide significant wave height data from European Centre for Medium-Range Weather Forecasts (ECMWF reanalysis field at a 0.125° grid were collected as a dataset for tuning the algorithm. The range of SWH is from 0 to 7 m. We then applied the algorithm to 24 VV and 21 HH additional SAR images to extract SWH at locations of 30 National Oceanic and Atmospheric Administration (NOAA National Data Buoy Center (NDBC buoys. It is found that the algorithm performs well with a SWH stander deviation (STD of about 0.5 m for both VV and HH polarization TS-X/TD-X images. For large wave validation (SWH 6–7 m, we applied the empirical algorithm to a tropical cyclone Sandy TD-X image acquired in 2012, and obtained good result with a SWH STD of 0.3 m. We concluded that the proposed empirical algorithm works for wave retrieval from TS-X/TD-X image in co-polarization without external sea surface wind information.

Neural network-based feature point descriptors for registration of optical and SAR images

Science.gov (United States)

Abulkhanov, Dmitry; Konovalenko, Ivan; Nikolaev, Dmitry; Savchik, Alexey; Shvets, Evgeny; Sidorchuk, Dmitry

2018-04-01

Registration of images of different nature is an important technique used in image fusion, change detection, efficient information representation and other problems of computer vision. Solving this task using feature-based approaches is usually more complex than registration of several optical images because traditional feature descriptors (SIFT, SURF, etc.) perform poorly when images have different nature. In this paper we consider the problem of registration of SAR and optical images. We train neural network to build feature point descriptors and use RANSAC algorithm to align found matches. Experimental results are presented that confirm the method's effectiveness.

Expressway deformation mapping using high-resolution TerraSAR-X images

KAUST Repository

Shi, Xuguo

2014-01-27

Monitoring deformation of linear infrastructures such as expressway and railway caused by natural processes or anthropogenic activities is a vital task to ensure the safety of human lives and properties. Interferometric Synthetic Aperture Radar (InSAR) has been widely recognized as an effective technology to carry out large-area surface deformation mapping. However, its application in linear infrastructure deformation monitoring has not been intensively studied till now. In this article, a modified Small BAseline Subset (SBAS) method is proposed to retrieve the deformation patterns of the expressway. In our method, only the point-like targets identified on the expressway were kept in our analysis, and two complementary subsets of interferograms were formed to better separate the signals of height error and deformation from inteferometric phase observations. We successfully applied this method with multitemporal high-resolution TerraSAR-X images to retrieve the spatialoral pattern of surface deformation along the Beian-Heihe expressway that is located in island-permafrost areas and threatened by geohazards. © 2014 Taylor & Francis.

Expressway deformation mapping using high-resolution TerraSAR-X images

KAUST Repository

Shi, Xuguo; Liao, Mingsheng; Wang, Teng; Zhang, Lu; Shan, Wei; Wang, Chunjiao

2014-01-01

Monitoring deformation of linear infrastructures such as expressway and railway caused by natural processes or anthropogenic activities is a vital task to ensure the safety of human lives and properties. Interferometric Synthetic Aperture Radar (InSAR) has been widely recognized as an effective technology to carry out large-area surface deformation mapping. However, its application in linear infrastructure deformation monitoring has not been intensively studied till now. In this article, a modified Small BAseline Subset (SBAS) method is proposed to retrieve the deformation patterns of the expressway. In our method, only the point-like targets identified on the expressway were kept in our analysis, and two complementary subsets of interferograms were formed to better separate the signals of height error and deformation from inteferometric phase observations. We successfully applied this method with multitemporal high-resolution TerraSAR-X images to retrieve the spatialoral pattern of surface deformation along the Beian-Heihe expressway that is located in island-permafrost areas and threatened by geohazards. © 2014 Taylor & Francis.

Integrated Shoreline Extraction Approach with Use of Rasat MS and SENTINEL-1A SAR Images

Science.gov (United States)

Demir, N.; Oy, S.; Erdem, F.; Şeker, D. Z.; Bayram, B.

2017-09-01

Shorelines are complex ecosystems and highly important socio-economic environments. They may change rapidly due to both natural and human-induced effects. Determination of movements along the shoreline and monitoring of the changes are essential for coastline management, modeling of sediment transportation and decision support systems. Remote sensing provides an opportunity to obtain rapid, up-to-date and reliable information for monitoring of shoreline. In this study, approximately 120 km of Antalya-Kemer shoreline which is under the threat of erosion, deposition, increasing of inhabitants and urbanization and touristic hotels, has been selected as the study area. In the study, RASAT pansharpened and SENTINEL-1A SAR images have been used to implement proposed shoreline extraction methods. The main motivation of this study is to combine the land/water body segmentation results of both RASAT MS and SENTINEL-1A SAR images to improve the quality of the results. The initial land/water body segmentation has been obtained using RASAT image by means of Random Forest classification method. This result has been used as training data set to define fuzzy parameters for shoreline extraction from SENTINEL-1A SAR image. Obtained results have been compared with the manually digitized shoreline. The accuracy assessment has been performed by calculating perpendicular distances between reference data and extracted shoreline by proposed method. As a result, the mean difference has been calculated around 1 pixel.

Evaluation of the Wishart test statistics for polarimetric SAR data

DEFF Research Database (Denmark)

Skriver, Henning; Nielsen, Allan Aasbjerg; Conradsen, Knut

2003-01-01

A test statistic for equality of two covariance matrices following the complex Wishart distribution has previously been used in new algorithms for change detection, edge detection and segmentation in polarimetric SAR images. Previously, the results for change detection and edge detection have been...... quantitatively evaluated. This paper deals with the evaluation of segmentation. A segmentation performance measure originally developed for single-channel SAR images has been extended to polarimetric SAR images, and used to evaluate segmentation for a merge-using-moment algorithm for polarimetric SAR data....

InSAR observations of active volcanoes in Latin America

Science.gov (United States)

Morales Rivera, A. M.; Chaussard, E.; Amelung, F.

2012-12-01

Over the last decade satellite-based interferometric synthetic aperture radar (InSAR) has developed into a well-known technique to gauge the status of active volcanoes. The InSAR technique can detect the ascent of magma to shallow levels of the volcanic plumbing system because new arriving magma pressurizes the system. This is likely associated with the inflation of the volcanic edifice and the surroundings. Although the potential of InSAR to detect magma migration is well known, the principal limitation was that only for few volcanoes frequent observations were acquired. The ALOS-1 satellite of the Japanese Aerospace Exploration Agency (JAXA) acquired a global L-band data set of 15-20 acquisitions during 2006-2011. Here we use ALOS InSAR and Small Baseline (SB) time-series methods for a ground deformation survey of Latin America with emphasis on the northern Andes. We present time-dependent ground deformation data for the volcanoes in Colombia, Ecuador and Peru and interpret the observations in terms of the dynamics of the volcanic systems.

Investigation of Slow-Moving Landslides from ALOS/PALSAR Images with TCPInSAR: A Case Study of Oso, USA

Directory of Open Access Journals (Sweden)

Qian Sun

2014-12-01

Full Text Available Monitoring slope instability is of great significance for understanding landslide kinematics and, therefore, reducing the related geological hazards. In recent years, interferometric synthetic aperture radar (InSAR has been widely applied to this end, especially thanks to the prompt evolution of multi-temporal InSAR (MTInSAR algorithms. In this paper, temporarily-coherent point InSAR (TCPInSAR, a recently-developed MTInSAR technique, is employed to investigate the slow-moving landslides in Oso, U.S., with 13 ALOS/PALSAR images. Compared to other MTInSAR techniques, TCPInSAR can work well with a small amount of data and is immune to unwrapping errors. Furthermore, the severe orbital ramps emanated from the inaccurate determination of the ALOS satellite’s state vector can be jointly estimated by TCPInSAR, resulting in an exhaustive separation between the orbital errors and displacement signals. The TCPInSAR-derived deformation map indicates that the riverside slopes adjacent to the North Fork of the Stillaguamish River, where the 2014 mudslide occurred, were active during 2007 and 2011. Besides, Coal Mountain has been found to be experiencing slow-moving landslides with clear boundaries and considerable magnitudes. The Deer Creek River is also threatened by a potential landslide dam due to the creeps detected in a nearby slope. The slope instability information revealed in this study is helpful to deal with the landslide hazards in Oso.

SAR Imaging of Ground Moving Targets with Non-ideal Motion Error Compensation(in English

Directory of Open Access Journals (Sweden)

Zhou Hui

2015-06-01

Full Text Available Conventional ground moving target imaging algorithms mainly focus on the range cell migration correction and the motion parameter estimation of the moving target. However, in real Synthetic Aperture Radar (SAR data processing, non-ideal motion error compensation is also a critical process, which focuses and has serious impacts on the imaging quality of moving targets. Non-ideal motion error can not be compensated by either the stationary SAR motion error compensation algorithms or the autofocus techniques. In this paper, two sorts of non-ideal motion errors that affect the Doppler centroid of the moving target is analyzed, and a novel non-ideal motion error compensation algorithm is proposed based on the Inertial Navigation System (INS data and the range walk trajectory. Simulated and real data processing results are provided to demonstrate the effectiveness of the proposed algorithm.

Detection of sinkhole precursors through SAR interferometry: radar and geological considerations

CSIR Research Space (South Africa)

Theron, Andre

2017-06-01

Full Text Available TerraSAR-X were acquired over a full year. DInSAR results revealed the presence of three previously unknown deformation features, one of which could be confirmed by subsequent field investigations. Furthermore, a water supply pipeline ruptured six months...

InSAR deformation monitoring of high risk landslides

Science.gov (United States)

Singhroy, V.; Li, J.

2013-05-01

During the past year there were at least twenty five media reports of landslides and seismic activities some fatal, occurring in various areas in Canada. These high risk geohazards sites requires high resolution monitoring both spatially and temporally for mitigation purposes, since they are near populated areas and energy, transportation and communication corridors. High resolution air photos, lidar and satellite images are quite common in areas where the landslides can be fatal. Radar interferometry (InSAR) techniques using images from several radar satellites are increasingly being used in slope stability assessment. This presentation provides examples of using high-resolution (1-3m) frequent revisits InSAR techniques from RADARSAT 2 and TerraSAR X to monitor several types of high-risk landslides affecting transportation and energy corridors and populated areas. We have analyses over 200 high resolution InSAR images over a three year period on geologically different landslides. The high-resolution InSAR images are effective in characterizing differential motion within these low velocity landslides. The low velocity landslides become high risk during the active wet spring periods. The wet soils are poor coherent targets and corner reflectors provide an effective means of InSAR monitoring the slope activities.

Flood extent mapping for Namibia using change detection and thresholding with SAR

International Nuclear Information System (INIS)

Long, Stephanie; Fatoyinbo, Temilola E; Policelli, Frederick

2014-01-01

A new method for flood detection change detection and thresholding (CDAT) was used with synthetic aperture radar (SAR) imagery to delineate the extent of flooding for the Chobe floodplain in the Caprivi region of Namibia. This region experiences annual seasonal flooding and has seen a recent renewal of severe flooding after a long dry period in the 1990s. Flooding in this area has caused loss of life and livelihoods for the surrounding communities and has caught the attention of disaster relief agencies. There is a need for flood extent mapping techniques that can be used to process images quickly, providing near real-time flooding information to relief agencies. ENVISAT/ASAR and Radarsat-2 images were acquired for several flooding seasons from February 2008 to March 2013. The CDAT method was used to determine flooding from these images and includes the use of image subtraction, decision-based classification with threshold values, and segmentation of SAR images. The total extent of flooding determined for 2009, 2011 and 2012 was about 542 km 2 , 720 km 2 , and 673 km 2 respectively. Pixels determined to be flooded in vegetation were typically <0.5% of the entire scene, with the exception of 2009 where the detection of flooding in vegetation was much greater (almost one third of the total flooded area). The time to maximum flooding for the 2013 flood season was determined to be about 27 days. Landsat water classification was used to compare the results from the new CDAT with SAR method; the results show good spatial agreement with Landsat scenes. (paper)

Software for Acquiring Image Data for PIV

Science.gov (United States)

Wernet, Mark P.; Cheung, H. M.; Kressler, Brian

2003-01-01

PIV Acquisition (PIVACQ) is a computer program for acquisition of data for particle-image velocimetry (PIV). In the PIV system for which PIVACQ was developed, small particles entrained in a flow are illuminated with a sheet of light from a pulsed laser. The illuminated region is monitored by a charge-coupled-device camera that operates in conjunction with a data-acquisition system that includes a frame grabber and a counter-timer board, both installed in a single computer. The camera operates in "frame-straddle" mode where a pair of images can be obtained closely spaced in time (on the order of microseconds). The frame grabber acquires image data from the camera and stores the data in the computer memory. The counter/timer board triggers the camera and synchronizes the pulsing of the laser with acquisition of data from the camera. PIVPROC coordinates all of these functions and provides a graphical user interface, through which the user can control the PIV data-acquisition system. PIVACQ enables the user to acquire a sequence of single-exposure images, display the images, process the images, and then save the images to the computer hard drive. PIVACQ works in conjunction with the PIVPROC program which processes the images of particles into the velocity field in the illuminated plane.

Space Radar Image of Manaus region of Brazil

Science.gov (United States)

1994-01-01

These L-band images of the Manaus region of Brazil were acquired by the Spaceborne Imaging Radar-C and X-band Synthetic Aperture Radar (SIR-C/X-SAR) aboard the space shuttle Endeavour. The left image was acquired on April 12, 1994, and the middle image was acquired on October 3, 1994. The area shown is approximately 8 kilometers by 40 kilometers (5 miles by 25 miles). The two large rivers in this image, the Rio Negro (top) and the Rio Solimoes (bottom), combine at Manaus (west of the image) to form the Amazon River. The image is centered at about 3 degrees south latitude and 61 degrees west longitude. North is toward the top left of the images. The differences in brightness between the images reflect changes in the scattering of the radar channel. In this case, the changes are indicative of flooding. A flooded forest has a higher backscatter at L-band (horizontally transmitted and received) than an unflooded river. The extent of the flooding is much greater in the April image than in the October image, and corresponds to the annual, 10-meter (33-foot) rise and fall of the Amazon River. A third image at right shows the change in the April and October images and was created by determining which areas had significant decreases in the intensity of radar returns. These areas, which appear blue on the third image at right, show the dramatic decrease in the extent of flooded forest, as the level of the Amazon River falls. The flooded forest is a vital habitat for fish and floating meadows are an important source of atmospheric methane. This demonstrates the capability of SIR-C/X-SAR to study important environmental changes that are impossible to see with optical sensors over regions such as the Amazon, where frequent cloud cover and dense forest canopies obscure monitoring of floods. Field studies by boat, on foot and in low-flying aircraft by the University of California at Santa Barbara, in collaboration with Brazil's Instituto Nacional de Pesguisas Estaciais, during

Science data collection with polarimetric SAR

DEFF Research Database (Denmark)

Dall, Jørgen; Woelders, Kim; Madsen, Søren Nørvang

1996-01-01

Discusses examples on the use of polarimetric SAR in a number of Earth science studies. The studies are presently being conducted by the Danish Center for Remote Sensing. A few studies of the European Space Agency's EMAC programme are also discussed. The Earth science objectives are presented......, and the potential of polarimetric SAR is discussed and illustrated with data collected by the Danish airborne EMISAR system during a number of experiments in 1994 and 1995. The presentation will include samples of data acquired for the different studies...

Urban-area extraction from polarimetric SAR image using combination of target decomposition and orientation angle

Science.gov (United States)

Zou, Bin; Lu, Da; Wu, Zhilu; Qiao, Zhijun G.

2016-05-01

The results of model-based target decomposition are the main features used to discriminate urban and non-urban area in polarimetric synthetic aperture radar (PolSAR) application. Traditional urban-area extraction methods based on modelbased target decomposition usually misclassified ground-trunk structure as urban-area or misclassified rotated urbanarea as forest. This paper introduces another feature named orientation angle to improve urban-area extraction scheme for the accurate mapping in urban by PolSAR image. The proposed method takes randomness of orientation angle into account for restriction of urban area first and, subsequently, implements rotation angle to improve results that oriented urban areas are recognized as double-bounce objects from volume scattering. ESAR L-band PolSAR data of the Oberpfaffenhofen Test Site Area was used to validate the proposed algorithm.

AN EVOLUTIONARY ALGORITHM FOR FAST INTENSITY BASED IMAGE MATCHING BETWEEN OPTICAL AND SAR SATELLITE IMAGERY

Directory of Open Access Journals (Sweden)

P. Fischer

2018-04-01

Full Text Available This paper presents a hybrid evolutionary algorithm for fast intensity based matching between satellite imagery from SAR and very high-resolution (VHR optical sensor systems. The precise and accurate co-registration of image time series and images of different sensors is a key task in multi-sensor image processing scenarios. The necessary preprocessing step of image matching and tie-point detection is divided into a search problem and a similarity measurement. Within this paper we evaluate the use of an evolutionary search strategy for establishing the spatial correspondence between satellite imagery of optical and radar sensors. The aim of the proposed algorithm is to decrease the computational costs during the search process by formulating the search as an optimization problem. Based upon the canonical evolutionary algorithm, the proposed algorithm is adapted for SAR/optical imagery intensity based matching. Extensions are drawn using techniques like hybridization (e.g. local search and others to lower the number of objective function calls and refine the result. The algorithm significantely decreases the computational costs whilst finding the optimal solution in a reliable way.

A combined use of multispectral and SAR images for ship detection and characterization through object based image analysis

Science.gov (United States)

Aiello, Martina; Gianinetto, Marco

2017-10-01

Marine routes represent a huge portion of commercial and human trades, therefore surveillance, security and environmental protection themes are gaining increasing importance. Being able to overcome the limits imposed by terrestrial means of monitoring, ship detection from satellite has recently prompted a renewed interest for a continuous monitoring of illegal activities. This paper describes an automatic Object Based Image Analysis (OBIA) approach to detect vessels made of different materials in various sea environments. The combined use of multispectral and SAR images allows for a regular observation unrestricted by lighting and atmospheric conditions and complementarity in terms of geographic coverage and geometric detail. The method developed adopts a region growing algorithm to segment the image in homogeneous objects, which are then classified through a decision tree algorithm based on spectral and geometrical properties. Then, a spatial analysis retrieves the vessels' position, length and heading parameters and a speed range is associated. Optimization of the image processing chain is performed by selecting image tiles through a statistical index. Vessel candidates are detected over amplitude SAR images using an adaptive threshold Constant False Alarm Rate (CFAR) algorithm prior the object based analysis. Validation is carried out by comparing the retrieved parameters with the information provided by the Automatic Identification System (AIS), when available, or with manual measurement when AIS data are not available. The estimation of length shows R2=0.85 and estimation of heading R2=0.92, computed as the average of R2 values obtained for both optical and radar images.

Coral reef detection using SAR/RADARSAT-1 images at Costa dos Corais, PE/AL, Brazil

Directory of Open Access Journals (Sweden)

Frederico de Moraes Rudorff

2008-06-01

Full Text Available The present work aimed to examine the potentials of SAR RADARSAT-1 images to detect emergent coral reefs at the Environmental Protection Area of "Costa dos Corais". Multi-view filters were applied and tested for speckle noise reduction. A digital unsupervised classification based on image segmentation was performed and the classification accuracy was evaluated by an error matrix built between the SAR image classification and a reference map obtained from a TM Landsat-5 classification. The adaptative filters showed the best results for speckle suppression and border preservation, especially the Kuan, Gamma MAP, Lee, Frost and Enhanced Frost filters. Small similarity and area thresholds (5 and 10, respectively were used for the image segmentation due to the reduced dimensions and the narrow and elongated forms of the reefs. The classification threshold of 99% had a better user's accuracy, but a lower producer's accuracy because it is a more restrictive threshold; therefore, it may be possible that it had a greater omission on reef classification. The results indicate that SAR images have a good potential for the detection of emergent coral reefs.O presente trabalho examinou o potencial de imagens SAR do RADARSAT-1 na detecção de recifes de coral expostos na Área de Proteção Ambiental das Costa dos Corais. Filtros de multi-visada foram aplicados e testados para redução do ruído speckle. Uma classificação não supervisionada baseada em uma imagem segmentada foi realizada e a acurácia da classificação foi avaliada através de uma matriz de erro construída entre a imagem classificada e o mapa de referência. Os filtros adaptativos apresentaram os melhores desempenhos para supressão de speckle e preservação de bordas, especialmente os filtros Kuan, Gamma MAP, Lee, Frost and Enhanced Frost. Os pequenos limiares de similaridade e de área (10 e 5, respectivamente foram melhores devido à forma fina e alongada dos recifes. O limiar de

Software for Generating Troposphere Corrections for InSAR Using GPS and Weather Model Data

Science.gov (United States)

Moore, Angelyn W.; Webb, Frank H.; Fishbein, Evan F.; Fielding, Eric J.; Owen, Susan E.; Granger, Stephanie L.; Bjoerndahl, Fredrik; Loefgren, Johan; Fang, Peng; Means, James D.;

Improved SAR Amplitude Image Offset Measurements for Deriving Three-Dimensional Coseismic Displacements

KAUST Repository

Wang, Teng; Jonsson, Sigurjon

2015-01-01

Offsets of synthetic aperture radar (SAR) images have played an important role in deriving complete three-dimensional (3-D) surface displacement fields in geoscientific applications. However, offset maps often suffer from multiple outliers and patch-like artifacts, because the standard offset-measurement method is a regular moving-window operation that does not consider the scattering characteristics of the ground. Here, we show that by focusing the offset measurements on predetected strong reflectors, the reliability and accuracy of SAR offsets can be significantly improved. Application to the 2011 Van (Turkey) earthquake reveals a clear deformation signal from an otherwise decorrelated interferogram, making derivation of the 3-D coseismic displacement field possible. Our proposed method can improve mapping of coseismic deformation and other ground displacements, such as glacier flow and landslide movement when strong reflectors exist.

Improved SAR Amplitude Image Offset Measurements for Deriving Three-Dimensional Coseismic Displacements

KAUST Repository

Wang, Teng

2015-02-03

Offsets of synthetic aperture radar (SAR) images have played an important role in deriving complete three-dimensional (3-D) surface displacement fields in geoscientific applications. However, offset maps often suffer from multiple outliers and patch-like artifacts, because the standard offset-measurement method is a regular moving-window operation that does not consider the scattering characteristics of the ground. Here, we show that by focusing the offset measurements on predetected strong reflectors, the reliability and accuracy of SAR offsets can be significantly improved. Application to the 2011 Van (Turkey) earthquake reveals a clear deformation signal from an otherwise decorrelated interferogram, making derivation of the 3-D coseismic displacement field possible. Our proposed method can improve mapping of coseismic deformation and other ground displacements, such as glacier flow and landslide movement when strong reflectors exist.

Monitoring of Building Construction by 4D Change Detection Using Multi-temporal SAR Images

Science.gov (United States)

Yang, C. H.; Pang, Y.; Soergel, U.

2017-05-01

Monitoring urban changes is important for city management, urban planning, updating of cadastral map, etc. In contrast to conventional field surveys, which are usually expensive and slow, remote sensing techniques are fast and cost-effective alternatives. Spaceborne synthetic aperture radar (SAR) sensors provide radar images captured rapidly over vast areas at fine spatiotemporal resolution. In addition, the active microwave sensors are capable of day-and-night vision and independent of weather conditions. These advantages make multi-temporal SAR images suitable for scene monitoring. Persistent scatterer interferometry (PSI) detects and analyses PS points, which are characterized by strong, stable, and coherent radar signals throughout a SAR image sequence and can be regarded as substructures of buildings in built-up cities. Attributes of PS points, for example, deformation velocities, are derived and used for further analysis. Based on PSI, a 4D change detection technique has been developed to detect disappearance and emergence of PS points (3D) at specific times (1D). In this paper, we apply this 4D technique to the centre of Berlin, Germany, to investigate its feasibility and application for construction monitoring. The aims of the three case studies are to monitor construction progress, business districts, and single buildings, respectively. The disappearing and emerging substructures of the buildings are successfully recognized along with their occurrence times. The changed substructures are then clustered into single construction segments based on DBSCAN clustering and α-shape outlining for object-based analysis. Compared with the ground truth, these spatiotemporal results have proven able to provide more detailed information for construction monitoring.

Flood extent and water level estimation from SAR using data-model integration

Science.gov (United States)

Ajadi, O. A.; Meyer, F. J.

2017-12-01

Synthetic Aperture Radar (SAR) images have long been recognized as a valuable data source for flood mapping. Compared to other sources, SAR's weather and illumination independence and large area coverage at high spatial resolution supports reliable, frequent, and detailed observations of developing flood events. Accordingly, SAR has the potential to greatly aid in the near real-time monitoring of natural hazards, such as flood detection, if combined with automated image processing. This research works towards increasing the reliability and temporal sampling of SAR-derived flood hazard information by integrating information from multiple SAR sensors and SAR modalities (images and Interferometric SAR (InSAR) coherence) and by combining SAR-derived change detection information with hydrologic and hydraulic flood forecast models. First, the combination of multi-temporal SAR intensity images and coherence information for generating flood extent maps is introduced. The application of least-squares estimation integrates flood information from multiple SAR sensors, thus increasing the temporal sampling. SAR-based flood extent information will be combined with a Digital Elevation Model (DEM) to reduce false alarms and to estimate water depth and flood volume. The SAR-based flood extent map is assimilated into the Hydrologic Engineering Center River Analysis System (Hec-RAS) model to aid in hydraulic model calibration. The developed technology is improving the accuracy of flood information by exploiting information from data and models. It also provides enhanced flood information to decision-makers supporting the response to flood extent and improving emergency relief efforts.

Method to acquire regions of fruit, branch and leaf from image of red apple in orchard

Science.gov (United States)

Lv, Jidong; Xu, Liming

2017-07-01

This work proposed a method to acquire regions of fruit, branch and leaf from red apple image in orchard. To acquire fruit image, R-G image was extracted from the RGB image for corrosive working, hole filling, subregion removal, expansive working and opening operation in order. Finally, fruit image was acquired by threshold segmentation. To acquire leaf image, fruit image was subtracted from RGB image before extracting 2G-R-B image. Then, leaf image was acquired by subregion removal and threshold segmentation. To acquire branch image, dynamic threshold segmentation was conducted in the R-G image. Then, the segmented image was added to fruit image to acquire adding fruit image which was subtracted from RGB image with leaf image. Finally, branch image was acquired by opening operation, subregion removal and threshold segmentation after extracting the R-G image from the subtracting image. Compared with previous methods, more complete image of fruit, leaf and branch can be acquired from red apple image with this method.

Novel Polarimetric SAR Interferometry Algorithms, Phase I

Data.gov (United States)

National Aeronautics and Space Administration — Polarimetric radar interferometry (PolInSAR) is a new SAR imaging mode that is rapidly becoming an important technique for bare earth topographic mapping, tree...

Visual analytics for semantic queries of TerraSAR-X image content

Science.gov (United States)

Espinoza-Molina, Daniela; Alonso, Kevin; Datcu, Mihai

2015-10-01

With the continuous image product acquisition of satellite missions, the size of the image archives is considerably increasing every day as well as the variety and complexity of their content, surpassing the end-user capacity to analyse and exploit them. Advances in the image retrieval field have contributed to the development of tools for interactive exploration and extraction of the images from huge archives using different parameters like metadata, key-words, and basic image descriptors. Even though we count on more powerful tools for automated image retrieval and data analysis, we still face the problem of understanding and analyzing the results. Thus, a systematic computational analysis of these results is required in order to provide to the end-user a summary of the archive content in comprehensible terms. In this context, visual analytics combines automated analysis with interactive visualizations analysis techniques for an effective understanding, reasoning and decision making on the basis of very large and complex datasets. Moreover, currently several researches are focused on associating the content of the images with semantic definitions for describing the data in a format to be easily understood by the end-user. In this paper, we present our approach for computing visual analytics and semantically querying the TerraSAR-X archive. Our approach is mainly composed of four steps: 1) the generation of a data model that explains the information contained in a TerraSAR-X product. The model is formed by primitive descriptors and metadata entries, 2) the storage of this model in a database system, 3) the semantic definition of the image content based on machine learning algorithms and relevance feedback, and 4) querying the image archive using semantic descriptors as query parameters and computing the statistical analysis of the query results. The experimental results shows that with the help of visual analytics and semantic definitions we are able to explain

Research on a dem Coregistration Method Based on the SAR Imaging Geometry

Science.gov (United States)

Niu, Y.; Zhao, C.; Zhang, J.; Wang, L.; Li, B.; Fan, L.

2018-04-01

Due to the systematic error, especially the horizontal deviation that exists in the multi-source, multi-temporal DEMs (Digital Elevation Models), a method for high precision coregistration is needed. This paper presents a new fast DEM coregistration method based on a given SAR (Synthetic Aperture Radar) imaging geometry to overcome the divergence and time-consuming problem of the conventional DEM coregistration method. First, intensity images are simulated for two DEMs under the given SAR imaging geometry. 2D (Two-dimensional) offsets are estimated in the frequency domain using the intensity cross-correlation operation in the FFT (Fast Fourier Transform) tool, which can greatly accelerate the calculation process. Next, the transformation function between two DEMs is achieved via the robust least-square fitting of 2D polynomial operation. Accordingly, two DEMs can be precisely coregistered. Last, two DEMs, i.e., one high-resolution LiDAR (Light Detection and Ranging) DEM and one low-resolution SRTM (Shutter Radar Topography Mission) DEM, covering the Yangjiao landslide region of Chongqing are taken as an example to test the new method. The results indicate that, in most cases, this new method can achieve not only a result as much as 80 times faster than the minimum elevation difference (Least Z-difference, LZD) DEM registration method, but also more accurate and more reliable results.

The 2006-2012 deformation at Sakurajima stratovolcano (Japan) detected via spaceborne multisensor SAR Interferometry

Science.gov (United States)

Pepe, Susi; Trippanera, Daniele; Casu, Francesco; Tizzani, Pietro; Nobile, Adriano; Aoki, Yosuke; Zoffoli, Simona; Acocella, Valerio; Sansosti, Eugenio

2013-04-01

We analyze the evolution of the ground deformation at Sakurajima active stratovolcano located in the Aira caldera (Kagoshima prefecture Japan). This caldera, extending over more than 20 km, has been formed as a consequence of a huge eruption, occurred 22,000 years ago, that caused a magma chamber collapse. The Sakurajima volcano is an andesitic cone formed by more recent activity within the caldera, beginning about 13,000 years ago. Its first historical recorded eruption occurred in 963 AD. Most eruptions are Strombolian and Vulcanian and affect only the summit area. The larger explosive (plinian) eruptions occurred in 1471-1476, 1779-1782 and 1914, each producing 1 - 2 km3 of lava and pyroclastic materials. Explosive eruptions of Vulcanian type, with ash emissions, have occurred intermittently from 1955 to 2002. From 2009 to December 2012, a strong and continuous period of volcanic activity has been recorded mainly at the Showa Crater producing plumes that reached altitudes of 1.8-3.5 km. In order to analyze the active deformation processes of the volcano complex and its surrounding areas, we performed SAR Interferometry (InSAR) techniques by using COSMOSkyMed (X-band) and ALOS (L-band) data. The joint data analysis allowed us to increase the spatial coverage of InSAR measurements., we processed 19 descending and 25 ascending orbit SAR images acquired by ALOS satellite from 2008 to 2011 and 2006 to 2011, respectively; we computed 57 descending and 71 ascending interferograms which were subsequently inverted via SBAS-InSAR algorithm to obtain mean velocity maps and deformation time series. The X-band dataset consists of 20 images acquired only on descending orbits between 2011 and 2012; from this dataset we computed 44 interferograms. The preliminary analysis of the mean deformation velocity reveals the presence of a consistent uplift signal in the North region of the Sakurajima Island that extends also to the North sector of Kagoshima bay. The corresponding

Accurate Analysis of Target Characteristic in Bistatic SAR Images: A Dihedral Corner Reflectors Case.

Science.gov (United States)

Ao, Dongyang; Li, Yuanhao; Hu, Cheng; Tian, Weiming

2017-12-22

The dihedral corner reflectors are the basic geometric structure of many targets and are the main contributions of radar cross section (RCS) in the synthetic aperture radar (SAR) images. In stealth technologies, the elaborate design of the dihedral corners with different opening angles is a useful approach to reduce the high RCS generated by multiple reflections. As bistatic synthetic aperture sensors have flexible geometric configurations and are sensitive to the dihedral corners with different opening angles, they specially fit for the stealth target detections. In this paper, the scattering characteristic of dihedral corner reflectors is accurately analyzed in bistatic synthetic aperture images. The variation of RCS with the changing opening angle is formulated and the method to design a proper bistatic radar for maximizing the detection capability is provided. Both the results of the theoretical analysis and the experiments show the bistatic SAR could detect the dihedral corners, under a certain bistatic angle which is related to the geometry of target structures.

Wake Component Detection in X-Band SAR Images for Ship Heading and Velocity Estimation

Directory of Open Access Journals (Sweden)

Maria Daniela Graziano

2016-06-01

Full Text Available A new algorithm for ship wake detection is developed with the aim of ship heading and velocity estimation. It exploits the Radon transform and utilizes merit indexes in the intensity domain to validate the detected linear features as real components of the ship wake. Finally, ship velocity is estimated by state-of-the-art techniques of azimuth shift and Kelvin arm wavelength. The algorithm is applied to 13 X-band SAR images from the TerraSAR-X and COSMO/SkyMed missions with different polarization and incidence angles. Results show that the vast majority of wake features are correctly detected and validated also in critical situations, i.e., when multiple wake appearances or dark areas not related to wake features are imaged. The ship route estimations are validated with truth-at-sea in seven cases. Finally, it is also verified that the algorithm does not detect wakes in the surroundings of 10 ships without wake appearances.

Accurate Analysis of Target Characteristic in Bistatic SAR Images: A Dihedral Corner Reflectors Case

Directory of Open Access Journals (Sweden)

Dongyang Ao

2017-12-01

Full Text Available The dihedral corner reflectors are the basic geometric structure of many targets and are the main contributions of radar cross section (RCS in the synthetic aperture radar (SAR images. In stealth technologies, the elaborate design of the dihedral corners with different opening angles is a useful approach to reduce the high RCS generated by multiple reflections. As bistatic synthetic aperture sensors have flexible geometric configurations and are sensitive to the dihedral corners with different opening angles, they specially fit for the stealth target detections. In this paper, the scattering characteristic of dihedral corner reflectors is accurately analyzed in bistatic synthetic aperture images. The variation of RCS with the changing opening angle is formulated and the method to design a proper bistatic radar for maximizing the detection capability is provided. Both the results of the theoretical analysis and the experiments show the bistatic SAR could detect the dihedral corners, under a certain bistatic angle which is related to the geometry of target structures.

Accurate Analysis of Target Characteristic in Bistatic SAR Images: A Dihedral Corner Reflectors Case

Science.gov (United States)

Ao, Dongyang; Hu, Cheng; Tian, Weiming

2017-01-01

The dihedral corner reflectors are the basic geometric structure of many targets and are the main contributions of radar cross section (RCS) in the synthetic aperture radar (SAR) images. In stealth technologies, the elaborate design of the dihedral corners with different opening angles is a useful approach to reduce the high RCS generated by multiple reflections. As bistatic synthetic aperture sensors have flexible geometric configurations and are sensitive to the dihedral corners with different opening angles, they specially fit for the stealth target detections. In this paper, the scattering characteristic of dihedral corner reflectors is accurately analyzed in bistatic synthetic aperture images. The variation of RCS with the changing opening angle is formulated and the method to design a proper bistatic radar for maximizing the detection capability is provided. Both the results of the theoretical analysis and the experiments show the bistatic SAR could detect the dihedral corners, under a certain bistatic angle which is related to the geometry of target structures. PMID:29271917

Mapping ground surface deformation using temporarily coherent point SAR interferometry: Application to Los Angeles Basin

Science.gov (United States)

Zhang, L.; Lu, Zhong; Ding, X.; Jung, H.-S.; Feng, G.; Lee, C.-W.

2012-01-01

Multi-temporal interferometric synthetic aperture radar (InSAR) is an effective tool to detect long-term seismotectonic motions by reducing the atmospheric artifacts, thereby providing more precise deformation signal. The commonly used approaches such as persistent scatterer InSAR (PSInSAR) and small baseline subset (SBAS) algorithms need to resolve the phase ambiguities in interferogram stacks either by searching a predefined solution space or by sparse phase unwrapping methods; however the efficiency and the success of phase unwrapping cannot be guaranteed. We present here an alternative approach – temporarily coherent point (TCP) InSAR (TCPInSAR) – to estimate the long term deformation rate without the need of phase unwrapping. The proposed approach has a series of innovations including TCP identification, TCP network and TCP least squares estimator. We apply the proposed method to the Los Angeles Basin in southern California where structurally active faults are believed capable of generating damaging earthquakes. The analysis is based on 55 interferograms from 32 ERS-1/2 images acquired during Oct. 1995 and Dec. 2000. To evaluate the performance of TCPInSAR on a small set of observations, a test with half of interferometric pairs is also performed. The retrieved TCPInSAR measurements have been validated by a comparison with GPS observations from Southern California Integrated GPS Network. Our result presents a similar deformation pattern as shown in past InSAR studies but with a smaller average standard deviation (4.6 mm) compared with GPS observations, indicating that TCPInSAR is a promising alternative for efficiently mapping ground deformation even from a relatively smaller set of interferograms.

Block adjustment of airborne InSAR based on interferogram phase and POS data

Science.gov (United States)

Yue, Xijuan; Zhao, Yinghui; Han, Chunming; Dou, Changyong

2015-12-01

High-precision surface elevation information in large scale can be obtained efficiently by airborne Interferomatric Synthetic Aperture Radar (InSAR) system, which is recently becoming an important tool to acquire remote sensing data and perform mapping applications in the area where surveying and mapping is difficult to be accomplished by spaceborne satellite or field working. . Based on the study of the three-dimensional (3D) positioning model using interferogram phase and Position and Orientation System (POS) data and block adjustment error model, a block adjustment method to produce seamless wide-area mosaic product generated from airborne InSAR data is proposed in this paper. The effect of 6 parameters, including trajectory and attitude of the aircraft, baseline length and incline angle, slant range, and interferometric phase, on the 3D positioning accuracy is quantitatively analyzed. Using the data acquired in the field campaign conducted in Mianyang county Sichuan province, China in June 2011, a mosaic seamless Digital Elevation Model (DEM) product was generated from 76 images in 4 flight strips by the proposed block adjustment model. The residuals of ground control points (GCPs), the absolute positioning accuracy of check points (CPs) and the relative positioning accuracy of tie points (TPs) both in same and adjacent strips were assessed. The experimental results suggest that the DEM and Digital Orthophoto Map (DOM) product generated by the airborne InSAR data with sparse GCPs can meet mapping accuracy requirement at scale of 1:10 000.

Acquired portosystemic collaterals: anatomy and imaging

Energy Technology Data Exchange (ETDEWEB)

Leite, Andrea Farias de Melo; Mota Junior, Americo, E-mail: andreafariasm@gmail.com [Instituto de Medicina Integral Professor Fernando Figueira de Pernambuco (IMIP), Recife, PE (Brazil); Chagas-Neto, Francisco Abaete [Universidade de Fortaleza (UNIFOR), Fortaleza, CE (Brazil); Teixeira, Sara Reis; Elias Junior, Jorge; Muglia, Valdair Francisco [Universidade de Sao Paulo (FMRP/USP), Ribeirao Preto, SP (Brazil). Faculdade de Medicina

2016-07-15

Portosystemic shunts are enlarged vessels that form collateral pathological pathways between the splanchnic circulation and the systemic circulation. Although their causes are multifactorial, portosystemic shunts all have one mechanism in common - increased portal venous pressure, which diverts the blood flow from the gastrointestinal tract to the systemic circulation. Congenital and acquired collateral pathways have both been described in the literature. The aim of this pictorial essay was to discuss the distinct anatomic and imaging features of portosystemic shunts, as well as to provide a robust method of differentiating between acquired portosystemic shunts and similar pathologies, through the use of illustrations and schematic drawings. Imaging of portosystemic shunts provides subclinical markers of increased portal venous pressure. Therefore, radiologists play a crucial role in the identification of portosystemic shunts. Early detection of portosystemic shunts can allow ample time to perform endovascular shunt operations, which can relieve portal hypertension and prevent acute or chronic complications in at-risk patient populations. (author)

Acquired portosystemic collaterals: anatomy and imaging

International Nuclear Information System (INIS)

Leite, Andrea Farias de Melo; Mota Junior, Americo; Chagas-Neto, Francisco Abaete; Teixeira, Sara Reis; Elias Junior, Jorge; Muglia, Valdair Francisco

2016-01-01

Portosystemic shunts are enlarged vessels that form collateral pathological pathways between the splanchnic circulation and the systemic circulation. Although their causes are multifactorial, portosystemic shunts all have one mechanism in common - increased portal venous pressure, which diverts the blood flow from the gastrointestinal tract to the systemic circulation. Congenital and acquired collateral pathways have both been described in the literature. The aim of this pictorial essay was to discuss the distinct anatomic and imaging features of portosystemic shunts, as well as to provide a robust method of differentiating between acquired portosystemic shunts and similar pathologies, through the use of illustrations and schematic drawings. Imaging of portosystemic shunts provides subclinical markers of increased portal venous pressure. Therefore, radiologists play a crucial role in the identification of portosystemic shunts. Early detection of portosystemic shunts can allow ample time to perform endovascular shunt operations, which can relieve portal hypertension and prevent acute or chronic complications in at-risk patient populations. (author)

STUDY ON LANDSLIDE DISASTER EXTRACTION METHOD BASED ON SPACEBORNE SAR REMOTE SENSING IMAGES – TAKE ALOS PALSAR FOR AN EXAMPLE

Directory of Open Access Journals (Sweden)

D. Xue

2018-04-01

Full Text Available In this paper, sequence ALOS PALSAR data and airborne SAR data of L-band from June 5, 2008 to September 8, 2015 are used. Based on the research of SAR data preprocessing and core algorithms, such as geocode, registration, filtering, unwrapping and baseline estimation, the improved Goldstein filtering algorithm and the branch-cut path tracking algorithm are used to unwrap the phase. The DEM and surface deformation information of the experimental area were extracted. Combining SAR-specific geometry and differential interferometry, on the basis of composite analysis of multi-source images, a method of detecting landslide disaster combining coherence of SAR image is developed, which makes up for the deficiency of single SAR and optical remote sensing acquisition ability. Especially in bad weather and abnormal climate areas, the speed of disaster emergency and the accuracy of extraction are improved. It is found that the deformation in this area is greatly affected by faults, and there is a tendency of uplift in the southeast plain and western mountainous area, while in the southwest part of the mountain area there is a tendency to sink. This research result provides a basis for decision-making for local disaster prevention and control.

Utilization of a genetic algorithm for the automatic detection of oil spill from RADARSAT-2 SAR satellite data

International Nuclear Information System (INIS)

Marghany, Maged

2014-01-01

Highlights: • An oil platform located 70 km from the coast of Louisiana sank on Thursday. • Oil spill has backscatter values of −25 dB in RADARSAT-2 SAR. • Oil spill is portrayed in SCNB mode by shallower incidence angle. • Ideal detection of oil spills in SAR images requires moderate wind speeds. • Genetic algorithm is excellent tool for automatic detection of oil spill in RADARSAT-2 SAR data. - Abstract: In this work, a genetic algorithm is applied for the automatic detection of oil spills. The procedure is implemented using sequences from RADARSAT-2 SAR ScanSAR Narrow single-beam data acquired in the Gulf of Mexico. The study demonstrates that the implementation of crossover allows for the generation of an accurate oil spill pattern. This conclusion is confirmed by the receiver-operating characteristic (ROC) curve. The ROC curve indicates that the existence of oil slick footprints can be identified using the area between the ROC curve and the no-discrimination line of 90%, which is greater than that of other surrounding environmental features. In conclusion, the genetic algorithm can be used as a tool for the automatic detection of oil spills, and the ScanSAR Narrow single-beam mode serves as an excellent sensor for oil spill detection and survey

Atmospheric Phase Delay in Sentinel SAR Interferometry

Science.gov (United States)

Krishnakumar, V.; Monserrat, O.; Crosetto, M.; Crippa, B.

2018-04-01

The repeat-pass Synthetic Aperture Radio Detection and Ranging (RADAR) Interferometry (InSAR) has been a widely used geodetic technique for observing the Earth's surface, especially for mapping the Earth's topography and deformations. However, InSAR measurements are prone to atmospheric errors. RADAR waves traverse the Earth's atmosphere twice and experience a delay due to atmospheric refraction. The two major layers of the atmosphere (troposphere and ionosphere) are mainly responsible for this delay in the propagating RADAR wave. Previous studies have shown that water vapour and clouds present in the troposphere and the Total Electron Content (TEC) of the ionosphere are responsible for the additional path delay in the RADAR wave. The tropospheric refractivity is mainly dependent on pressure, temperature and partial pressure of water vapour. The tropospheric refractivity leads to an increase in the observed range. These induced propagation delays affect the quality of phase measurement and introduce errors in the topography and deformation fields. The effect of this delay was studied on a differential interferogram (DInSAR). To calculate the amount of tropospheric delay occurred, the meteorological data collected from the Spanish Agencia Estatal de Meteorología (AEMET) and MODIS were used. The interferograms generated from Sentinel-1 carrying C-band Synthetic Aperture RADAR Single Look Complex (SLC) images acquired on the study area are used. The study area consists of different types of scatterers exhibiting different coherence. The existing Saastamoinen model was used to perform a quantitative evaluation of the phase changes caused by pressure, temperature and humidity of the troposphere during the study. Unless the phase values due to atmospheric disturbances are not corrected, it is difficult to obtain accurate measurements. Thus, the atmospheric error correction is essential for all practical applications of DInSAR to avoid inaccurate height and deformation

ATMOSPHERIC PHASE DELAY IN SENTINEL SAR INTERFEROMETRY

Directory of Open Access Journals (Sweden)

V. Krishnakumar

2018-04-01

Full Text Available The repeat-pass Synthetic Aperture Radio Detection and Ranging (RADAR Interferometry (InSAR has been a widely used geodetic technique for observing the Earth’s surface, especially for mapping the Earth’s topography and deformations. However, InSAR measurements are prone to atmospheric errors. RADAR waves traverse the Earth’s atmosphere twice and experience a delay due to atmospheric refraction. The two major layers of the atmosphere (troposphere and ionosphere are mainly responsible for this delay in the propagating RADAR wave. Previous studies have shown that water vapour and clouds present in the troposphere and the Total Electron Content (TEC of the ionosphere are responsible for the additional path delay in the RADAR wave. The tropospheric refractivity is mainly dependent on pressure, temperature and partial pressure of water vapour. The tropospheric refractivity leads to an increase in the observed range. These induced propagation delays affect the quality of phase measurement and introduce errors in the topography and deformation fields. The effect of this delay was studied on a differential interferogram (DInSAR. To calculate the amount of tropospheric delay occurred, the meteorological data collected from the Spanish Agencia Estatal de Meteorología (AEMET and MODIS were used. The interferograms generated from Sentinel-1 carrying C-band Synthetic Aperture RADAR Single Look Complex (SLC images acquired on the study area are used. The study area consists of different types of scatterers exhibiting different coherence. The existing Saastamoinen model was used to perform a quantitative evaluation of the phase changes caused by pressure, temperature and humidity of the troposphere during the study. Unless the phase values due to atmospheric disturbances are not corrected, it is difficult to obtain accurate measurements. Thus, the atmospheric error correction is essential for all practical applications of DInSAR to avoid inaccurate

Random Forests as a tool for estimating uncertainty at pixel-level in SAR image classification

DEFF Research Database (Denmark)

Loosvelt, Lien; Peters, Jan; Skriver, Henning

2012-01-01

, we introduce Random Forests for the probabilistic mapping of vegetation from high-dimensional remote sensing data and present a comprehensive methodology to assess and analyze classification uncertainty based on the local probabilities of class membership. We apply this method to SAR image data...

Polarimetric SAR interferometry-based decomposition modelling for reliable scattering retrieval

Science.gov (United States)

Agrawal, Neeraj; Kumar, Shashi; Tolpekin, Valentyn

2016-05-01

Fully Polarimetric SAR (PolSAR) data is used for scattering information retrieval from single SAR resolution cell. Single SAR resolution cell may contain contribution from more than one scattering objects. Hence, single or dual polarized data does not provide all the possible scattering information. So, to overcome this problem fully Polarimetric data is used. It was observed in previous study that fully Polarimetric data of different dates provide different scattering values for same object and coefficient of determination obtained from linear regression between volume scattering and aboveground biomass (AGB) shows different values for the SAR dataset of different dates. Scattering values are important input elements for modelling of forest aboveground biomass. In this research work an approach is proposed to get reliable scattering from interferometric pair of fully Polarimetric RADARSAT-2 data. The field survey for data collection was carried out for Barkot forest during November 10th to December 5th, 2014. Stratified random sampling was used to collect field data for circumference at breast height (CBH) and tree height measurement. Field-measured AGB was compared with the volume scattering elements obtained from decomposition modelling of individual PolSAR images and PolInSAR coherency matrix. Yamaguchi 4-component decomposition was implemented to retrieve scattering elements from SAR data. PolInSAR based decomposition was the great challenge in this work and it was implemented with certain assumptions to create Hermitian coherency matrix with co-registered polarimetric interferometric pair of SAR data. Regression analysis between field-measured AGB and volume scattering element obtained from PolInSAR data showed highest (0.589) coefficient of determination. The same regression with volume scattering elements of individual SAR images showed 0.49 and 0.50 coefficients of determination for master and slave images respectively. This study recommends use of

A new automatic SAR-based flood mapping application hosted on the European Space Agency's grid processing on demand fast access to imagery environment

Science.gov (United States)

Hostache, Renaud; Chini, Marco; Matgen, Patrick; Giustarini, Laura

2013-04-01

, UK and March 2010 Red River flood, US) observed by high-resolution SAR sensors as well as airborne photography highlight advantages and limitations of the online application. A mid-term target is the exploitation of ESA SENTINEL 1 SAR data streams. In the long term it is foreseen to develop a potential extension of the application for systematically extracting flooded areas from all SAR images acquired on a daily, weekly or monthly basis. On-going research activities investigate the usefulness of the method for mapping flood hazard at global scale using databases of historic SAR remote sensing-derived flood inundation maps.

Data Based Parameter Estimation Method for Circular-scanning SAR Imaging

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Chen Gong-bo

2013-06-01

Full Text Available The circular-scanning Synthetic Aperture Radar (SAR is a novel working mode and its image quality is closely related to the accuracy of the imaging parameters, especially considering the inaccuracy of the real speed of the motion. According to the characteristics of the circular-scanning mode, a new data based method for estimating the velocities of the radar platform and the scanning-angle of the radar antenna is proposed in this paper. By referring to the basic conception of the Doppler navigation technique, the mathematic model and formulations for the parameter estimation are firstly improved. The optimal parameter approximation based on the least square criterion is then realized in solving those equations derived from the data processing. The simulation results verified the validity of the proposed scheme.

Estimation of Bridge Height over Water from Polarimetric SAR Image Data Using Mapping and Projection Algorithm and De-Orientation Theory

Science.gov (United States)

Wang, Haipeng; Xu, Feng; Jin, Ya-Qiu; Ouchi, Kazuo

An inversion method of bridge height over water by polarimetric synthetic aperture radar (SAR) is developed. A geometric ray description to illustrate scattering mechanism of a bridge over water surface is identified by polarimetric image analysis. Using the mapping and projecting algorithm, a polarimetric SAR image of a bridge model is first simulated and shows that scattering from a bridge over water can be identified by three strip lines corresponding to single-, double-, and triple-order scattering, respectively. A set of polarimetric parameters based on the de-orientation theory is applied to analysis of three types scattering, and the thinning-clustering algorithm and Hough transform are then employed to locate the image positions of these strip lines. These lines are used to invert the bridge height. Fully polarimetric image data of airborne Pi-SAR at X-band are applied to inversion of the height and width of the Naruto Bridge in Japan. Based on the same principle, this approach is also applicable to spaceborne ALOSPALSAR single-polarization data of the Eastern Ocean Bridge in China. The results show good feasibility to realize the bridge height inversion.

Subsidence Detected by Multi-Pass Differential SAR Interferometry in the Cassino Plain (Central Italy: Joint Effect of Geological and Anthropogenic Factors?

Directory of Open Access Journals (Sweden)

Marco Polcari

2014-10-01

Full Text Available In the present work, the Differential SAR Interferometry (DInSAR technique has been applied to study the surface movements affecting the sedimentary basin of Cassino municipality. Two datasets of SAR images, provided by ERS 1-2 and Envisat missions, have been acquired from 1992 to 2010. Such datasets have been processed independently each other and with different techniques nevertheless providing compatible results. DInSAR data show a subsidence rate mostly located in the northeast side of the city, with a subsidence rate decreasing from about 5–6 mm/yr in the period 1992–2000 to about 1–2 mm/yr between 2004 and 2010, highlighting a progressive reduction of the phenomenon. Based on interferometric results and geological/geotechnical observations, the explanation of the detected movements allows to confirm the anthropogenic (surface effect due to building construction and geological causes (thickness and characteristics of the compressible stratum.

High-speed railway bridge dynamic measurement based on GB-InSAR technology

Science.gov (United States)

Liu, Miao; Ding, Ke-liang; Liu, Xianglei; Song, Zichao

2015-12-01

It is an important task to evaluate the safety during the life of bridges using the corresponding vibration parameters. With the advantages of non-contact and high accuracy, the new remote measurement technology of GB-InSAR is suitable to make dynamic measurement for bridges to acquire the vibration parameters. Three key technologies, including stepped frequency-continuous wave technique, synthetic aperture radar and interferometric measurement technique, are introduced in this paper. The GB-InSAR is applied for a high-speed railway bridge to measure of dynamic characteristics with the train passing which can be used to analyze the safety of the monitored bridge. The test results shown that it is an reliable non-contact technique for GB-InSAR to acquire the dynamic vibration parameter for the high-speed railway bridges.

Combined acquisition technique (CAT for neuroimaging of multiple sclerosis at low specific absorption rates (SAR.

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Armin Biller

Full Text Available PURPOSE: To compare a novel combined acquisition technique (CAT of turbo-spin-echo (TSE and echo-planar-imaging (EPI with conventional TSE. CAT reduces the electromagnetic energy load transmitted for spin excitation. This radiofrequency (RF burden is limited by the specific absorption rate (SAR for patient safety. SAR limits restrict high-field MRI applications, in particular. MATERIAL AND METHODS: The study was approved by the local Medical Ethics Committee. Written informed consent was obtained from all participants. T2- and PD-weighted brain images of n = 40 Multiple Sclerosis (MS patients were acquired by CAT and TSE at 3 Tesla. Lesions were recorded by two blinded, board-certificated neuroradiologists. Diagnostic equivalence of CAT and TSE to detect MS lesions was evaluated along with their SAR, sound pressure level (SPL and sensations of acoustic noise, heating, vibration and peripheral nerve stimulation. RESULTS: Every MS lesion revealed on TSE was detected by CAT according to both raters (Cohen's kappa of within-rater/across-CAT/TSE lesion detection κCAT = 1.00, at an inter-rater lesion detection agreement of κLES = 0.82. CAT reduced the SAR burden significantly compared to TSE (p<0.001. Mean SAR differences between TSE and CAT were 29.0 (± 5.7 % for the T2-contrast and 32.7 (± 21.9 % for the PD-contrast (expressed as percentages of the effective SAR limit of 3.2 W/kg for head examinations. Average SPL of CAT was no louder than during TSE. Sensations of CAT- vs. TSE-induced heating, noise and scanning vibrations did not differ. CONCLUSION: T2-/PD-CAT is diagnostically equivalent to TSE for MS lesion detection yet substantially reduces the RF exposure. Such SAR reduction facilitates high-field MRI applications at 3 Tesla or above and corresponding protocol standardizations but CAT can also be used to scan faster, at higher resolution or with more slices. According to our data, CAT is no more uncomfortable than TSE scanning.

SAR Imaging of Wave Tails: Recognition of Second Mode Internal Wave Patterns and Some Mechanisms of their Formation

Science.gov (United States)

da Silva, Jose C. B.; Magalhaes, J. M.; Buijsman, M. C.; Garcia, C. A. E.

2016-08-01

Mode-2 internal waves are usually not as energetic as larger mode-1 Internal Solitary Waves (ISWs), but they have attracted a great deal of attention in recent years because they have been identified as playing a significant role in mixing shelf waters [1]. This mixing is particularly effective for mode-2 ISWs because the location of these waves in the middle of the pycnocline plays an important role in eroding the barrier between the base of the surface mixed layer and the stratified deep layer below. An urgent problem in physical oceanography is therefore to account for the magnitude and distribution of ISW-driven mixing, including mode-2 ISWs. Several generation mechanisms of mode-2 ISWs have been identified. These include: (1) mode-1 ISWs propagating onshore (shoaling) and entering the breaking instability stage, or propagating over a steep sill; (2) a mode-1 ISW propagating offshore (antishoaling) over steep slopes of the shelf break, and undergoing modal transformation; (3) intrusion of the whole head of a gravity current into a three-layer fluid; (4) impingement of an internal tidal beam on the pycnocline, itself emanating from critical bathymetry; (5) nonlinear disintegration of internal tide modes; (6) lee wave mechanism. In this paper we provide methods to identify internal wave features denominated "Wave Tails" in SAR images of the ocean surface, which are many times associated with second mode internal waves. The SAR case studies that are presented portray evidence of the aforementioned generation mechanisms, and we further discuss possible methods to discriminate between the various types of mode-2 ISWs in SAR images, that emerge from these physical mechanisms. Some of the SAR images correspond to numerical simulations with the MITgcm in fully nonlinear and nonhydrostatic mode and in a 2D configuration with realistic stratification, bathymetry and other environmental conditions.Results of a global survey with some of these observations are presented

New Processing of Spaceborne Imaging Radar-C (SIR-C) Data

Science.gov (United States)

Meyer, F. J.; Gracheva, V.; Arko, S. A.; Labelle-Hamer, A. L.

2017-12-01

The Spaceborne Imaging Radar-C (SIR-C) was a radar system, which successfully operated on two separate shuttle missions in April and October 1994. During these two missions, a total of 143 hours of radar data were recorded. SIR-C was the first multifrequency and polarimetric spaceborne radar system, operating in dual frequency (L- and C- band) and with quad-polarization. SIR-C had a variety of different operating modes, which are innovative even from today's point of view. Depending on the mode, it was possible to acquire data with different polarizations and carrier frequency combinations. Additionally, different swaths and bandwidths could be used during the data collection and it was possible to receive data with two antennas in the along-track direction.The United States Geological Survey (USGS) distributes the synthetic aperture radar (SAR) images as single-look complex (SLC) and multi-look complex (MLC) products. Unfortunately, since June 2005 the SIR-C processor has been inoperable and not repairable. All acquired SLC and MLC images were processed with a course resolution of 100 m with the goal of generating a quick look. These images are however not well suited for scientific analysis. Only a small percentage of the acquired data has been processed as full resolution SAR images and the unprocessed high resolution data cannot be processed any more at the moment.At the Alaska Satellite Facility (ASF) a new processor was developed to process binary SIR-C data to full resolution SAR images. ASF is planning to process the entire recoverable SIR-C archive to full resolution SLCs, MLCs and high resolution geocoded image products. ASF will make these products available to the science community through their existing data archiving and distribution system.The final paper will describe the new processor and analyze the challenges of reprocessing the SIR-C data.

Detecting and monitoring UCG subsidence with InSAR

Energy Technology Data Exchange (ETDEWEB)

Mellors, R J; Foxall, W; Yang, X

2012-03-23

The use of interferometric synthetic aperture radar (InSAR) to measure surface subsidence caused by Underground Coal Gasification (UCG) is tested. InSAR is a remote sensing technique that uses Synthetic Aperture Radar images to make spatial images of surface deformation and may be deployed from satellite or an airplane. With current commercial satellite data, the technique works best in areas with little vegetation or farming activity. UCG subsidence is generally caused by roof collapse, which adversely affects UCG operations due to gas loss and is therefore important to monitor. Previous studies have demonstrated the usefulness of InSAR in measuring surface subsidence related to coal mining and surface deformation caused by a coal mining roof collapse in Crandall Canyon, Utah is imaged as a proof-of-concept. InSAR data is collected and processed over three known UCG operations including two pilot plants (Majuba, South Africa and Wulanchabu, China) and an operational plant (Angren, Uzbekistan). A clear f eature showing approximately 7 cm of subsidence is observed in the UCG field in Angren. Subsidence is not observed in the other two areas, which produce from deeper coal seams and processed a smaller volume. The results show that in some cases, InSAR is a useful tool to image UCG related subsidence. Data from newer satellites and improved algorithms will improve effectiveness.

Land subsidence caused by the East Mesa geothermal field, California, observed using SAR interferometry

Science.gov (United States)

Massonnet, D.; Holzer, T.; Vadon, H.

1997-01-01

Interferometric combination of pairs of synthetic aperture radar (SAR) images acquired by the ERS-1 satellite maps the deformation field associated with the activity of the East Mesa geothermal plant, located in southern California. SAR interferometry is applied to this flat area without the need of a digital terrain model. Several combinations are used to ascertain the nature of the phenomenon. Short term interferograms reveal surface phase changes on agricultural fields similar to what had been observed previously with SEASAT radar data. Long term (2 years) interferograms allow the study of land subsidence and improve prior knowledge of the displacement field, and agree with existing, sparse levelling data. This example illustrates the power of the interferometric technique for deriving accurate industrial intelligence as well as its potential for legal action, in cases involving environmental damages. Copyright 1997 by the American Geophysical Union.

Investigation of the Capability of Compact Polarimetric SAR Interferometry to Estimate Forest Height

Science.gov (United States)

Zhang, Hong; Xie, Lei; Wang, Chao; Chen, Jiehong

2013-08-01

The main objective of this paper is to investigate the capability of compact Polarimetric SAR Interferometry (C-PolInSAR) on forest height estimation. For this, the pseudo fully polarimetric interferomteric (F-PolInSAR) covariance matrix is firstly reconstructed, then the three- stage inversion algorithm, hybrid algorithm, Music and Capon algorithm are applied to both C-PolInSAR covariance matrix and pseudo F-PolInSAR covariance matrix. The availability of forest height estimation is demonstrated using L-band data generated by simulator PolSARProSim and X-band airborne data acquired by East China Research Institute of Electronic Engineering, China Electronics Technology Group Corporation.

Automated inundation monitoring using TerraSAR-X multitemporal imagery

Science.gov (United States)

Gebhardt, S.; Huth, J.; Wehrmann, T.; Schettler, I.; Künzer, C.; Schmidt, M.; Dech, S.

2009-04-01

The Mekong Delta in Vietnam offers natural resources for several million inhabitants. However, a strong population increase, changing climatic conditions and regulatory measures at the upper reaches of the Mekong lead to severe changes in the Delta. Extreme flood events occur more frequently, drinking water availability is increasingly limited, soils show signs of salinization or acidification, species and complete habitats diminish. During the Monsoon season the river regularly overflows its banks in the lower Mekong area, usually with beneficial effects. However, extreme flood events occur more frequently causing extensive damage, on the average once every 6 to 10 years river flood levels exceed the critical beneficial level X-band SAR data are well suited for deriving inundated surface areas. The TerraSAR-X sensor with its different scanning modi allows for the derivation of spatial and temporal high resolved inundation masks. The paper presents an automated procedure for deriving inundated areas from TerraSAR-X Scansar and Stripmap image data. Within the framework of the German-Vietnamese WISDOM project, focussing the Mekong Delta region in Vietnam, images have been acquired covering the flood season from June 2008 to November 2008. Based on these images a time series of the so called watermask showing inundated areas have been derived. The product is required as intermediate to (i) calibrate 2d inundation model scenarios, (ii) estimate the extent of affected areas, and (iii) analyze the scope of prior crisis. The image processing approach is based on the assumption that water surfaces are forward scattering the radar signal resulting in low backscatter signals to the sensor. It uses multiple grey level thresholds and image morphological operations. The approach is robust in terms of automation, accuracy, robustness, and processing time. The resulting watermasks show the seasonal flooding pattern with inundations starting in July, having their peak at the end

Identification of Systemic Acquired Resistance–Related Volatile Organic Compounds and their Role in Plant Immunity

OpenAIRE

Bichlmeier, Marlies

2017-01-01

Systemic acquired resistance (SAR) is an inducible immune response that depends on ENHANCED DISEASE SUSCEPTIBILITY1 (EDS1), which is essential for SAR signalling. In contrast to SAR, local resistance remains intact in Arabidopsis (Arabidopsis thaliana) eds1-2 mutant plants in response to Pseudomonas syringae delivering the effector protein AvrRpm1. I utilized the SAR-specific phenotype of the eds1-2 mutant to identify volatile organic compounds (VOCs) related to SAR. To this end, SAR was indu...

Classification of surface types using SIR-C/X-SAR, Mount Everest Area, Tibet

Science.gov (United States)

Albright, Thomas P.; Painter, Thomas H.; Roberts, Dar A.; Shi, Jiancheng; Dozier, Jeff; Fielding, Eric

1998-01-01

Imaging radar is a promising tool for mapping snow and ice cover in alpine regions. It combines a high-resolution, day or night, all-weather imaging capability with sensitivity to hydrologic and climatic snow and ice parameters. We use the spaceborne imaging radar-C/X-band synthetic aperture radar (SIR-C/X-SAR) to map snow and glacial ice on the rugged north slope of Mount Everest. From interferometrically derived digital elevation data, we compute the terrain calibration factor and cosine of the local illumination angle. We then process and terrain-correct radar data sets acquired on April 16, 1994. In addition to the spectral data, we include surface slope to improve discrimination among several surface types. These data sets are then used in a decision tree to generate an image classification. This method is successful in identifying and mapping scree/talus, dry snow, dry snow-covered glacier, wet snow-covered glacier, and rock-covered glacier, as corroborated by comparison with existing surface cover maps and other ancillary information. Application of the classification scheme to data acquired on October 7 of the same year yields accurate results for most surface types but underreports the extent of dry snow cover.

Training Convolutional Neural Networks for Translational Invariance on SAR ATR

DEFF Research Database (Denmark)

Malmgren-Hansen, David; Engholm, Rasmus; Østergaard Pedersen, Morten

2016-01-01

In this paper we present a comparison of the robustness of Convolutional Neural Networks (CNN) to other classifiers in the presence of uncertainty of the objects localization in SAR image. We present a framework for simulating simple SAR images, translating the object of interest systematically...

Knowledge-based sea ice classification by polarimetric SAR

DEFF Research Database (Denmark)

Skriver, Henning; Dierking, Wolfgang

2004-01-01

Polarimetric SAR images acquired at C- and L-band over sea ice in the Greenland Sea, Baltic Sea, and Beaufort Sea have been analysed with respect to their potential for ice type classification. The polarimetric data were gathered by the Danish EMISAR and the US AIRSAR which both are airborne...... systems. A hierarchical classification scheme was chosen for sea ice because our knowledge about magnitudes, variations, and dependences of sea ice signatures can be directly considered. The optimal sequence of classification rules and the rules themselves depend on the ice conditions/regimes. The use...... of the polarimetric phase information improves the classification only in the case of thin ice types but is not necessary for thicker ice (above about 30 cm thickness)...

Post-Eruptive Inflation of Okmok Volcano, Alaska, from InSAR, 2008–2014

Directory of Open Access Journals (Sweden)

Feifei Qu

2015-12-01

Full Text Available Okmok, a ~10-km wide caldera that occupies most of the northeastern end of Umnak Island, is one of the most active volcanoes in the Aleutian arc. The most recent eruption at Okmok during July–August 2008 was by far its largest and most explosive since at least the early 19th century. We investigate post-eruptive magma supply and storage at the volcano during 2008–2014 by analyzing all available synthetic aperture radar (SAR images of Okmok acquired during that time period using the multi-temporal InSAR technique. Data from the C-band Envisat and X-band TerraSAR-X satellites indicate that Okmok started inflating very soon after the end of 2008 eruption at a time-variable rate of 48–130 mm/y, consistent with GPS measurements. The “model-assisted” phase unwrapping method is applied to improve the phase unwrapping operation for long temporal baseline pairs. The InSAR time-series is used as input for deformation source modeling, which suggests magma accumulating at variable rates in a shallow storage zone at ~3.9 km below sea level beneath the summit caldera, consistent with previous studies. The modeled volume accumulation in the six years following the 2008 eruption is ~75% of the 1997 eruption volume and ~25% of the 2008 eruption volume.

Post-eruptive inflation of Okmok Volcano, Alaska, from InSAR, 2008–2014

Science.gov (United States)

Qu, Feifei; Lu, Zhong; Poland, Michael; Freymueller, Jeffrey T.; Zhang, Qin; Jung, Hyung-Sup

2016-01-01

Okmok, a ~10-km wide caldera that occupies most of the northeastern end of Umnak Island, is one of the most active volcanoes in the Aleutian arc. The most recent eruption at Okmok during July-August 2008 was by far its largest and most explosive since at least the early 19th century. We investigate post-eruptive magma supply and storage at the volcano during 2008–2014 by analyzing all available synthetic aperture radar (SAR) images of Okmok acquired during that time period using the multi-temporal InSAR technique. Data from the C-band Envisat and X-band TerraSAR-X satellites indicate that Okmok started inflating very soon after the end of 2008 eruption at a time-variable rate of 48-130 mm/y, consistent with GPS measurements. The “model-assisted” phase unwrapping method is applied to improve the phase unwrapping operation for long temporal baseline pairs. The InSAR time-series is used as input for deformation source modeling, which suggests magma accumulating at variable rates in a shallow storage zone at ~3.9 km below sea level beneath the summit caldera, consistent with previous studies. The modeled volume accumulation in the 6 years following the 2008 eruption is ~75% of the 1997 eruption volume and ~25% of the 2008 eruption volume.

Wave directional spectrum from SAR imagery

Digital Repository Service at National Institute of Oceanography (India)

Fernandes, A.A.; Sarma, Y.V.B.; Menon, H.B.; Vethamony, P.

< 2m and the zero-crossing period during the satellite overpass is small (< 6s, �O�O < 60m). We therefore utilized the visit of one of the authors (Sarma) to the Southampton Oceanographic Centre, U.K., to procure two ERS-1 digital image mode SAR...-dimensional FFT as well as a computer program for downloading SAR data from CCT. Finally we owe a debt of gratitude to J C da Silva, Southampton Oceanographic Centre, U K for sharing some of his SAR data with us. References Allan T. D. (Ed) (1983...

Landslide precursory deformation interpretation using ALOS-2/PALSAR-2 InSAR image along Min River in Maoxien, Sichuan Province, China

Science.gov (United States)

Sato, H. P.

2017-12-01

Maoxien area in Sichuan Province, China has many landslide. For example, landslide (rock avalanche) occurred on the slope in Xinmocun Village in Maoxeien on 24 June 2017. I produced and interpreetd InSAR image using ALOS/PALSAR data observed on 19 Jul 2007-3 Sep 2007 and on 27 Jan 2011-14 Mar 2011, and ALOS-2/PALSAR-2 data observed on 26 Jul 2015-13 Dec 2015 and on 13 Dec 2015-11 Dec 2016. These images give good coherence and it was easy to identify local landslide surface deformation. As a result, e.g., two slopes were estimated to have local landslide surface deformation; one is at 103.936587 deg E and 32.04462 deg N, another is at 103.674754 deg E and 31.852838 N. However, the slope in Xinmocun Village was not identified as landslide precursory deformation. In the poster I will present more InSAR image observed after 11 Dec 2016 and discuss the possibility of local landslide surface deformaton using InSAR image. ALOS/PALSAR and ALOS-2/PALSAR-2 data were provided by JAXA through Landslide Working Group in JAXA and through Special Research 2015-B-02 of Earthquake Research Institute/Tokyo University. This study was supported by KAKENHI (17H02973).

The Advanced Rapid Imaging and Analysis (ARIA) Project: Status of SAR products for Earthquakes, Floods, Volcanoes and Groundwater-related Subsidence

Science.gov (United States)

Owen, S. E.; Yun, S. H.; Hua, H.; Agram, P. S.; Liu, Z.; Sacco, G. F.; Manipon, G.; Linick, J. P.; Fielding, E. J.; Lundgren, P.; Farr, T. G.; Webb, F.; Rosen, P. A.; Simons, M.

2017-12-01

The Advanced Rapid Imaging and Analysis (ARIA) project for Natural Hazards is focused on rapidly generating high-level geodetic imaging products and placing them in the hands of the solid earth science and local, national, and international natural hazard communities by providing science product generation, exploration, and delivery capabilities at an operational level. Space-based geodetic measurement techniques including Interferometric Synthetic Aperture Radar (InSAR), differential Global Positioning System, and SAR-based change detection have become critical additions to our toolset for understanding and mapping the damage and deformation caused by earthquakes, volcanic eruptions, floods, landslides, and groundwater extraction. Up until recently, processing of these data sets has been handcrafted for each study or event and has not generated products rapidly and reliably enough for response to natural disasters or for timely analysis of large data sets. The ARIA project, a joint venture co-sponsored by the California Institute of Technology and by NASA through the Jet Propulsion Laboratory, has been capturing the knowledge applied to these responses and building it into an automated infrastructure to generate imaging products in near real-time that can improve situational awareness for disaster response. In addition to supporting the growing science and hazard response communities, the ARIA project has developed the capabilities to provide automated imaging and analysis capabilities necessary to keep up with the influx of raw SAR data from geodetic imaging missions such as ESA's Sentinel-1A/B, now operating with repeat intervals as short as 6 days, and the upcoming NASA NISAR mission. We will present the progress and results we have made on automating the analysis of Sentinel-1A/B SAR data for hazard monitoring and response, with emphasis on recent developments and end user engagement in flood extent mapping and deformation time series for both volcano

Spatiotemporal Characterization of Land Subsidence and Uplift (2009–2010 over Wuhan in Central China Revealed by TerraSAR-X InSAR Analysis

Directory of Open Access Journals (Sweden)

Lin Bai

2016-04-01

Full Text Available The effects of ground deformation pose a significant geo-hazard to the environment and infrastructure in Wuhan, the most populous city in Central China, in the eastern Jianghan Plain at the intersection of the Yangtze and Han rivers. Prior to this study, however, rates and patterns of region-wide ground deformation in Wuhan were little known. Here we employ multi-temporal SAR interferometry to detect and characterize spatiotemporal variations of ground deformation in major metropolitan areas in Wuhan. A total of twelve TerraSAR-X images acquired during 2009–2010 are used in the InSAR time series analysis. InSAR-derived results are validated by levelling survey measurements and reveal a distinct subsidence pattern within six zones in major commercial and industrial areas, with a maximum subsidence rate up to −67.3 mm/year. A comparison analysis between subsiding patterns and urban developments as well as geological conditions suggests that land subsidence in Wuhan is mainly attributed to anthropogenic activities, natural compaction of soft soil, and karst dissolution of subsurface carbonate rocks. However, anthropogenic activities related to intensive municipal construction and industrial production have more significant impacts on the measured subsidence than natural factors. Moreover, remarkable signals of secular land uplift are found along both banks of the Yangtze River, especially along the southern bank, with deformation rates ranging mostly from +5 mm/year to +17.5 mm/year. A strong temporal correlation is highlighted between the detected displacement evolutions and the water level records of the Yangtze River, inferring that this previously unknown deformation phenomenon is likely related to seasonal fluctuations in water levels of the Yangtze River.

On the absence of InSAR-detected volcano deformation spanning the 1995-1996 and 1999 eruptions of Shishaldin Volcano, Alaska

Science.gov (United States)

Moran, S.C.; Kwoun, O.; Masterlark, Timothy; Lu, Z.

2006-01-01

Shishaldin Volcano, a large, frequently active basaltic-andesite volcano located on Unimak Island in the Aleutian Arc of Alaska, had a minor eruption in 1995–1996 and a VEI 3 sub-Plinian basaltic eruption in 1999. We used 21 synthetic aperture radar images acquired by ERS-1, ERS-2, JERS-1, and RADARSAT-1 satellites to construct 12 coherent interferograms that span most of the 1993–2003 time interval. All interferograms lack coherence within ∼5 km of the summit, primarily due to persistent snow and ice cover on the edifice. Remarkably, in the 5–15 km distance range where interferograms are coherent, the InSAR images show no intrusion- or withdrawal-related deformation at Shishaldin during this entire time period. However, several InSAR images do show deformation associated with a shallow ML 5.2 earthquake located ∼14 km west of Shishaldin that occurred 6 weeks before the 1999 eruption. We use a theoretical model to predict deformation magnitudes due to a volumetric expansion source having a volume equivalent to the 1999 erupted volume, and find that deformation magnitudes for sources shallower than 10 km are within the expected detection capabilities for interferograms generated from C-band ERS 1/2 and RADARSAT-1 synthetic aperture radar images. We also find that InSAR images cannot resolve relatively shallow deformation sources (1–2 km below sea level) due to spatial gaps in the InSAR images caused by lost coherence. The lack of any deformation, particularly for the 1999 eruption, leads us to speculate that magma feeding eruptions at the summit moves rapidly (at least 80m/day) from > 10 km depth, and that the intrusion–eruption cycle at Shishaldin does not produce significant permanent deformation at the surface.

Significant Wave Height under Hurricane Irma derived from SAR Sentinel-1 Data

Science.gov (United States)

Lehner, S.; Pleskachevsky, A.; Soloviev, A.; Fujimura, A.

2017-12-01

The 2017 Atlantic hurricane season was with three major hurricanes a particular active one. The Category 4 hurricane Irma made landfall on the Florida Keys on September 10th 2017 and was imaged several times by ESAs Sentinel-1 satellites in C-band and the TerraSAR-X satellite in X-band. The high resolution TerraSAR-X imagery showed the footprint of individual tornadoes on the sea surface together with their turbulent wake imaged as a dark line due to increased turbulence. The water-cloud structures of the tornadoes are analyzed and their sea surface structure is compared to optical and IR cloud imagery. An estimate of the wind field using standard XMOD algorithms is provided, although saturating under the strong rain and high wind speed conditions. Imaging the hurricanes by space radar gives the opportunity to observe the sea surface and thus measure the wind field and the sea state under hurricane conditions through the clouds even in this severe weather, although rain features, which are usually not observed in SAR become visible due to damping effects. The Copernicus Sentinel-1 A and B satellites, which are operating in C-band provided several images of the sea surface under hurricane Irma, Jose and Maria. The data were acquired daily and converted into measurements of sea surface wind field u10 and significant wave height Hs over a swath width of 280km about 1000 km along the orbit. The wind field of the hurricanes as derived by CMOD is provided by NOAA operationally on their web server. In the hurricane cases though the wind speed saturates at 20 m/sec and is thus too low in the area of hurricane wind speed. The technique to derive significant wave height is new though and does not show any calibration issues. This technique provides for the first time measurements of the areal coverage and distribution of the ocean wave height as caused by a hurricane on SAR wide swath images. Wave heights up to 10 m were measured under the forward quadrant of the hurricane

A MATCHING METHOD TO REDUCE THE INFLUENCE OF SAR GEOMETRIC DEFORMATION

Directory of Open Access Journals (Sweden)

C. Gao

2018-04-01

Full Text Available There are large geometrical deformations in SAR image, including foreshortening, layover, shade，which leads to SAR Image matching with low accuracy. Especially in complex terrain area, the control points are difficult to obtain, and the matching is difficult to achieve. Considering the impact of geometric distortions in SAR image pairs, a matching algorithm with a combination of speeded up robust features (SURF and summed of normalize cross correlation (SNCC was proposed, which can avoid the influence of SAR geometric deformation. Firstly, SURF algorithm was utilized to predict the search area. Then the matching point pairs was selected based on summed of normalized cross correlation. Finally, false match points were eliminated by the bidirectional consistency. SURF algorithm can control the range of matching points, and the matching points extracted from the deformation area are eliminated, and the matching points with stable and even distribution are obtained. The experimental results demonstrated that the proposed algorithm had high precision, and can effectively avoid the effect of geometric distortion on SAR image matching. Meet accuracy requirements of the block adjustment with sparse control points.

Structural Classification of Marshes with Polarimetric SAR Highlighting the Temporal Mapping of Marshes Exposed to Oil

Directory of Open Access Journals (Sweden)

Elijah Ramsey

2015-09-01

Full Text Available Empirical relationships between field-derived Leaf Area Index (LAI and Leaf Angle Distribution (LAD and polarimetric synthetic aperture radar (PolSAR based biophysical indicators were created and applied to map S. alterniflora marsh canopy structure. PolSAR and field data were collected near concurrently in the summers of 2010, 2011, and 2012 in coastal marshes, and PolSAR data alone were acquired in 2009. Regression analyses showed that LAI correspondence with the PolSAR biophysical indicator variables equaled or exceeded those of vegetation water content (VWC correspondences. In the final six regressor model, the ratio HV/VV explained 49% of the total 77% explained LAI variance, and the HH-VV coherence and phase information accounted for the remainder. HV/HH dominated the two regressor LAD relationship, and spatial heterogeneity and backscatter mechanism followed by coherence information dominated the final three regressor model that explained 74% of the LAD variance. Regression results applied to 2009 through 2012 PolSAR images showed substantial changes in marsh LAI and LAD. Although the direct cause was not substantiated, following a release of freshwater in response to the 2010 Deepwater Horizon oil spill, the fairly uniform interior marsh structure of 2009 was more vertical and dense shortly after the oil spill cessation. After 2010, marsh structure generally progressed back toward the 2009 uniformity; however, the trend was more disjointed in oil impact marshes.

Mapping mountain meadow with high resolution and polarimetric SAR data

International Nuclear Information System (INIS)

Tian, Bangsen; Li, Zhen; Xu, Juan; Fu, Sitao; Liu, Jiuli

2014-01-01

This paper presents a method to map the large grassland in the eastern margin of the Tibetan Plateau with the high resolution polarimetric SAR (PolSAR) imagery. When PolSAR imagery is used for land cover classification, the brightness of a SAR image is affected by topography due to varying projection between ground and image coordinates. The objective of this paper is twofold: (1) we first extend the theory of SAR terrain correction to the polarimetric case, to utilize the entire available polarimetric signature, where correction is performed explicitly based on a matrix format like covariance matrix. (2) Next, the orthoectified PolSAR is applied to classify mountain meadow and investigate the potential of PolSAR in mapping grassland. In this paper, the gamma naught radiometric correction estimates the local illuminated area at each grid point in the radar geometry. Then, each element of the coherency matrix is divided by the local area to produce a polarimetric product. Secondly, the impact of radiometric correction upon classification accuracy is investigated. A supervised classification is performed on the orthorectified Radarsat-2 PolSAR to map the spatial distribution of meadow and evaluate monitoring capabilities of mountain meadow

Adaptive thresholding algorithm based on SAR images and wind data to segment oil spills along the northwest coast of the Iberian Peninsula

International Nuclear Information System (INIS)

Mera, David; Cotos, José M.; Varela-Pet, José; Garcia-Pineda, Oscar

2012-01-01

Highlights: ► We present an adaptive thresholding algorithm to segment oil spills. ► The segmentation algorithm is based on SAR images and wind field estimations. ► A Database of oil spill confirmations was used for the development of the algorithm. ► Wind field estimations have demonstrated to be useful for filtering look-alikes. ► Parallel programming has been successfully used to minimize processing time. - Abstract: Satellite Synthetic Aperture Radar (SAR) has been established as a useful tool for detecting hydrocarbon spillage on the ocean’s surface. Several surveillance applications have been developed based on this technology. Environmental variables such as wind speed should be taken into account for better SAR image segmentation. This paper presents an adaptive thresholding algorithm for detecting oil spills based on SAR data and a wind field estimation as well as its implementation as a part of a functional prototype. The algorithm was adapted to an important shipping route off the Galician coast (northwest Iberian Peninsula) and was developed on the basis of confirmed oil spills. Image testing revealed 99.93% pixel labelling accuracy. By taking advantage of multi-core processor architecture, the prototype was optimized to get a nearly 30% improvement in processing time.

An Integrated Processing Strategy for Mountain Glacier Motion Monitoring Based on SAR Images

Science.gov (United States)

Ruan, Z.; Yan, S.; Liu, G.; LV, M.

2017-12-01

Mountain glacier dynamic variables are important parameters in studies of environment and climate change in High Mountain Asia. Due to the increasing events of abnormal glacier-related hazards, research of monitoring glacier movements has attracted more interest during these years. Glacier velocities are sensitive and changing fast under complex conditions of high mountain regions, which implies that analysis of glacier dynamic changes requires comprehensive and frequent observations with relatively high accuracy. Synthetic aperture radar (SAR) has been successfully exploited to detect glacier motion in a number of previous studies, usually with pixel-tracking and interferometry methods. However, the traditional algorithms applied to mountain glacier regions are constrained by the complex terrain and diverse glacial motion types. Interferometry techniques are prone to fail in mountain glaciers because of their narrow size and the steep terrain, while pixel-tracking algorithm, which is more robust in high mountain areas, is subject to accuracy loss. In order to derive glacier velocities continually and efficiently, we propose a modified strategy to exploit SAR data information for mountain glaciers. In our approach, we integrate a set of algorithms for compensating non-glacial-motion-related signals which exist in the offset values retrieved by sub-pixel cross-correlation of SAR image pairs. We exploit modified elastic deformation model to remove the offsets associated with orbit and sensor attitude, and for the topographic residual offset we utilize a set of operations including DEM-assisted compensation algorithm and wavelet-based algorithm. At the last step of the flow, an integrated algorithm combining phase and intensity information of SAR images will be used to improve regional motion results failed in cross-correlation related processing. The proposed strategy is applied to the West Kunlun Mountain and Muztagh Ata region in western China using ALOS

Application of Satellite SAR for Discovery and Quantification of Natural Marine Oil Seeps

Science.gov (United States)

Amos, J.; Lai, R.; Zimmer, B.; Leiva, A.; MacDonald, I.

2006-12-01

Natural marine oil seeps discharge gassy drops from the seafloor. Oil drops and gas bubbles reach the surface from water depths as great as 3000m. The oil spreads rapidly, forming an invisible layer that drifts down-wind and down-current in long, linear streaks called slicks. Oil slicks are visible in SAR data because the surfactant dampens capillary waves and reduces backscatter. Application of SAR as an exploration tool in energy prospecting is well-established. We have applied this technique for discovering the chemosynthetic communities that colonize the seafloor in the vicinity of deep-water seeps on the continental margin of the Gulf of Mexico. The management goal for this effort is to prevent harmful impact to these communities resulting from exploration or production activities. The scientific goals are to delineate the zoogeography of the chemosynthetic fauna, which is widespread on continental margins, and to establish study sites where their life history can be investigated. In the course of an ongoing, multidisciplinary study in the spring and summer of 2006, we explored 20 possible sites where SAR and geophysical data indicated seeps might occur. SAR was only partly diagnostic: all sites with SAR-detected slicks were found to have biologic communities, but communities were also found at geophysical anomalies that did not produce slicks. We acquired over 60 RADARSAT SAR images from the northern Gulf of Mexico in cooperation with the Alaska Satellite Facility. The ship RV ATLANTIS was at sea during the acquisition and collected synoptic weather and oceanographic data. To automate interpretation of large image dataset we have employed texture recognition with use of a library of textons applied iteratively to the images. This treatment shows promise in distinguishing floating oil from false targets generated by rain fronts and other phenomena. One goal of the analysis is to delineate bounding boxes to quantify the ocean area covered by the thin oil layer

SAR Imagery Segmentation by Statistical Region Growing and Hierarchical Merging

Energy Technology Data Exchange (ETDEWEB)

Ushizima, Daniela Mayumi; Carvalho, E.A.; Medeiros, F.N.S.; Martins, C.I.O.; Marques, R.C.P.; Oliveira, I.N.S.

2010-05-22

This paper presents an approach to accomplish synthetic aperture radar (SAR) image segmentation, which are corrupted by speckle noise. Some ordinary segmentation techniques may require speckle filtering previously. Our approach performs radar image segmentation using the original noisy pixels as input data, eliminating preprocessing steps, an advantage over most of the current methods. The algorithm comprises a statistical region growing procedure combined with hierarchical region merging to extract regions of interest from SAR images. The region growing step over-segments the input image to enable region aggregation by employing a combination of the Kolmogorov-Smirnov (KS) test with a hierarchical stepwise optimization (HSWO) algorithm for the process coordination. We have tested and assessed the proposed technique on artificially speckled image and real SAR data containing different types of targets.

Estimating Elevation Angles From SAR Crosstalk

Science.gov (United States)

Freeman, Anthony

1994-01-01

Scheme for processing polarimetric synthetic-aperture-radar (SAR) image data yields estimates of elevation angles along radar beam to target resolution cells. By use of estimated elevation angles, measured distances along radar beam to targets (slant ranges), and measured altitude of aircraft carrying SAR equipment, one can estimate height of target terrain in each resolution cell. Monopulselike scheme yields low-resolution topographical data.

Millimeter wave radar system on a rotating platform for combined search and track functionality with SAR imaging

Science.gov (United States)

Aulenbacher, Uwe; Rech, Klaus; Sedlmeier, Johannes; Pratisto, Hans; Wellig, Peter

2014-10-01

Ground based millimeter wave radar sensors offer the potential for a weather-independent automatic ground surveillance at day and night, e.g. for camp protection applications. The basic principle and the experimental verification of a radar system concept is described, which by means of an extreme off-axis positioning of the antenna(s) combines azimuthal mechanical beam steering with the formation of a circular-arc shaped synthetic aperture (SA). In automatic ground surveillance the function of search and detection of moving ground targets is performed by means of the conventional mechanical scan mode. The rotated antenna structure designed as a small array with two or more RX antenna elements with simultaneous receiver chains allows to instantaneous track multiple moving targets (monopulse principle). The simultaneously operated SAR mode yields areal images of the distribution of stationary scatterers. For ground surveillance application this SAR mode is best suited for identifying possible threats by means of change detection. The feasibility of this concept was tested by means of an experimental radar system comprising of a 94 GHz (W band) FM-CW module with 1 GHz bandwidth and two RX antennas with parallel receiver channels, placed off-axis at a rotating platform. SAR mode and search/track mode were tested during an outdoor measurement campaign. The scenery of two persons walking along a road and partially through forest served as test for the capability to track multiple moving targets. For SAR mode verification an image of the area composed of roads, grassland, woodland and several man-made objects was reconstructed from the measured data.

Scattering Mechanism Extraction by a Modified Cloude-Pottier Decomposition for Dual Polarization SAR

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Kefeng Ji

2015-06-01

Full Text Available Dual polarization is a typical operational mode of polarimetric synthetic aperture radar (SAR. However, few studies have considered the scattering mechanism extraction of dual-polarization SARs. A modified Cloude-Pottier decomposition is proposed to investigate the performance of the scattering mechanism extraction of dual-polarization SARs. It is theoretically demonstrated that only HH-VV SAR can discriminate the three canonical scattering mechanisms from an isotropic surface, horizontal dipole, and isotropic dihedral. Various experiments are conducted using 21 scenes from real datasets acquired by AIRSAR, Convair-580 SAR, EMISAR, E-SAR, Pi-SAR, and RADARSAT-2. Division of the dual-polarization H-α plane is experimentally obtained. The lack of cross-polarization induces the diffusion of scattering mechanisms and their overlap in the HH-VV H-α plane. However, the performance of HH-VV SAR for extracting scattering mechanisms is acceptable. Thus, HH-VV SAR is a suitable alternative to full-polarization SAR in certain cases. Meanwhile, the extraction performance of the other two dual-polarization SARs is badly degraded due to the lack of co-polarization. Therefore, HH-HV and HV-VV SARs cannot effectively extract the scattering mechanisms in the H-α plane.

Satellite SAR imagery for site discovery, change detection and monitoring activities in cultural heritage sites: experiments on the Nasca region, Peru

Science.gov (United States)

Tapete, D.; Cigna, F.; Masini, N.; Lasaponara, R.

2012-04-01

Besides their suitability for multi-temporal and spatial deformation analysis, the Synthetic Aperture Radar (SAR) image archives acquired by space-borne radar sensors can be exploited to support archaeological investigations over huge sites, even those partially or totally buried and still to be excavated. Amplitude information is one of the main properties of SAR data from which it is possible to retrieve evidences of buried structures, using feature extraction and texture analysis. Multi-temporality allows the reconstruction of past and recent evolution of both landscape and built-up environment, with the possibility to detect natural and/or anthropogenic changes, including human-induced damages to the conservation of cultural heritage. We present the methodology and first results of the experiments currently undertaken using SAR data in the Nasca region (Southern Peru), where two important civilizations such as Paracas and Nasca developed and flourished from 4th century BC to the 6th century AD. The study areas include a wide spectrum of archaeological and environmental elements to be preserved, among which: the archaeological site of Cahuachi and its surroundings, considered the largest adobe Ceremonial Centre in the World; the Nasca lines and geoglyphs in the areas of Palpa, Atarco and Nasca; the ancient networks of aqueducts and drainage galleries in the Puquios area, built by Nasca in the 1st-6th centuries AD. Archaeological prospection and multi-purpose remote sensing activities are currently carried out in the framework of the Italian mission of heritage Conservation and Archaeogeophysics (ITACA), with the direct involvement of researchers from the Institute for Archaeological and Monumental Heritage and the Institute of Methodologies for Environmental Analysis, Italian National Research Council. In this context, C- and L-band SAR images covering the Nasca region since 2001 were identified for the purposes of this research and, in particular, the following

Wake-based ship route estimation in high-resolution SAR images

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Graziano, M. Daniela; Rufino, Giancarlo; D'Errico, Marco

2014-10-01

This paper presents a novel algorithm for wake detection in Synthetic Aperture Radar images of the sea. The algorithm has been conceived as part of a ship traffic monitoring system, in charge of ship detection validation and to estimate ship route features, such as heading and ground speed. In addition, it has been intended to be adequate for inclusion in an automatic procedure without human operator supervision. The algorithm exploits the Radon transform to identify the images ship wake on the basis of the well known theoretical characteristics of the wakes' geometry and components, that are the turbulent wake, the narrow-V wakes, and the Kelvin arms, as well as the typical appearance of such components in Synthetic Aperture Radar images of the sea as bright or dark linear feature. Examples of application to high-resolution X-band Synthetic Aperture Radar products (COSMOSkymed and TerraSAR-X) are reported, both for wake detection and ship route estimation, showing the achieved quality and reliability of wake detection, adequacy to automatic procedures, as well as speed measure accuracy.

Complex surface deformation of Akutan volcano, Alaska revealed from InSAR time series

Science.gov (United States)

Wang, Teng; DeGrandpre, Kimberly; Lu, Zhong; Freymueller, Jeffrey T.

2018-02-01

Akutan volcano is one of the most active volcanoes in the Aleutian arc. An intense swarm of volcano-tectonic earthquakes occurred across the island in 1996. Surface deformation after the 1996 earthquake sequence has been studied using Interferometric Synthetic Aperture Radar (InSAR), yet it is hard to determine the detailed temporal behavior and spatial extent of the deformation due to decorrelation and the sparse temporal sampling of SAR data. Atmospheric delay anomalies over Akutan volcano are also strong, bringing additional technical challenges. Here we present a time series InSAR analysis from 2003 to 2016 to reveal the surface deformation in more detail. Four tracks of Envisat data acquired from 2003 to 2010 and one track of TerraSAR-X data acquired from 2010 to 2016 are processed to produce high-resolution surface deformation, with a focus on studying two transient episodes of inflation in 2008 and 2014. For the TerraSAR-X data, the atmospheric delay is estimated and removed using the common-master stacking method. These derived deformation maps show a consistently uplifting area on the northeastern flank of the volcano. From the TerraSAR-X data, we quantify the velocity of the subsidence inside the caldera to be as high as 10 mm/year, and identify another subsidence area near the ground cracks created during the 1996 swarm.

A SAR Observation and Numerical Study on Ocean Surface Imprints of Atmospheric Vortex Streets

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William G. Pichel

2008-05-01

Full Text Available The sea surface imprints of Atmospheric Vortex Street (AVS off Aleutian Volcanic Islands, Alaska were observed in two RADARSAT-1 Synthetic Aperture Radar (SAR images separated by about 11 hours. In both images, three pairs of distinctive vortices shedding in the lee side of two volcanic mountains can be clearly seen. The length and width of the vortex street are about 60-70 km and 20 km, respectively. Although the AVS’s in the two SAR images have similar shapes, the structure of vortices within the AVS is highly asymmetrical. The sea surface wind speed is estimated from the SAR images with wind direction input from Navy NOGAPS model. In this paper we present a complete MM5 model simulation of the observed AVS. The surface wind simulated from the MM5 model is in good agreement with SAR-derived wind. The vortex shedding rate calculated from the model run is about 1 hour and 50 minutes. Other basic characteristics of the AVS including propagation speed of the vortex, Strouhal and Reynolds numbers favorable for AVS generation are also derived. The wind associated with AVS modifies the cloud structure in the marine atmospheric boundary layer. The AVS cloud pattern is also observed on a MODIS visible band image taken between the two RADARSAT SAR images. An ENVISAT advance SAR image taken 4 hours after the second RADARSAT SAR image shows that the AVS has almost vanished.

Wind mapping offshore in coastal Mediterranean area using SAR images

DEFF Research Database (Denmark)

Calaudi, Rosamaria; Arena, Felice; Badger, Merete

Satellite observations of the ocean surface from Synthetic Aperture Radars (SAR) provide information about the spatial wind variability over large areas. This is of special interest in the Mediterranean, where spatial wind information is only provided by sparse buoys, often with long periods...... of missing data. Here, we focus on evaluating the use of SAR for offshore wind mapping. Preliminary results from the analysis of SAR-based ocean winds in Mediterranean areas show interesting large scale wind flow features consistent with results from previous studies using numerical models and space borne...

Combined Acquisition Technique (CAT) for Neuroimaging of Multiple Sclerosis at Low Specific Absorption Rates (SAR)

Science.gov (United States)

Biller, Armin; Choli, Morwan; Blaimer, Martin; Breuer, Felix A.; Jakob, Peter M.; Bartsch, Andreas J.

2014-01-01

Purpose To compare a novel combined acquisition technique (CAT) of turbo-spin-echo (TSE) and echo-planar-imaging (EPI) with conventional TSE. CAT reduces the electromagnetic energy load transmitted for spin excitation. This radiofrequency (RF) burden is limited by the specific absorption rate (SAR) for patient safety. SAR limits restrict high-field MRI applications, in particular. Material and Methods The study was approved by the local Medical Ethics Committee. Written informed consent was obtained from all participants. T2- and PD-weighted brain images of n = 40 Multiple Sclerosis (MS) patients were acquired by CAT and TSE at 3 Tesla. Lesions were recorded by two blinded, board-certificated neuroradiologists. Diagnostic equivalence of CAT and TSE to detect MS lesions was evaluated along with their SAR, sound pressure level (SPL) and sensations of acoustic noise, heating, vibration and peripheral nerve stimulation. Results Every MS lesion revealed on TSE was detected by CAT according to both raters (Cohen’s kappa of within-rater/across-CAT/TSE lesion detection κCAT = 1.00, at an inter-rater lesion detection agreement of κLES = 0.82). CAT reduced the SAR burden significantly compared to TSE (pCAT were 29.0 (±5.7) % for the T2-contrast and 32.7 (±21.9) % for the PD-contrast (expressed as percentages of the effective SAR limit of 3.2 W/kg for head examinations). Average SPL of CAT was no louder than during TSE. Sensations of CAT- vs. TSE-induced heating, noise and scanning vibrations did not differ. Conclusion T2−/PD-CAT is diagnostically equivalent to TSE for MS lesion detection yet substantially reduces the RF exposure. Such SAR reduction facilitates high-field MRI applications at 3 Tesla or above and corresponding protocol standardizations but CAT can also be used to scan faster, at higher resolution or with more slices. According to our data, CAT is no more uncomfortable than TSE scanning. PMID:24608106

Skipping the real world: Classification of PolSAR images without explicit feature extraction

Science.gov (United States)

Hänsch, Ronny; Hellwich, Olaf

2018-06-01

The typical processing chain for pixel-wise classification from PolSAR images starts with an optional preprocessing step (e.g. speckle reduction), continues with extracting features projecting the complex-valued data into the real domain (e.g. by polarimetric decompositions) which are then used as input for a machine-learning based classifier, and ends in an optional postprocessing (e.g. label smoothing). The extracted features are usually hand-crafted as well as preselected and represent (a somewhat arbitrary) projection from the complex to the real domain in order to fit the requirements of standard machine-learning approaches such as Support Vector Machines or Artificial Neural Networks. This paper proposes to adapt the internal node tests of Random Forests to work directly on the complex-valued PolSAR data, which makes any explicit feature extraction obsolete. This approach leads to a classification framework with a significantly decreased computation time and memory footprint since no image features have to be computed and stored beforehand. The experimental results on one fully-polarimetric and one dual-polarimetric dataset show that, despite the simpler approach, accuracy can be maintained (decreased by only less than 2 % for the fully-polarimetric dataset) or even improved (increased by roughly 9 % for the dual-polarimetric dataset).

Two-step single slope/SAR ADC with error correction for CMOS image sensor.

Science.gov (United States)

Tang, Fang; Bermak, Amine; Amira, Abbes; Amor Benammar, Mohieddine; He, Debiao; Zhao, Xiaojin

2014-01-01

Conventional two-step ADC for CMOS image sensor requires full resolution noise performance in the first stage single slope ADC, leading to high power consumption and large chip area. This paper presents an 11-bit two-step single slope/successive approximation register (SAR) ADC scheme for CMOS image sensor applications. The first stage single slope ADC generates a 3-bit data and 1 redundant bit. The redundant bit is combined with the following 8-bit SAR ADC output code using a proposed error correction algorithm. Instead of requiring full resolution noise performance, the first stage single slope circuit of the proposed ADC can tolerate up to 3.125% quantization noise. With the proposed error correction mechanism, the power consumption and chip area of the single slope ADC are significantly reduced. The prototype ADC is fabricated using 0.18 μ m CMOS technology. The chip area of the proposed ADC is 7 μ m × 500 μ m. The measurement results show that the energy efficiency figure-of-merit (FOM) of the proposed ADC core is only 125 pJ/sample under 1.4 V power supply and the chip area efficiency is 84 k  μ m(2) · cycles/sample.

Two-Step Single Slope/SAR ADC with Error Correction for CMOS Image Sensor

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Fang Tang

2014-01-01

Full Text Available Conventional two-step ADC for CMOS image sensor requires full resolution noise performance in the first stage single slope ADC, leading to high power consumption and large chip area. This paper presents an 11-bit two-step single slope/successive approximation register (SAR ADC scheme for CMOS image sensor applications. The first stage single slope ADC generates a 3-bit data and 1 redundant bit. The redundant bit is combined with the following 8-bit SAR ADC output code using a proposed error correction algorithm. Instead of requiring full resolution noise performance, the first stage single slope circuit of the proposed ADC can tolerate up to 3.125% quantization noise. With the proposed error correction mechanism, the power consumption and chip area of the single slope ADC are significantly reduced. The prototype ADC is fabricated using 0.18 μm CMOS technology. The chip area of the proposed ADC is 7 μm × 500 μm. The measurement results show that the energy efficiency figure-of-merit (FOM of the proposed ADC core is only 125 pJ/sample under 1.4 V power supply and the chip area efficiency is 84 k μm2·cycles/sample.

Saharasar: An Interactive SAR Image Database for Desert Mapping

Science.gov (United States)

Lopez, S.; Paillou, Ph.

2017-06-01

We present a dedicated tool for accessing radar images acquired by the ALOS/PALSAR mission over Sahara and Arabia. We developed a dedicated web site, using the Mapserver web mapping server and the Cesium javascript library.

Estimation of Melt Pond Fractions on First Year Sea Ice Using Compact Polarization SAR

Science.gov (United States)

Li, Haiyan; Perrie, William; Li, Qun; Hou, Yijun

2017-10-01

Melt ponds are a common feature on Arctic sea ice. They are linked to the sea ice surface albedo and transmittance of energy to the ocean from the atmosphere and thus constitute an important process to parameterize in Arctic climate models and simulations. This paper presents a first attempt to retrieve the melt pond fraction from hybrid-polarized compact polarization (CP) SAR imagery, which has wider swath and shorter revisit time than the quad-polarization systems, e.g., from RADARSAT-2 (RS-2). The co-polarization (co-pol) ratio has been verified to provide estimates of melt pond fractions. However, it is a challenge to link CP parameters and the co-pol ratio. The theoretical possibility is presented, for making this linkage with the CP parameter C22/C11 (the ratio between the elements of the coherence matrix of CP SAR) for melt pond detection and monitoring with the tilted-Bragg scattering model for the ocean surface. The empirical transformed formulation, denoted as the "compact polarization and quad-pol" ("CPQP") model, is proposed, based on 2062 RS-2 quad-pol SAR images, collocated with in situ measurements. We compared the retrieved melt pond fraction with CP parameters simulated from quad-pol SAR data with results retrieved from the co-pol ratio from quad-pol SAR observations acquired during the Arctic-Ice (Arctic-Ice Covered Ecosystem in a Rapidly Changing Environment) field project. The results are shown to be comparable for observed melt pond measurements in spatial and temporal distributions. Thus, the utility of CP mode SAR for melt pond fraction estimation on first year level ice is presented.

URBAN MODELLING PERFORMANCE OF NEXT GENERATION SAR MISSIONS

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U. G. Sefercik

2017-09-01

Full Text Available In synthetic aperture radar (SAR technology, urban mapping and modelling have become possible with revolutionary missions TerraSAR-X (TSX and Cosmo-SkyMed (CSK since 2007. These satellites offer 1m spatial resolution in high-resolution spotlight imaging mode and capable for high quality digital surface model (DSM acquisition for urban areas utilizing interferometric SAR (InSAR technology. With the advantage of independent generation from seasonal weather conditions, TSX and CSK DSMs are much in demand by scientific users. The performance of SAR DSMs is influenced by the distortions such as layover, foreshortening, shadow and double-bounce depend up on imaging geometry. In this study, the potential of DSMs derived from convenient 1m high-resolution spotlight (HS InSAR pairs of CSK and TSX is validated by model-to-model absolute and relative accuracy estimations in an urban area. For the verification, an airborne laser scanning (ALS DSM of the study area was used as the reference model. Results demonstrated that TSX and CSK urban DSMs are compatible in open, built-up and forest land forms with the absolute accuracy of 8–10 m. The relative accuracies based on the coherence of neighbouring pixels are superior to absolute accuracies both for CSK and TSX.

Multi-temporal InSAR monitoring of landslides in a tropical urban environment: focus on Bukavu (DR Congo)

Science.gov (United States)

Nobile, Adriano; Monsieurs, Elise; Dewitte, Olivier; d'Oreyes, Nicolas; Kervyn, Francois

2016-04-01

The western branch of the East African Rift System, in Central Africa, is characterized by the presence of several geohazards: earthquakes, volcanoes, and landslides. Every year, landslides cause fatalities, structural and functional damage to infrastructure and private properties with serious disruptions of the organization of societies and severe impact on the populations. These impacts are particularly important in the city of Bukavu (DR Congo) located within the Rift, on the southern shore of Lake Kivu. Large slow-moving landslides continuously affect highly populated slopes in the city. However little is known about their actual kinematics and the processes at play. Here we use multi-temporal InSAR technique to monitor these ground deformations. Using 50 Cosmo-SkyMed SAR images, acquired between March - October 2015 with a revisiting time of 8 days (ascending and descending orbits), we produce displacement-rate maps and ground deformation time series using the PS technique. Movements with a velocity >5cm/yr are detected, which is consistent with field observations. DGPS measurements, taken at 21 benchmarks in the area during the same period, allow validating the results. Similar ground deformation rates are found for the period 2002-2008 using Envisat ASAR images. Furthermore, comparison with rainfall monitoring data acquire on site should help us to understand the influence of water and the tropical seasonality in the slide mechanisms.

The Research on Denoising of SAR Image Based on Improved K-SVD Algorithm

Science.gov (United States)

Tan, Linglong; Li, Changkai; Wang, Yueqin

2018-04-01

SAR images often receive noise interference in the process of acquisition and transmission, which can greatly reduce the quality of images and cause great difficulties for image processing. The existing complete DCT dictionary algorithm is fast in processing speed, but its denoising effect is poor. In this paper, the problem of poor denoising, proposed K-SVD (K-means and singular value decomposition) algorithm is applied to the image noise suppression. Firstly, the sparse dictionary structure is introduced in detail. The dictionary has a compact representation and can effectively train the image signal. Then, the sparse dictionary is trained by K-SVD algorithm according to the sparse representation of the dictionary. The algorithm has more advantages in high dimensional data processing. Experimental results show that the proposed algorithm can remove the speckle noise more effectively than the complete DCT dictionary and retain the edge details better.

SYNERGY OF OPTICAL AND SAR DATA FOR MAPPING AND MONITORING MANGROVES

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A. K. Monzon

2016-06-01

Full Text Available Quantitative information on mangrove cover extents is essential in producing relevant resource management plans and conservation strategies. In the Philippines, mangrove rehabilitation was made a priority in relation to disaster risk response and mitigation following the calamities in the coastal communities during typhoon Haiyan/Yolanda; hence, baseline information on the extent of remaining mangrove cover was essential for effective site interventions. Although mangrove cover maps for the country already exists, analysis of mangrove cover changes were limited to the application of fixed annual deforestation rates due to the challenge of acquiring consistent temporal cloud-free optical satellite data over large landscapes. This study presents an initial analysis of SAR and optical imagery combined with field-based observations for detecting mangrove cover extent and changes through a straightforward graphical approach. The analysis is part of a larger study evaluating the synergistic use of time-series L-band SAR and optical data for mapping and monitoring of mangroves. Image segmentation was implemented on the 25-meter ALOS/PALSAR image mosaics, in which the generated objects were subjected to statistical analysis using the software R. In combination with selected Landsat bands, the class statistics from the image bands were used to generate decision trees and thresholds for the hierarchical image classification. The results were compared with global mangrove cover dataset and validated using collected ground truth data. This study developed an integrated replicable approach for analyzing future radar and optical datasets, essential in national level mangrove cover change monitoring and assessment for long-term conservation targets and strategies.

Combined Use of Multi-Temporal Optical and Radar Satellite Images for Grassland Monitoring

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Pauline Dusseux

2014-06-01

Full Text Available The aim of this study was to assess the ability of optical images, SAR (Synthetic Aperture Radar images and the combination of both types of data to discriminate between grasslands and crops in agricultural areas where cloud cover is very high most of the time, which restricts the use of visible and near-infrared satellite data. We compared the performances of variables extracted from four optical and five SAR satellite images with high/very high spatial resolutions acquired during the growing season. A vegetation index, namely the NDVI (Normalized Difference Vegetation Index, and two biophysical variables, the LAI (Leaf Area Index and the fCOVER (fraction of Vegetation Cover were computed using optical time series and polarization (HH, VV, HV, VH. The polarization ratio and polarimetric decomposition (Freeman–Durden and Cloude–Pottier were calculated using SAR time series. Then, variables derived from optical, SAR and both types of remotely-sensed data were successively classified using the Support Vector Machine (SVM technique. The results show that the classification accuracy of SAR variables is higher than those using optical data (0.98 compared to 0.81. They also highlight that the combination of optical and SAR time series data is of prime interest to discriminate grasslands from crops, allowing an improved classification accuracy.

Space Radar Image of Bebedauro, Brazil, seasonal

Science.gov (United States)

1994-01-01

This is an X-band image showing seasonal changes at the hydrological test site of Bebedouro in Brazil. The image is centered at 9 degrees south latitude and 40.2 degrees west longitude. This image was acquired by the Spaceborne Imaging Radar-C and X-band Synthetic Aperture Radar (SIR-C/X-SAR) aboard the space shuttle Endeavour on April 10, 1994, during the first flight of the radar system, and on October 1, 1994, during the second mission. The swath width is approximately 16.5 kilometers (10.5 miles) wide. The image channels have the following color assignments: red represents data acquired on April 10; green represents data acquired on October 1; blue corresponds to the ratio of the two data sets. Agriculture plays an important economic and social role in Brazil. One of the major problems related to Brazilian agriculture is estimating the size of planting areas and their productivity. Due to cloud cover and the rainy season, which occurs from November through April, optical and infrared Earth observations are seldom used to survey the region. An additional goal of monitoring this region is to watch the floodplains of rivers like Rio Sao Francisco in order to determine suitable locations for additional agricultural fields. This area belongs to the semi-arid northeastern region of Brazil, where estimates have suggested that about 10 times more land could be used for agriculture, including some locations which could be used for irrigation projects. Monitoring of soil moisture during the important summer crop season is of high priority for the future development and productivity of this region. In April the area was covered with vegetation because of the moisture of the soil and only small differences could be seen in X-band data. In October the run-off channels of this hilly region stand out quite clearly because the greenish areas indicated much less soil moisture and water content in plants. Spaceborne Imaging Radar-C and X-band Synthetic Aperture Radar (SIR-C/X-SAR

Methods of evaluating the effects of coding on SAR data

Science.gov (United States)

Dutkiewicz, Melanie; Cumming, Ian

1993-01-01

It is recognized that mean square error (MSE) is not a sufficient criterion for determining the acceptability of an image reconstructed from data that has been compressed and decompressed using an encoding algorithm. In the case of Synthetic Aperture Radar (SAR) data, it is also deemed to be insufficient to display the reconstructed image (and perhaps error image) alongside the original and make a (subjective) judgment as to the quality of the reconstructed data. In this paper we suggest a number of additional evaluation criteria which we feel should be included as evaluation metrics in SAR data encoding experiments. These criteria have been specifically chosen to provide a means of ensuring that the important information in the SAR data is preserved. The paper also presents the results of an investigation into the effects of coding on SAR data fidelity when the coding is applied in (1) the signal data domain, and (2) the image domain. An analysis of the results highlights the shortcomings of the MSE criterion, and shows which of the suggested additional criterion have been found to be most important.

MAPPING THE EXTENT AND MAGNITUDE OF SEVER FLOODING INDUCED BY HURRICANE IRMA WITH MULTI-TEMPORAL SENTINEL-1 SAR AND INSAR OBSERVATIONS

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B. Zhang

2018-04-01

Full Text Available During Hurricane Irma’s passage over Florida in September 2017, many sections of the state experienced heavy rain and sequent flooding. In order to drain water out of potential flooding zones and assess property damage, it is important to map the extent and magnitude of the flooded areas at various stages of the storm. We use Synthetic Aperture Radar (SAR and Interferometric SAR (InSAR observations, acquired by Sentinel-1 before, during and after the hurricane passage, which enable us to evaluate surface condition during different stages of the hurricane. This study uses multi-temporal images acquired under dry condition before the hurricane to constrain the background backscattering signature. Flooded areas are detected when the backscattering during the hurricane is statistically significantly different from the average dry conditions. The detected changes can be either an increase or decrease of the backscattering, which depends on the scattering characteristics of the surface. In addition, water level change information in Palmdale, South Florida is extracted from an interferogram with the aid of a local water gauge as the reference. The results of our flooding analysis revealed that the majority of the study area in South Florida was flooded during Hurricane Irma.

Space Radar Image of Kilauea Volcano, Hawaii

Science.gov (United States)

1994-01-01

This is a deformation map of the south flank of Kilauea volcano on the big island of Hawaii, centered at 19.5 degrees north latitude and 155.25 degrees west longitude. The map was created by combining interferometric radar data -- that is data acquired on different passes of the space shuttle which are then overlayed to obtain elevation information -- acquired by the Spaceborne Imaging Radar-C/X-band Synthetic Aperture Radar during its first flight in April 1994 and its second flight in October 1994. The area shown is approximately 40 kilometers by 80 kilometers (25 miles by 50 miles). North is toward the upper left of the image. The colors indicate the displacement of the surface in the direction that the radar instrument was pointed (toward the right of the image) in the six months between images. The analysis of ground movement is preliminary, but appears consistent with the motions detected by the Global Positioning System ground receivers that have been used over the past five years. The south flank of the Kilauea volcano is among the most rapidly deforming terrains on Earth. Several regions show motions over the six-month time period. Most obvious is at the base of Hilina Pali, where 10 centimeters (4 inches) or more of crustal deformation can be seen in a concentrated area near the coastline. On a more localized scale, the currently active Pu'u O'o summit also shows about 10 centimeters (4 inches) of change near the vent area. Finally, there are indications of additional movement along the upper southwest rift zone, just below the Kilauea caldera in the image. Deformation of the south flank is believed to be the result of movements along faults deep beneath the surface of the volcano, as well as injections of magma, or molten rock, into the volcano's 'plumbing' system. Detection of ground motions from space has proven to be a unique capability of imaging radar technology. Scientists hope to use deformation data acquired by SIR-C/X-SAR and future imaging

Emergency product generation for disaster management using RISAT and DMSAR quick look SAR processors

Science.gov (United States)

Desai, Nilesh; Sharma, Ritesh; Kumar, Saravana; Misra, Tapan; Gujraty, Virendra; Rana, SurinderSingh

2006-12-01

Since last few years, ISRO has embarked upon the development of two complex Synthetic Aperture Radar (SAR) missions, viz. Spaceborne Radar Imaging Satellite (RISAT) and Airborne SAR for Disaster Mangement (DMSAR), as a capacity building measure under country's Disaster Management Support (DMS) Program, for estimating the extent of damage over large areas (~75 Km) and also assess the effectiveness of the relief measures undertaken during natural disasters such as cyclones, epidemics, earthquakes, floods and landslides, forest fires, crop diseases etc. Synthetic Aperture Radar (SAR) has an unique role to play in mapping and monitoring of large areas affected by natural disasters especially floods, owing to its unique capability to see through clouds as well as all-weather imaging capability. The generation of SAR images with quick turn around time is very essential to meet the above DMS objectives. Thus the development of SAR Processors, for these two SAR systems poses considerable challenges and design efforts. Considering the growing user demand and inevitable necessity for a full-fledged high throughput processor, to process SAR data and generate image in real or near-real time, the design and development of a generic SAR Processor has been taken up and evolved, which will meet the SAR processing requirements for both Airborne and Spaceborne SAR systems. This hardware SAR processor is being built, to the extent possible, using only Commercial-Off-The-Shelf (COTS) DSP and other hardware plug-in modules on a Compact PCI (cPCI) platform. Thus, the major thrust has been on working out Multi-processor Digital Signal Processor (DSP) architecture and algorithm development and optimization rather than hardware design and fabrication. For DMSAR, this generic SAR Processor operates as a Quick Look SAR Processor (QLP) on-board the aircraft to produce real time full swath DMSAR images and as a ground based Near-Real Time high precision full swath Processor (NRTP). It will

The design of long wavelength planetary SAR sensor and its applications for monitoring shallow sub-surface of Moon and planets.

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Kim, K.

2015-12-01

SAR observations over planetary surface have been conducted mainly in two ways. The first is the subsurface sounding, for example Mars Advanced Radar for Subsurface and Ionosphere Sounding (MARSIS) and Shallow Surface Radar (SHARAD), using ground penetration capability of long wavelength electromagnetic waves. On the other hand, imaging SAR sensors using burst mode design have been employed to acquire surface observations in the presence of opaque atmospheres such as in the case of Venus and Titan. We propose a lightweight SAR imaging system with P/L band wavelength to cover the vertical observation gap of these planetary radar observation schemes. The sensor is for investigating prominent surface and near-subsurface geological structures and physical characteristics. Such measurements will support landers and rover missions as well as future manned missions. We evaluate required power consumption, and estimate mass and horizontal resolution, which can be as good as 3-7 meters. Initial specifications for P/L dual band SARs for the lunar case at 130 km orbital altitude were designed already based on a assumptions that sufficient size antenna (>3m width diameter or width about 3m and >10kg weight) can be equipped. Useful science measurements to be obtained include: (1) derivation of subsurface regolith depth; 2) Surface and shallow subsurface radar imaging, together with radar ranging techniques such as radargrammetry and inteferometry. The concepts in this study can be used as an important technical basis for the future solid plant/satellite missions and already proposed for the 2018 Korean Lunar mission.

Performance Analysis for Airborne Interferometric SAR Affected by Flexible Baseline Oscillation

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Liu Zhong-sheng

2014-04-01

Full Text Available The airborne interferometric SAR platform suffers from instability factors, such as air turbulence and mechanical vibrations during flight. Such factors cause the oscillation of the flexible baseline, which leads to significant degradation of the performance of the interferometric SAR system. This study is concerned with the baseline oscillation. First, the error of the slant range model under baseline oscillation conditions is formulated. Then, the SAR complex image signal and dual-channel correlation coefficient are modeled based on the first-order, second-order, and generic slant range error. Subsequently, the impact of the baseline oscillation on the imaging and interferometric performance of the SAR system is analyzed. Finally, simulations of the echo data are used to validate the theoretical analysis of the baseline oscillation in the airborne interferometric SAR.

Mono- and Digalactosyldiacylglycerol Lipids Function Nonredundantly to Regulate Systemic Acquired Resistance in Plants

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Qing-ming Gao

2014-12-01

Full Text Available Summary: The plant galactolipids monogalactosyldiacylglycerol (MGDG and digalactosyldiacylglycerol (DGDG have been linked to the anti-inflammatory and cancer benefits of a green leafy vegetable diet in humans due to their ability to regulate the levels of free radicals like nitric oxide (NO. Here, we show that DGDG contributes to plant NO as well as salicylic acid biosynthesis and is required for the induction of systemic acquired resistance (SAR. In contrast, MGDG regulates the biosynthesis of the SAR signals azelaic acid (AzA and glycerol-3-phosphate (G3P that function downstream of NO. Interestingly, DGDG is also required for AzA-induced SAR, but MGDG is not. Notably, transgenic expression of a bacterial glucosyltransferase is unable to restore SAR in dgd1 plants even though it does rescue their morphological and fatty acid phenotypes. These results suggest that MGDG and DGDG are required at distinct steps and function exclusively in their individual roles during the induction of SAR. : The galactolipids monogalactosyldiacylglycerol (MGDG and digalactosyldiacylglycerol (DGDG constitute ∼80% of total membrane lipids in plants. Gao et al. now show that these galactolipids function nonredundantly to regulate systemic acquired resistance (SAR. Furthermore, they show that the terminal galactose on the α-galactose-β-galactose head group of DGDG is critical for SAR.

Image Registration Methode in Radar Interferometry

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S. Chelbi

2015-08-01

Full Text Available This article presents a methodology for the determination of the registration of an Interferometric Synthetic radar (InSAR pair images with half pixel precision. Using the two superposed radar images Single Look complexes (SLC [1-4], we developed an iterative process to superpose these two images according to their correlation coefficient with a high coherence area. This work concerns the exploitation of ERS Tandem pair of radar images SLC of the Algiers area acquired on 03 January and 04 January 1994. The former is taken as a master image and the latter as a slave image.

Basin Scale Assessment of Landslides Geomorphological Setting by Advanced InSAR Analysis

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Francesca Bozzano

2017-03-01

Full Text Available An extensive investigation of more than 90 landslides affecting a small river basin in Central Italy was performed by combining field surveys and remote sensing techniques. We thus defined the geomorphological setting of slope instability processes. Basic information, such as landslides mapping and landslides type definition, have been acquired thanks to geomorphological field investigations and multi-temporal aerial photos interpretation, while satellite SAR archive data (acquired by ERS and Envisat from 1992 to 2010 have been analyzed by means of A-DInSAR (Advanced Differential Interferometric Synthetic Aperture Radar techniques to evaluate landslides past displacements patterns. Multi-temporal assessment of landslides state of activity has been performed basing on geomorphological evidence criteria and past ground displacement measurements obtained by A-DInSAR. This step has been performed by means of an activity matrix derived from information achieved thanks to double orbital geometry. Thanks to this approach we also achieved more detailed knowledge about the landslides kinematics in time and space.

Calibrating a hydraulic model using water levels derived from time series high-resolution Radarsat-2 synthetic aperture radar images and elevation data

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Trudel, M.; Desrochers, N.; Leconte, R.

2017-12-01

Knowledge of water extent (WE) and level (WL) of rivers is necessary to calibrate and validate hydraulic models and thus to better simulate and forecast floods. Synthetic aperture radar (SAR) has demonstrated its potential for delineating water bodies, as backscattering of water is much lower than that of other natural surfaces. The ability of SAR to obtain information despite cloud cover makes it an interesting tool for temporal monitoring of water bodies. The delineation of WE combined with a high-resolution digital terrain model (DTM) allows extracting WL. However, most research using SAR data to calibrate hydraulic models has been carried out using one or two images. The objectives of this study is to use WL derived from time series high resolution Radarsat-2 SAR images for the calibration of a 1-D hydraulic model (HEC-RAS). Twenty high-resolution (5 m) Radarsat-2 images were acquired over a 40 km reach of the Athabasca River, in northern Alberta, Canada, between 2012 and 2016, covering both low and high flow regimes. A high-resolution (2m) DTM was generated combining information from LIDAR data and bathymetry acquired between 2008 and 2016 by boat surveying. The HEC-RAS model was implemented on the Athabasca River to simulate WL using cross-sections spaced by 100 m. An image histogram thresholding method was applied on each Radarsat-2 image to derive WE. WE were then compared against each cross-section to identify those were the slope of the banks is not too abrupt and therefore amenable to extract WL. 139 observations of WL at different locations along the river reach and with streamflow measurements were used to calibrate the HEC-RAS model. The RMSE between SAR-derived and simulated WL is under 0.35 m. Validation was performed using in situ observations of WL measured in 2008, 2012 and 2016. The RMSE between the simulated water levels calibrated with SAR images and in situ observations is less than 0.20 m. In addition, a critical success index (CSI) was

Detection of macroalgae blooms by complex SAR imagery

International Nuclear Information System (INIS)

Shen, Hui; Perrie, William; Liu, Qingrong; He, Yijun

2014-01-01

Highlights: • Complex SAR imagery enables better recognition of macroalgae patches. • Combination of different information in SAR matrix forms new index factors. • Proposed index factors contribute to unsupervised recognition of macroalgae. -- Abstract: Increased frequency and enhanced damage to the marine environment and to human society caused by green macroalgae blooms demand improved high-resolution early detection methods. Conventional satellite remote sensing methods via spectra radiometers do not work in cloud-covered areas, and therefore cannot meet these demands for operational applications. We present a methodology for green macroalgae bloom detection based on RADARSAT-2 synthetic aperture radar (SAR) images. Green macroalgae patches exhibit different polarimetric characteristics compared to the open ocean surface, in both the amplitude and phase domains of SAR-measured complex radar backscatter returns. In this study, new index factors are defined which have opposite signs in green macroalgae-covered areas, compared to the open water surface. These index factors enable unsupervised detection from SAR images, providing a high-resolution new tool for detection of green macroalgae blooms, which can potentially contribute to a better understanding of the mechanisms related to outbreaks of green macroalgae blooms in coastal areas throughout the world ocean

Imaging of acquired non-traumatic cochlear lesions: iconographic essay

International Nuclear Information System (INIS)

Garcia, Marcelo de Mattos; Gonzaga, Juliana Gontijo

2006-01-01

Different non-traumatic acquired cochlear lesions are shown in this article with imaging methods. They may be responsible for neuro sensorial hearing loss or vertigo. The method of choice is computed tomography when evaluating the osseous labyrinth whereas magnetic resonance imaging has superior resolution in the studies of the membranaceous labyrinth. (author)

3D Monitoring of Buildings Using TerraSAR-X InSAR, DInSAR and PolSAR Capacities

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Flora Weissgerber

2017-09-01

Full Text Available The rapid expansion of cities increases the need of urban remote sensing for a large scale monitoring. This paper provides greater understanding of how TerraSAR-X (TSX high-resolution abilities enable to reach the spatial precision required to monitor individual buildings, through the use of a 4 year temporal stack of 100 images over Paris (France. Three different SAR modes are investigated for this purpose. First a method involving a whole time-series is proposed to measure realistic heights of buildings. Then, we show that the small wavelength of TSX makes the interferometric products very sensitive to the ordinary building-deformation, and that daily deformation can be measured over the entire building with a centimetric accuracy, and without any a priori on the deformation evolution, even when neglecting the impact of the atmosphere. Deformations up to 4 cm were estimated for the Eiffel Tower and up to 1 cm for other lower buildings. These deformations were analyzed and validated with weather and in situ local data. Finally, four TSX polarimetric images were used to investigate geometric and dielectric properties of buildings under the deterministic framework. Despite of the resolution loss of this mode, the possibility to estimate the structural elements of a building orientations and their relative complexity in the spatial organization are demonstrated.

Clustering of Multi-Temporal Fully Polarimetric L-Band SAR Data for Agricultural Land Cover Mapping

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Tamiminia, H.; Homayouni, S.; Safari, A.

2015-12-01

Recently, the unique capabilities of Polarimetric Synthetic Aperture Radar (PolSAR) sensors make them an important and efficient tool for natural resources and environmental applications, such as land cover and crop classification. The aim of this paper is to classify multi-temporal full polarimetric SAR data using kernel-based fuzzy C-means clustering method, over an agricultural region. This method starts with transforming input data into the higher dimensional space using kernel functions and then clustering them in the feature space. Feature space, due to its inherent properties, has the ability to take in account the nonlinear and complex nature of polarimetric data. Several SAR polarimetric features extracted using target decomposition algorithms. Features from Cloude-Pottier, Freeman-Durden and Yamaguchi algorithms used as inputs for the clustering. This method was applied to multi-temporal UAVSAR L-band images acquired over an agricultural area near Winnipeg, Canada, during June and July in 2012. The results demonstrate the efficiency of this approach with respect to the classical methods. In addition, using multi-temporal data in the clustering process helped to investigate the phenological cycle of plants and significantly improved the performance of agricultural land cover mapping.

Space-Borne and Ground-Based InSAR Data Integration: The Åknes Test Site

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Federica Bardi

2016-03-01

Full Text Available This work concerns a proposal of the integration of InSAR (Interferometric Synthetic Aperture Radar data acquired by ground-based (GB and satellite platforms. The selected test site is the Åknes rockslide, which affects the western Norwegian coast. The availability of GB-InSAR and satellite InSAR data and the accessibility of a wide literature make the landslide suitable for testing the proposed procedure. The first step consists of the organization of a geodatabase, performed in the GIS environment, containing all of the available data. The second step concerns the analysis of satellite and GB-InSAR data, separately. Two datasets, acquired by RADARSAT-2 (related to a period between October 2008 and August 2013 and by a combination of TerraSAR-X and TanDEM-X (acquired between July 2010 and October 2012, both of them in ascending orbit, processed applying SBAS (Small BAseline Subset method, are available. GB-InSAR data related to five different campaigns of measurements, referred to the summer seasons of 2006, 2008, 2009, 2010 and 2012, are available, as well. The third step relies on data integration, performed firstly from a qualitative point of view and later from a semi-quantitative point of view. The results of the proposed procedure have been validated by comparing them to GPS (Global Positioning System data. The proposed procedure allowed us to better define landslide sectors in terms of different ranges of displacements. From a qualitative point of view, stable and unstable areas have been distinguished. In the sector concerning movement, two different sectors have been defined thanks to the results of the semi-quantitative integration step: the first sector, concerning displacement values higher than 10 mm, and the 2nd sector, where the displacements did not exceed a 10-mm value of displacement in the analyzed period.

Characterizing and estimating noise in InSAR and InSAR time series with MODIS

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Barnhart, William D.; Lohman, Rowena B.

2013-01-01

InSAR time series analysis is increasingly used to image subcentimeter displacement rates of the ground surface. The precision of InSAR observations is often affected by several noise sources, including spatially correlated noise from the turbulent atmosphere. Under ideal scenarios, InSAR time series techniques can substantially mitigate these effects; however, in practice the temporal distribution of InSAR acquisitions over much of the world exhibit seasonal biases, long temporal gaps, and insufficient acquisitions to confidently obtain the precisions desired for tectonic research. Here, we introduce a technique for constraining the magnitude of errors expected from atmospheric phase delays on the ground displacement rates inferred from an InSAR time series using independent observations of precipitable water vapor from MODIS. We implement a Monte Carlo error estimation technique based on multiple (100+) MODIS-based time series that sample date ranges close to the acquisitions times of the available SAR imagery. This stochastic approach allows evaluation of the significance of signals present in the final time series product, in particular their correlation with topography and seasonality. We find that topographically correlated noise in individual interferograms is not spatially stationary, even over short-spatial scales (<10 km). Overall, MODIS-inferred displacements and velocities exhibit errors of similar magnitude to the variability within an InSAR time series. We examine the MODIS-based confidence bounds in regions with a range of inferred displacement rates, and find we are capable of resolving velocities as low as 1.5 mm/yr with uncertainties increasing to ∼6 mm/yr in regions with higher topographic relief.

Early appearance of SARS on chest CT scan

International Nuclear Information System (INIS)

Cheng Xiaoguang; Feng Suchen; Xia Guoguang; Zhao Tao; Gu Xiang; Qu Hui

2003-01-01

Objective: To evaluate the early appearance of SARS on chest CT scan and its role in the early diagnosis. Methods: Forty cases of SARS in keeping with the criteria of the Ministry of Health had chest CT scans within 7 days of onset of symptoms, and CR chest X-ray films were available as well. These chest X-rays and CT images were retrospectively reviewed to determine if there were any abnormalities on the images. The lesions on the chest CT images were then further analyzed in terms of the number, location, size, and density. Results: Positive abnormalities on chest CT scans were revealed in all 40 SARS cases. Positive findings on CR chest films were showed in only 25 cases, equivocal in 6, and normal in 9 cases. The main abnormalities seen on CT and X-rays were pulmonary infiltrations varied markedly in severity. 70 % cases had 1 or 2 lesions on chest CT scan, 30 % cases had 3 or more lesions. The lesions seen on chest CT scan tended to be ground-glass opacification, sometimes with consolidation which was very faint and inhomogeneous, easily missed on chest X-rays. Typically the lesions were located in the periphery of the lung, or both central and peripheral lung, but very rare in a pure central location. They were commonly in the shape of patch or ball. Conclusions: Chest CT scan is much more sensitive in detecting the lesions of the lung in SARS. The early appearance of SARS on chest CT scan is characteristic but non-specific, indicating that chest CT scan plays a very important role in the early diagnosis and differential diagnosis of SARS

Structural classification of marshes with Polarimetric SAR highlighting the temporal mapping of marshes exposed to oil

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Ramsey, Elijah W.; Rangoonwala, Amina; Jones, Cathleen E.

2015-01-01

Empirical relationships between field-derived Leaf Area Index (LAI) and Leaf Angle Distribution (LAD) and polarimetric synthetic aperture radar (PolSAR) based biophysical indicators were created and applied to map S. alterniflora marsh canopy structure. PolSAR and field data were collected near concurrently in the summers of 2010, 2011, and 2012 in coastal marshes, and PolSAR data alone were acquired in 2009. Regression analyses showed that LAI correspondence with the PolSAR biophysical indicator variables equaled or exceeded those of vegetation water content (VWC) correspondences. In the final six regressor model, the ratio HV/VV explained 49% of the total 77% explained LAI variance, and the HH-VV coherence and phase information accounted for the remainder. HV/HH dominated the two regressor LAD relationship, and spatial heterogeneity and backscatter mechanism followed by coherence information dominated the final three regressor model that explained 74% of the LAD variance. Regression results applied to 2009 through 2012 PolSAR images showed substantial changes in marsh LAI and LAD. Although the direct cause was not substantiated, following a release of freshwater in response to the 2010 Deepwater Horizon oil spill, the fairly uniform interior marsh structure of 2009 was more vertical and dense shortly after the oil spill cessation. After 2010, marsh structure generally progressed back toward the 2009 uniformity; however, the trend was more disjointed in oil impact marshes.             

Evaluation of Polarimetric SAR Decomposition for Classifying Wetland Vegetation Types

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Sang-Hoon Hong

2015-07-01

Full Text Available The Florida Everglades is the largest subtropical wetland system in the United States and, as with subtropical and tropical wetlands elsewhere, has been threatened by severe environmental stresses. It is very important to monitor such wetlands to inform management on the status of these fragile ecosystems. This study aims to examine the applicability of TerraSAR-X quadruple polarimetric (quad-pol synthetic aperture radar (PolSAR data for classifying wetland vegetation in the Everglades. We processed quad-pol data using the Hong & Wdowinski four-component decomposition, which accounts for double bounce scattering in the cross-polarization signal. The calculated decomposition images consist of four scattering mechanisms (single, co- and cross-pol double, and volume scattering. We applied an object-oriented image analysis approach to classify vegetation types with the decomposition results. We also used a high-resolution multispectral optical RapidEye image to compare statistics and classification results with Synthetic Aperture Radar (SAR observations. The calculated classification accuracy was higher than 85%, suggesting that the TerraSAR-X quad-pol SAR signal had a high potential for distinguishing different vegetation types. Scattering components from SAR acquisition were particularly advantageous for classifying mangroves along tidal channels. We conclude that the typical scattering behaviors from model-based decomposition are useful for discriminating among different wetland vegetation types.

On the classification of mixed floating pollutants on the Yellow Sea of China by using a quad-polarized SAR image

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Wang, Xiaochen; Shao, Yun; Tian, Wei; Li, Kun

2018-06-01

This study explored different methodologies using a C-band RADARSAT-2 quad-polarized Synthetic Aperture Radar (SAR) image located over China's Yellow Sea to investigate polarization decomposition parameters for identifying mixed floating pollutants from a complex ocean background. It was found that solitary polarization decomposition did not meet the demand for detecting and classifying multiple floating pollutants, even after applying a polarized SAR image. Furthermore, considering that Yamaguchi decomposition is sensitive to vegetation and the algal variety Enteromorpha prolifera, while H/A/alpha decomposition is sensitive to oil spills, a combination of parameters which was deduced from these two decompositions was proposed for marine environmental monitoring of mixed floating sea surface pollutants. A combination of volume scattering, surface scattering, and scattering entropy was the best indicator for classifying mixed floating pollutants from a complex ocean background. The Kappa coefficients for Enteromorpha prolifera and oil spills were 0.7514 and 0.8470, respectively, evidence that the composite polarized parameters based on quad-polarized SAR imagery proposed in this research is an effective monitoring method for complex marine pollution.

Unsupervised SBAS-DInSAR Processing of Space-borne SAR data for Earth Surface Displacement Time Series Generation

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Casu, F.; de Luca, C.; Lanari, R.; Manunta, M.; Zinno, I.

2016-12-01

During the last 25 years, the Differential Synthetic Aperture Radar Interferometry (DInSAR) has played an important role for understanding the Earth's surface deformation and its dynamics. In particular, the large collections of SAR data acquired by a number of space-borne missions (ERS, ENVISAT, ALOS, RADARSAT, TerraSAR-X, COSMO-SkyMed) have pushed toward the development of advanced DInSAR techniques for monitoring the temporal evolution of the ground displacements with an high spatial density. Moreover, the advent of the Copernicus Sentinel-1 (S1) constellation is providing a further increase in the SAR data flow available to the Earth science community, due to its characteristics of global coverage strategy and free and open access data policy. Therefore, managing and storing such a huge amount of data, processing it in an effcient way and maximizing the available archives exploitation are becoming high priority issues. In this work we present some recent advances in the DInSAR field for dealing with the effective exploitation of the present and future SAR data archives. In particular, an efficient parallel SBAS implementation (namely P-SBAS) that takes benefit from high performance computing is proposed. Then, the P-SBAS migration to the emerging Cloud Computing paradigm is shown, together with extensive tests carried out in the Amazon's Elastic Cloud Compute (EC2) infrastructure. Finally, the integration of the P-SBAS processing chain within the ESA Geohazards Exploitation Platform (GEP), for setting up operational on-demand and systematic web tools, open to every user, aimed at automatically processing stacks of SAR data for the generation of SBAS displacement time series, is also illustrated. A number of experimental results obtained by using the ERS, ENVISAT and S1 data in areas characterized by volcanic, seismic and anthropogenic phenomena will be shown. This work is partially supported by: the DPC-CNR agreement, the EPOS-IP project and the ESA GEP project.

Using SAR images to delineate ocean oil slicks with a texture-classifying neural network algorithm (TCNNA)

Energy Technology Data Exchange (ETDEWEB)

Garcia-Pineda, O.; MacDonald, I.R. [Florida State Univ., Tallahassee, FL (United States). Dept. of Oceanography; Zimmer, B. [Texas A and M Univ., Corpus Christi, TX (United States). Dept. of Mathematics and Statistics; Howard, M. [Texas A and M Univ., College Station, TX (United States). Dept. of Oceanography; Pichel, W. [National Oceanic and Atmospheric Administration, Camp Springs, MD (United States). Center for Satellite Applications and Research, National Environmental Satellite, Data and Information Service; Li, X. [National Oceanic and Atmospheric Administration, Camp Springs, MD (United States). Systems Group, National Environmental Satellite, Data and Information

2009-10-15

Synthetic aperture radar (SAR) is used to detect surfactant layers produced by floating oil on the ocean surface. This study presented details of a texture-classifying neural network algorithm (TCNNA) designed to process SAR data from a wide selection of beam modes. Patterns from SAR imagery were extracted in a semi-supervised procedure using a combination of edge-detection filters; texture descriptors; collection information; and environmental data. Various natural oil seeps in the Gulf of Mexico were used as case studies. An analysis of the case studies demonstrated that the TCNNA was able to extract targets and rapidly interpret images collected under a range of environmental conditions. Results presented by the TCNNA were used to evaluate the effects of different environmental conditions on the expressions of oil slicks detected by the data. Optimal incidence angle ranges and wind speed ranges for surfactant film detection were also presented. Results obtained by the TCNNA can be stored and manipulated in geographic information system (GIS) data layers. 26 refs., 1 tab., 7 figs.

Space Radar Image of Chernobyl

Science.gov (United States)

1994-01-01

This is an image of the Chernobyl nuclear power plant and its surroundings, centered at 51.17 north latitude and 30.15 west longitude. The image was acquired by the Spaceborne Imaging Radar-C and X-band Synthetic Aperture Radar aboard the space shuttle Endeavour on its 16th orbit on October 1, 1994. The area is located on the northern border of the Ukraine Republic and was produced by using the L-band (horizontally transmitted and received) polarization. The differences in the intensity are due to differences in vegetation cover, with brighter areas being indicative of more vegetation. These data were acquired as part of a collaboration between NASA and the National Space Agency of Ukraine in Remote Sensing and Earth Sciences. NASA has included several sites provided by the Ukrainian space agency as targets of opportunity during the second flight of SIR-C/X-SAR. The Ukrainian space agency also plans to conduct airborne surveys of these sites during the mission. The Chernobyl nuclear power plant is located toward the top of the image near the Pripyat River. The 12-kilometer (7.44-mile)-long cooling pond is easily distinguishable as an elongated dark shape in the center near the top of the image. The reactor complex is visible as the bright area to the extreme left of the cooling pond and the city of Chernobyl is the bright area just below the cooling pond next to the Pripyat River. The large dark area in the bottom right of the image is the Kiev Reservoir just north of Kiev. Also visible is the Dnieper River, which feeds into the Kiev Reservoir from the top of the image. The Soviet government evacuated 116,000 people within 30 kilometers (18.6 miles) of the Chernobyl reactor after the explosion and fire on April 26, 1986. Spaceborne Imaging Radar-C and X-band Synthetic Aperture Radar (SIR-C/X-SAR) is part of NASA's Mission to Planet Earth. The radars illuminate Earth with microwaves, allowing detailed observations at any time, regardless of weather or sunlight

Tracking morphological changes and slope instability using spaceborne and ground-based SAR data

Science.gov (United States)

Di Traglia, Federico; Nolesini, Teresa; Ciampalini, Andrea; Solari, Lorenzo; Frodella, William; Bellotti, Fernando; Fumagalli, Alfio; De Rosa, Giuseppe; Casagli, Nicola

2018-01-01

delta formation after the 2007 eruption and the lava overflows of 2010 and 2011. After the emplacement of the 2014 lava field, high displacements in the central and northern portions of the SdF were recorded by the GBInSAR device, whereas the spaceborne data were unable to detect these rapid movements. A comparison between IRT images and GBInSAR-derived displacement maps acquired during the same time interval revealed that the observed displacements along the SdF were related to the crumbling of newly emplaced 2014 lava and of its external breccia. Detected slope instability after the 2014 flank eruption was related to lava accumulation on the SdF and to the difference in the material underlying the 2014 lava flow: i) lava flows and breccia layers related to the 2002-03 and 2007 lava flow fields in the northern SdF sector and ii) loose volcaniclastic deposits in the central part of the SdF. This work emphasizes the importance of smart integration of spaceborne, SAR-derived hazard information with permanent-sited, operational monitoring by GBInSAR devices to detect areas impacted by mass wasting and volcanic activity.

Early season monitoring of corn and soybeans with TerraSAR-X and RADARSAT-2

Science.gov (United States)

McNairn, H.; Kross, A.; Lapen, D.; Caves, R.; Shang, J.

2014-05-01

Early and on-going crop production forecasts are important to facilitate food price stability for regions at risk, and for agriculture exporters, to set market value. Most regional and global efforts in forecasting rely on multiple sources of information from the field. With increased access to data from spaceborne Synthetic Aperture Radar (SAR), these sensors could contribute information on crop acreage. But these acreage estimates must be available early in the season to assist with production forecasts. This study acquired TerraSAR-X and RADARSAT-2 data over a region in eastern Canada dominated by economically important corn and soybean production. Using a supervised decision tree classifier, results determined that either sensor was capable of delivering highly accurate maps of corn and soybeans at the end of the growing season. Accuracies far exceeded 90%. Spatial and multi-temporal filtering approaches were compared and small improvements in accuracies were found by applying the multi-temporal filter to the RADARSAT-2 data. Of significant interest, this study determined that by using only three TerraSAR-X images corn could be accurately identified by the end of June, a mere six weeks after planting and at a vegetative growth stage (V6 - sixth leaf collar developed). However, soybeans required additional acquisitions given the variance in planting densities and planting dates in this region of Canada. In this case, accurate soybean classification required TerraSAR-X images until early August at the start of the reproductive stage (R5 - seed development is beginning). Also important, by applying a multi-temporal filter accurate mapping (close to 90%) of corn and soybeans from RADARSAT-2 could occur five weeks earlier (by August 19) than if a spatial filter was used. Thus application of this filtering approach could accelerate delivery of a crop inventory for this region of Canada. Corn and soybeans are important commodities both globally and within Canada. This

Analysis of the Effect of Radio Frequency Interference on Repeat Track Airborne InSAR System

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Ding Bin

2012-03-01

Full Text Available The SAR system operating at low frequency is susceptible to Radio Frequency Interference (RFI from television station, radio station, and some other civil electronic facilities. The presence of RFI degrades the SAR image quality, and obscures the targets in the scene. Furthermore, RFI can cause interferometric phase error in repeat track InSAR system. In order to analyze the effect of RFI on interferometric phase of InSAR, real measured RFI signal are added on cone simulated SAR echoes. The imaging and interferometric processing results of both the RFI-contaminated and raw data are given. The effect of real measured RFI signal on repeat track InSAR system is analyzed. Finally, the imaging and interferometric processing results of both with and without RFI suppressed of the P band airborne repeat track InSAR real data are presented, which demonstrates the efficiency of the RFI suppression method in terms of decreasing the interferometric phase errors caused by RFI.

Monoterpenes Support Systemic Acquired Resistance within and between Plants.

Science.gov (United States)

Riedlmeier, Marlies; Ghirardo, Andrea; Wenig, Marion; Knappe, Claudia; Koch, Kerstin; Georgii, Elisabeth; Dey, Sanjukta; Parker, Jane E; Schnitzler, Jörg-Peter; Vlot, A Corina

2017-06-01

This study investigates the role of volatile organic compounds in systemic acquired resistance (SAR), a salicylic acid (SA)-associated, broad-spectrum immune response in systemic, healthy tissues of locally infected plants. Gas chromatography coupled to mass spectrometry analyses of SAR-related emissions of wild-type and non-SAR-signal-producing mutant plants associated SAR with monoterpene emissions. Headspace exposure of Arabidopsis thaliana to a mixture of the bicyclic monoterpenes α-pinene and β-pinene induced defense, accumulation of reactive oxygen species, and expression of SA- and SAR-related genes, including the SAR regulatory AZELAIC ACID INDUCED1 ( AZI1 ) gene and three of its paralogs. Pinene-induced resistance was dependent on SA biosynthesis and signaling and on AZI1 Arabidopsis geranylgeranyl reductase1 mutants with reduced monoterpene biosynthesis were SAR-defective but mounted normal local resistance and methyl salicylate-induced defense responses, suggesting that monoterpenes act in parallel with SA The volatile emissions from SAR signal-emitting plants induced defense in neighboring plants, and this was associated with the presence of α-pinene, β-pinene, and camphene in the emissions of the "sender" plants. Our data suggest that monoterpenes, particularly pinenes, promote SAR, acting through ROS and AZI1 , and likely function as infochemicals in plant-to-plant signaling, thus allowing defense signal propagation between neighboring plants. © 2017 American Society of Plant Biologists. All rights reserved.

Retrieval of the ocean wave spectrum in open and thin ice covered ocean waters from ERS Synthetic Aperture Radar images

International Nuclear Information System (INIS)

De Carolis, G.

2001-01-01

This paper concerns with the task of retrieving ocean wave spectra form imagery provided by space-borne SAR systems such as that on board ERS satellite. SAR imagery of surface wave fields travelling into open ocean and into thin sea ice covers composed of frazil and pancake icefields is considered. The major purpose is to gain insight on how the spectral changes can be related to sea ice properties of geophysical interest such as the thickness. Starting from SAR image cross spectra computed from Single Look Complex (SLC) SAR images, the ocean wave spectrum is retrieved using an inversion procedure based on the gradient descent algorithm. The capability of this method when applied to satellite SAR sensors is investigated. Interest in the SAR image cross spectrum exploitation is twofold: first, the directional properties of the ocean wave spectra are retained; second, external wave information needed to initialize the inversion procedure may be greatly reduced using only information included in the SAR image cross spectrum itself. The main drawback is that the wind waves spectrum could be partly lost and its spectral peak wave number underestimated. An ERS-SAR SLC image acquired on April 10, 1993 over the Greenland Sea was selected as test image. A pair of windows that include open-sea only and sea ice cover, respectively, were selected. The inversions were carried out using different guess wave spectra taken from SAR image cross spectra. Moreover, care was taken to properly handle negative values eventually occurring during the inversion runs. This results in a modification of the gradient descending the technique that is required if a non-negative solution of the wave spectrum is searched for. Results are discussed in view of the possibility of SAR data to detect ocean wave dispersion as a means for the retrieval of ice thickness

Simulation-Based Evaluation of Light Posts and Street Signs as 3-D Geolocation Targets in SAR Images

Science.gov (United States)

Auer, S.; Balss, U.

2017-05-01

The assignment of phase center positions (in 2D or 3D) derived from SAR data to physical object is challenging for many man-made structures such as buildings or bridges. In contrast, light poles and traffic signs are promising targets for tasks based on 3-D geolocation as they often show a prominent and spatially isolated appearance. For a detailed understanding of the nature of both targets, this paper presents results of a dedicated simulation case study, which is based on ray tracing methods (simulator RaySAR). For the first time, the appearance of the targets is analyzed in 2D (image plane) and 3D space (world coordinates of scene model) and reflecting surfaces are identified for related dominant image pixels. The case studies confirms the crucial impact of spatial resolution in the context of light poles and traffic signs and the appropriateness of light poles as target for 3-D geolocation in case of horizontal ground surfaces beneath.

SIMULATION-BASED EVALUATION OF LIGHT POSTS AND STREET SIGNS AS 3-D GEOLOCATION TARGETS IN SAR IMAGES

Directory of Open Access Journals (Sweden)

S. Auer

2017-05-01

Full Text Available The assignment of phase center positions (in 2D or 3D derived from SAR data to physical object is challenging for many man-made structures such as buildings or bridges. In contrast, light poles and traffic signs are promising targets for tasks based on 3-D geolocation as they often show a prominent and spatially isolated appearance. For a detailed understanding of the nature of both targets, this paper presents results of a dedicated simulation case study, which is based on ray tracing methods (simulator RaySAR. For the first time, the appearance of the targets is analyzed in 2D (image plane and 3D space (world coordinates of scene model and reflecting surfaces are identified for related dominant image pixels. The case studies confirms the crucial impact of spatial resolution in the context of light poles and traffic signs and the appropriateness of light poles as target for 3-D geolocation in case of horizontal ground surfaces beneath.

NEAR REAL-TIME AUTOMATIC MARINE VESSEL DETECTION ON OPTICAL SATELLITE IMAGES

Directory of Open Access Journals (Sweden)

G. Máttyus

2013-05-01

Full Text Available Vessel monitoring and surveillance is important for maritime safety and security, environment protection and border control. Ship monitoring systems based on Synthetic-aperture Radar (SAR satellite images are operational. On SAR images the ships made of metal with sharp edges appear as bright dots and edges, therefore they can be well distinguished from the water. Since the radar is independent from the sun light and can acquire images also by cloudy weather and rain, it provides a reliable service. Vessel detection from spaceborne optical images (VDSOI can extend the SAR based systems by providing more frequent revisit times and overcoming some drawbacks of the SAR images (e.g. lower spatial resolution, difficult human interpretation. Optical satellite images (OSI can have a higher spatial resolution thus enabling the detection of smaller vessels and enhancing the vessel type classification. The human interpretation of an optical image is also easier than as of SAR image. In this paper I present a rapid automatic vessel detection method which uses pattern recognition methods, originally developed in the computer vision field. In the first step I train a binary classifier from image samples of vessels and background. The classifier uses simple features which can be calculated very fast. For the detection the classifier is slided along the image in various directions and scales. The detector has a cascade structure which rejects most of the background in the early stages which leads to faster execution. The detections are grouped together to avoid multiple detections. Finally the position, size(i.e. length and width and heading of the vessels is extracted from the contours of the vessel. The presented method is parallelized, thus it runs fast (in minutes for 16000 × 16000 pixels image on a multicore computer, enabling near real-time applications, e.g. one hour from image acquisition to end user.

Near Real-Time Automatic Marine Vessel Detection on Optical Satellite Images

Science.gov (United States)

Máttyus, G.

2013-05-01

Vessel monitoring and surveillance is important for maritime safety and security, environment protection and border control. Ship monitoring systems based on Synthetic-aperture Radar (SAR) satellite images are operational. On SAR images the ships made of metal with sharp edges appear as bright dots and edges, therefore they can be well distinguished from the water. Since the radar is independent from the sun light and can acquire images also by cloudy weather and rain, it provides a reliable service. Vessel detection from spaceborne optical images (VDSOI) can extend the SAR based systems by providing more frequent revisit times and overcoming some drawbacks of the SAR images (e.g. lower spatial resolution, difficult human interpretation). Optical satellite images (OSI) can have a higher spatial resolution thus enabling the detection of smaller vessels and enhancing the vessel type classification. The human interpretation of an optical image is also easier than as of SAR image. In this paper I present a rapid automatic vessel detection method which uses pattern recognition methods, originally developed in the computer vision field. In the first step I train a binary classifier from image samples of vessels and background. The classifier uses simple features which can be calculated very fast. For the detection the classifier is slided along the image in various directions and scales. The detector has a cascade structure which rejects most of the background in the early stages which leads to faster execution. The detections are grouped together to avoid multiple detections. Finally the position, size(i.e. length and width) and heading of the vessels is extracted from the contours of the vessel. The presented method is parallelized, thus it runs fast (in minutes for 16000 × 16000 pixels image) on a multicore computer, enabling near real-time applications, e.g. one hour from image acquisition to end user.

Monitoring of Oil Exploitation Infrastructure by Combining Unsupervised Pixel-Based Classification of Polarimetric SAR and Object-Based Image Analysis

Directory of Open Access Journals (Sweden)

Simon Plank

2014-12-01

Full Text Available In developing countries, there is a high correlation between the dependence of oil exports and violent conflicts. Furthermore, even in countries which experienced a peaceful development of their oil industry, land use and environmental issues occur. Therefore, independent monitoring of oil field infrastructure may support problem solving. Earth observation data enables fast monitoring of large areas which allows comparing the real amount of land used by the oil exploitation and the companies’ contractual obligations. The target feature of this monitoring is the infrastructure of the oil exploitation, oil well pads—rectangular features of bare land covering an area of approximately 50–60 m × 100 m. This article presents an automated feature extraction procedure based on the combination of a pixel-based unsupervised classification of polarimetric synthetic aperture radar data (PolSAR and an object-based post-classification. The method is developed and tested using dual-polarimetric TerraSAR-X imagery acquired over the Doba basin in south Chad. The advantages of PolSAR are independence of the cloud coverage (vs. optical imagery and the possibility of detailed land use classification (vs. single-pol SAR. The PolSAR classification uses the polarimetric Wishart probability density function based on the anisotropy/entropy/alpha decomposition. The object-based post-classification refinement, based on properties of the feature targets such as shape and area, increases the user’s accuracy of the methodology by an order of a magnitude. The final achieved user’s and producer’s accuracy is 59%–71% in each case (area based accuracy assessment. Considering only the numbers of correctly/falsely detected oil well pads, the user’s and producer’s accuracies increase to even 74%–89%. In an iterative training procedure the best suited polarimetric speckle filter and processing parameters of the developed feature extraction procedure are

FUSION OF OPTICAL DATA AND SAR DATA FOR THE ESTIMATION OF NITROGEN CONCENTRATION IN PEARL RIVER ESTUARY HONG KONG SEAS, CHINA

Directory of Open Access Journals (Sweden)

X. Liu

2012-08-01

Full Text Available The knowledge of nitrogen concentration in the ocean is fundamental for the study of oceanic biogeochemical processes. The objective of this research is to estimate total inorganic nitrogen (TIN by integrating optical parameters from HJ-1 CCD image and polarization parameters from RADARSAT-2 quad-polarization image. The situ data and HJ-1 CCD, RADARSAT-2 image were acquired from Pearl River Estuary Hong Kong Seas, China in August, 2010. The four sensitive parameters, reflectance of Band 4, NDSI (Normalized Difference Spectral Index, the backscattering coefficient of HV and VH were derived as input variables to assess the TIN. A multiple regression model was established between four input variables and TIN. The result showed that the fusion of optical data and SAR data was proved to be successful in estimating TIN in sea surface, with the correlation coefficient (R2 between measured TIN and predicated TIN of 0.774, and the root mean square error (RMSE of 0.063. The optical data in combination with SAR data is promising for detecting biochemical component in sea surface.

The Advanced Rapid Imaging and Analysis (ARIA) Project: Providing Standard and On-Demand SAR products for Hazard Science and Hazard Response

Science.gov (United States)

Owen, S. E.; Hua, H.; Rosen, P. A.; Agram, P. S.; Webb, F.; Simons, M.; Yun, S. H.; Sacco, G. F.; Liu, Z.; Fielding, E. J.; Lundgren, P.; Moore, A. W.

2017-12-01

A new era of geodetic imaging arrived with the launch of the ESA Sentinel-1A/B satellites in 2014 and 2016, and with the 2016 confirmation of the NISAR mission, planned for launch in 2021. These missions assure high quality, freely and openly distributed regularly sampled SAR data into the indefinite future. These unprecedented data sets are a watershed for solid earth sciences as we progress towards the goal of ubiquitous InSAR measurements. We now face the challenge of how to best address the massive volumes of data and intensive processing requirements. Should scientists individually process the same data independently themselves? Should a centralized service provider create standard products that all can use? Are there other approaches to accelerate science that are cost effective and efficient? The Advanced Rapid Imaging and Analysis (ARIA) project, a joint venture co-sponsored by California Institute of Technology (Caltech) and by NASA through the Jet Propulsion Laboratory (JPL), is focused on rapidly generating higher level geodetic imaging products and placing them in the hands of the solid earth science and local, national, and international natural hazard communities by providing science product generation, exploration, and delivery capabilities at an operational level. However, there are challenges in defining the optimal InSAR data products for the solid earth science community. In this presentation, we will present our experience with InSAR users, our lessons learned the advantages of on demand and standard products, and our proposal for the most effective path forward.

Integrating interferometric SAR data with levelling measurements of land subsidence using geostatistics

NARCIS (Netherlands)

Zhou, Y.; Stein, A.; Molenaar, M.

2003-01-01

Differential Synthetic Aperture Radar (SAR) interferometric (D-InSAR) data of ground surface deformation are affected by several error sources associated with image acquisitions and data processing. In this paper, we study the use of D-InSAR for quantifying land subsidence due to groundwater

Spaceborne Polarimetric SAR Interferometry: Performance Analysis and Mission Concepts

Directory of Open Access Journals (Sweden)

Shane R. Cloude

2005-12-01

Full Text Available We investigate multichannel imaging radar systems employing coherent combinations of polarimetry and interferometry (Pol-InSAR. Such systems are well suited for the extraction of bio- and geophysical parameters by evaluating the combined scattering from surfaces and volumes. This combination leads to several important differences between the design of Pol-InSAR sensors and conventional single polarisation SAR interferometers. We first highlight these differences and then investigate the Pol-InSAR performance of two proposed spaceborne SAR systems (ALOS/PalSAR and TerraSAR-L operating in repeat-pass mode. For this, we introduce the novel concept of a phase tube which enables (1 a quantitative assessment of the Pol-InSAR performance, (2 a comparison between different sensor configurations, and (3 an optimization of the instrument settings for different Pol-InSAR applications. The phase tube may hence serve as an interface between system engineers and application-oriented scientists. The performance analysis reveals major limitations for even moderate levels of temporal decorrelation. Such deteriorations may be avoided in single-pass sensor configurations and we demonstrate the potential benefits from the use of future bi- and multistatic SAR interferometers.

Chinese HJ-1C SAR And Its Wind Mapping Capability

Science.gov (United States)

Huang, Weigen; Chen, Fengfeng; Yang, Jingsong; Fu, Bin; Chen, Peng; Zhang, Chan

2010-04-01

Chinese Huan Jing (HJ)-1C synthetic aperture radar (SAR) satellite has been planed to be launched in 2010. HJ-1C satellite will fly in a sun-synchronous polar orbit of 500-km altitude. SAR will be the only sensor on board the satellite. It operates in S band with VV polarization. Its image mode has the incidence angles 25°and 47°at the near and far sides of the swath respectively. There are two selectable SAR modes of operation, which are fine resolution beams and standard beams respectively. The sea surface wind mapping capability of the SAR has been examined using M4S radar imaging model developed by Romeiser. The model is based on Bragg scattering theory in a composite surface model expansion. It accounts for contributions of the full ocean wave spectrum to the radar backscatter from ocean surface. The model reproduces absolute normalized radar cross section (NRCS) values for wide ranges of wind speeds. The model results of HJ-1C SAR have been compared with the model results of Envisat ASAR. It shows that HJ-1C SAR is as good as Envisat ASAR at sea surface wind mapping.

Polarimetric SAR interferometry applied to land ice: modeling

DEFF Research Database (Denmark)

Dall, Jørgen; Papathanassiou, Konstantinos; Skriver, Henning

2004-01-01

This paper introduces a few simple scattering models intended for the application of polarimetric SAR interfer-ometry to land ice. The principal aim is to eliminate the penetration bias hampering ice sheet elevation maps generated with single-channel SAR interferometry. The polarimetric coherent...... scattering models are similar to the oriented-volume model and the random-volume-over-ground model used in vegetation studies, but the ice models are adapted to the different geometry of land ice. Also, due to compaction, land ice is not uniform; a fact that must be taken into account for large penetration...... depths. The validity of the scattering models is examined using L-band polarimetric interferometric SAR data acquired with the EMISAR system over an ice cap located in the percolation zone of the Greenland ice sheet. Radar reflectors were deployed on the ice surface prior to the data acquisition in order...

Estimating soil moisture using the Danish polarimetric SAR

DEFF Research Database (Denmark)

Jiankang, Ji; Thomsen, A.; Skriver, Henning

1995-01-01

The results of applying data from the Danish polarimetric SAR (EMISAR) to estimate soil moisture for bare fields are presented. Fully calibrated C-band SAR images for hh, vv and cross polarizations have been used in this study. The measured surface roughness data showed that classical roughness a...... of surface parameters with the bilinear model, the correlation coefficient between the estimated and measured soil moisture, as well as rms height, is about 0.77. To improve the result, the local incidence angles need to be taken into account......The results of applying data from the Danish polarimetric SAR (EMISAR) to estimate soil moisture for bare fields are presented. Fully calibrated C-band SAR images for hh, vv and cross polarizations have been used in this study. The measured surface roughness data showed that classical roughness...... autocorrelation functions (Gaussian and Exponential) were not able to fit natural surfaces well. A Gauss-Exp hybrid model which agreed better with the measured data has been proposed. Theoretical surface scattering models (POM, IEM), as well as an empirical model for retrieval of soil moisture and surface rms...

Groundwater depletion in Central Mexico: Use of GRACE and InSAR to support water resources management

Science.gov (United States)

Castellazzi, Pascal; Martel, Richard; Rivera, Alfonso; Huang, Jianliang; Pavlic, Goran; Calderhead, Angus I.; Chaussard, Estelle; Garfias, Jaime; Salas, Javier

2016-08-01

Groundwater deficits occur in several areas of Central Mexico, where water resource assessment is limited by the availability and reliability of field data. In this context, GRACE and InSAR are used to remotely assess groundwater storage loss in one of Mexico's most important watersheds in terms of size and economic activity: the Lerma-Santiago-Pacifico (LSP). In situ data and Land Surface Models are used to subtract soil moisture and surface water storage changes from the total water storage change measured by GRACE satellites. As a result, groundwater mass change time-series are obtained for a 12 years period. ALOS-PALSAR images acquired from 2007 to 2011 were processed using the SBAS-InSAR algorithm to reveal areas subject to ground motion related to groundwater over-exploitation. In the perspective of providing guidance for groundwater management, GRACE and InSAR observations are compared with official water budgets and field observations. InSAR-derived subsidence mapping generally agrees well with official water budgets, and shows that deficits occur mainly in cities and irrigated agricultural areas. GRACE does not entirely detect the significant groundwater losses largely reported by official water budgets, literature and InSAR observations. The difference is interpreted as returns of wastewater to the groundwater flow systems, which limits the watershed scale groundwater depletion but suggests major impacts on groundwater quality. This phenomenon is enhanced by ground fracturing as noticed in the field. Studying the fate of the extracted groundwater is essential when comparing GRACE data with higher resolution observations, and particularly in the perspective of further InSAR/GRACE combination in hydrogeology.

基于MRF的多时相SAR影像非监督变化检测%Unsupervised Change Detection in Multitemporal SAR Images Using MRF Models

Institute of Scientific and Technical Information of China (English)

江利明; 廖明生; 张路; 林珲

2007-01-01

An unsupervised change-detection method that considers the spatial contextual information in a log-ratio difference image generated from multitemporal SAR images is proposed. A Markov random filed (MRF) model is particularly employed to exploit statistical spatial correlation of intensity levels among neighboring pixels. Under the assumption of the independency of pixels and mixed Gaussian distribution in the log-ratio difference image, a stochastic and iterative EM-MPM change-detection algorithm based on an MRF model is developed. The EM-MPM algorithm is based on a maximiser of posterior marginals (MPM) algorithm for image segmentation and an expectation-maximum (EM) algorithm for parameter estimation in a completely automatic way. The experiment results obtained on multitemporal ERS-2 SAR images show the effectiveness of the proposed method.

A High-precision Motion Compensation Method for SAR Based on Image Intensity Optimization

Directory of Open Access Journals (Sweden)

Hu Ke-bin

2015-02-01

Full Text Available Owing to the platform instability and precision limitations of motion sensors, motion errors negatively affect the quality of synthetic aperture radar (SAR images. The autofocus Back Projection (BP algorithm based on the optimization of image sharpness compensates for motion errors through phase error estimation. This method can attain relatively good performance, while assuming the same phase error for all pixels, i.e., it ignores the spatial variance of motion errors. To overcome this drawback, a high-precision motion error compensation method is presented in this study. In the proposed method, the Antenna Phase Centers (APC are estimated via optimization using the criterion of maximum image intensity. Then, the estimated APCs are applied for BP imaging. Because the APC estimation equals the range history estimation for each pixel, high-precision phase compensation for every pixel can be achieved. Point-target simulations and processing of experimental data validate the effectiveness of the proposed method.

Simulated multipolarized MAPSAR images to distinguish agricultural crops

Directory of Open Access Journals (Sweden)

Wagner Fernando Silva

2012-06-01

Full Text Available Many researchers have shown the potential of Synthetic Aperture Radar (SAR images for agricultural applications, particularly for monitoring regions with limitations in terms of acquiring cloud free optical images. Recently, Brazil and Germany began a feasibility study on the construction of an orbital L-band SAR sensor referred to as MAPSAR (Multi-Application Purpose SAR. This sensor provides L-band images in three spatial resolutions and polarimetric, interferometric and stereoscopic capabilities. Thus, studies are needed to evaluate the potential of future MAPSAR images. The objective of this study was to evaluate multipolarized MAPSAR images simulated by the airborne SAR-R99B sensor to distinguish coffee, cotton and pasture fields in Brazil. Discrimination among crops was evaluated through graphical and cluster analysis of mean backscatter values, considering single, dual and triple polarizations. Planting row direction of coffee influenced the backscatter and was divided into two classes: parallel and perpendicular to the sensor look direction. Single polarizations had poor ability to discriminate the crops. The overall accuracies were less than 59 %, but the understanding of the microwave interaction with the crops could be explored. Combinations of two polarizations could differentiate various fields of crops, highlighting the combination VV-HV that reached 78 % overall accuracy. The use of three polarizations resulted in 85.4 % overall accuracy, indicating that the classes pasture and parallel coffee were fully discriminated from the other classes. These results confirmed the potential of multipolarized MAPSAR images to distinguish the studied crops and showed considerable improvement in the accuracy of the results when the number of polarizations was increased.

Oil Spill detection off the eastern coast of India using Sentinel-1 dual polarimeteric SAR imagery

Science.gov (United States)

De, S.; Bhattacharya, A.; Gautam, R.

2017-12-01

Among the various Earth observing sensors, the spaceborne Polarimetric Synthetic Aperture Radar (PolSAR) is considered as one of the most flexible and has been widely used in disaster response applications due to its all-weather illumination independent capability. Sentinel-1 is a two-satellite constellation with a C-band polarimetric Synthetic Aperture Radar (PolSAR) sensor, which provides global coverage with a 12-day repeat cycle in the same acquisition geometry, and the possibility of a 3-day repeat imaging in independent geometry, making it ideal for operational geodynamic monitoring. The proposed study aims to detect changes in polarimetric parameters associated with an oil spill event occurred off the coast of Ennore, Tamil Nadu, India (13.228° N Lon: 80.363° E ) on 28 January 2017. The initial spill covered an area of approximately 7.26 sq. km, spreading to an area of 12.56 sq. km. in a single day. The spread was mainly attributed to the strong shore parallel southerly current. To this end, two PolSAR images were used from before and after the event acquired on 17 and 29 January 2017, respectively in dual-polarimetric (VV,VH) interferometric wide swath mode and with same acquisition geometry. The images are first calibrated, co-registered and terrain corrected to make them comparable in a geo-coordinate framework. A refined Lee speckle filter is applied with a 5x5 window to reduce the influence of coherent speckle. The pair of images are then used to generate a hellinger distance based change index corresponding to each polarimetric channel. The indices are then applied as input to a Convolutional Neural Network (CNN) with the objective of discriminating the areas corresponding to changes due to the oil spill, movement of ships, rough ocean surface etc. The final result is a binary change detection map of the oil spill area. The results obtained were compared with that obtained by survey of the affected oil spill area by the Integrated Coastal and Marine

(Non-) homomorphic approaches to denoise intensity SAR images with non-local means and stochastic distances

Science.gov (United States)

Penna, Pedro A. A.; Mascarenhas, Nelson D. A.

2018-02-01

The development of new methods to denoise images still attract researchers, who seek to combat the noise with the minimal loss of resolution and details, like edges and fine structures. Many algorithms have the goal to remove additive white Gaussian noise (AWGN). However, it is not the only type of noise which interferes in the analysis and interpretation of images. Therefore, it is extremely important to expand the filters capacity to different noise models present in li-terature, for example the multiplicative noise called speckle that is present in synthetic aperture radar (SAR) images. The state-of-the-art algorithms in remote sensing area work with similarity between patches. This paper aims to develop two approaches using the non local means (NLM), developed for AWGN. In our research, we expanded its capacity for intensity SAR ima-ges speckle. The first approach is grounded on the use of stochastic distances based on the G0 distribution without transforming the data to the logarithm domain, like homomorphic transformation. It takes into account the speckle and backscatter to estimate the parameters necessary to compute the stochastic distances on NLM. The second method uses a priori NLM denoising with a homomorphic transformation and applies the inverse Gamma distribution to estimate the parameters that were used into NLM with stochastic distances. The latter method also presents a new alternative to compute the parameters for the G0 distribution. Finally, this work compares and analyzes the synthetic and real results of the proposed methods with some recent filters of the literature.

Observation of pressure ridges in SAR images of sea ice: Scattering theory and comparison with observations

Science.gov (United States)

Vesecky, J. F.; Daida, J. M.; Shuchman, R. A.; Onstott, R. H.; Camiso, J. C.

1993-01-01

Ridges and keels (hummocks and bummocks) in sea ice flows are important in sea ice research for both scientific and practical reasons. Sea ice movement and deformation is driven by internal and external stresses on the ice. Ridges and keels play important roles in both cases because they determine the external wind and current stresses via drag coefficients. For example, the drag coefficient over sea ice can vary by a factor of several depending on the fluid mechanical roughness length of the surface. This roughness length is thought to be strongly dependent on the ridge structures present. Thus, variations in ridge and keel structure can cause gradients in external stresses which must be balanced by internal stresses and possibly fracture of the ice. Ridging in sea ice is also a sign of fracture. In a practical sense, large ridges form the biggest impediment to surface travel over the ice or penetration through sea ice by ice-strengthened ships. Ridges also play an important role in the damage caused by sea ice to off-shore structures. Hence, observation and measurement of sea ice ridges is an important component of sea ice remote sensing. The research reported here builds on previous work, estimating the characteristics of ridges and leads in sea ice from SAR images. Our objective is to develop methods for quantitative measurement of sea ice ridges from SAR images. To make further progress, in particular, to estimate ridge height, a scattering model for ridges is needed. Our research approach for a ridge scattering model begins with a survey of the geometrical properties of ridges and a comparison with the characteristics of the surrounding ice. For this purpose we have used airborne optical laser (AOL) data collected during the 1987 Greenland Sea Experiment. These data were used to generate a spatial wavenumber spectrum for height variance for a typical ridge - the typical ridge is the average over 10 large ridges. Our first-order model radar scattering includes

Clues on active differential uplift across the Giudicarie belt (Central-Eastern Alps, Italy) by means of PSInSAR data

Science.gov (United States)

Massironi, Matteo; Zampieri, Dario; Schiavo, Alessio; Bianchi, Marco; Franceschini, Andrea

2010-05-01

The Permanent Scatterers Synthetic Aperture Radar INterferometry (PSInSAR) methodology provides high resolution assessment of surface deformations (precision ranging from 0.8 to 0.1 mm/year) over long periods of observation. Hence, it is particularly suitable to analyze surface motion over wide regions associated to a weak tectonic activity. For this reason we have adopted the PSInSAR technique to study regional movement across the Giudicarie belt, a NNE-trending trust belt oblique to the Southern Alpine chain and presently characterized by a low to moderate seismicity. Over 11,000 PS velocities along the satellite Line Of Sight (LOS) were calculated using images acquired in descending orbit during the 1992-1996 time span. The PSInSAR data show a differential uplift of around 1.4-1.7 mm/year across the most external WNW-dipping thrusts of the Giudicarie belt (Mt. Baldo, Mt. Stivo and Mt. Grattacul thrusts alignment). This corresponds to a horizontal contraction across the external part of the Giudicarie belt of about 1.3-1.5 mm/year.

Mapping the Extent and Magnitude of Severe Flooding Induced by Hurricanes Harvey, Irma, and Maria with Sentinel-1 SAR and InSAR Observations

Science.gov (United States)

Zhang, B.; Koirala, R.; Oliver-Cabrera, T.; Wdowinski, S.; Osmanoglu, B.

2017-12-01

Hurricanes can cause winds, rainfall and storm surge, all of which could result in flooding. Between August and September 2017, Hurricanes Harvey, Irma and Maria made landfall over Texas, Florida and Puerto Rico causing destruction and damages. Flood mapping is important for water management and to estimate risks and property damage. Though water gauges are able to monitor water levels, they are normally distributed sparsely. To map flooding products of these extreme events, we use Synthetic Aperture Radar (SAR) observations acquired by the European satellite constellation Sentinel-1. We obtained two acquisitions from before each flooding event, a single acquisition during the hurricane, and two after each event, a total of five acquisitions. We use both amplitude and phase observations to map extent and magnitude of flooding respectively. To map flooding extents, we use amplitude images from before, after and if possible during the hurricane pass. A calibration is used to convert the image raw data to backscatter coefficient, termed sigma nought. We generate a composite of the two image layers using red and green bands to show the change of sigma nought between acquisitions, which directly reflects the extent of flooding. Because inundation can result with either an increase or decrease of sigma nought values depending on the surface scattering characteristics, we map flooded areas in location where sigma nought changes were above a detection threshold. To study magnitude of flooding we study Interferometric Synthetic Aperture Radar (InSAR) phase changes. Changes in the water level can be detected by the radar when the signal is reflected away from water surface and bounces again by another object (e.g. trees and/or buildings) known as double bounce phase. To generate meaningful interferograms, we compare phase information with the nearest water gauge records to verify our results. Preliminary results show that the three hurricanes caused flooding condition over

LANDSAT TM and SAR - ERS1 data for analysis of Vrancea seismic region

International Nuclear Information System (INIS)

Zoran, M.

2002-01-01

This paper is aimed to present the results of the application of LANDSAT TM and SAR- ERS1 satellite data for Vrancea seismic area investigation, in order to emphasize geomorphological features as well as to identify faulting zones responsible of seismic events generation. Remote sensing analysis and field studies of active faults can provide a geologic history that overcomes many of the shortcomings of instrumental and historic records. Vrancea - Focsani is structurally and seismically complex area, bounded by latitudes 45.6 angle N and 46.0 angle N and longitudes 26.5 angle E and 27.5 angle E. The Peceneaga -Camena Fault, a deep crustal fracture with dextral slip, is considered to be North-Eastern boundary of the Moesian Platform. The Eastern unit of the Moesian Sub-Plate is characterized by a series of principal faults with a North-Western orientation and by a secondary system of faults orientated NE-SW. NW trending crustal fractures are also evidenced East of the Peceneaga-Camena Fault, within our test area. A SAR- ERS1 image and a multispectral Landsat TM data set were used and processed with EASI/PACE image processing software package as well as with developed algorithms. In order to a better management all the information available on the study area, data acquired have been integrated in a unique database. This information consists of thematic maps from cartography, land use map from classification of remotely sensed data. This study revealed that satellite data used are excellent for recognizing the continuity and regional relationships of faults. Linear features in TM images appear shorter and denser distributed, whereas ERS1 images are dominated by the principal structures. In certain cases they complete the lineaments and lineament patterns derived from TM data. Higher spatial resolution satellite data and SAR interferometric data are needed for mapping of these features. Remote sensing techniques provide a means for locating, identifying and mapping

An Unsupervised Method of Change Detection in Multi-Temporal PolSAR Data Using a Test Statistic and an Improved K&I Algorithm

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Jinqi Zhao

2017-12-01

Full Text Available In recent years, multi-temporal imagery from spaceborne sensors has provided a fast and practical means for surveying and assessing changes in terrain surfaces. Owing to the all-weather imaging capability, polarimetric synthetic aperture radar (PolSAR has become a key tool for change detection. Change detection methods include both unsupervised and supervised methods. Supervised change detection, which needs some human intervention, is generally ineffective and impractical. Due to this limitation, unsupervised methods are widely used in change detection. The traditional unsupervised methods only use a part of the polarization information, and the required thresholding algorithms are independent of the multi-temporal data, which results in the change detection map being ineffective and inaccurate. To solve these problems, a novel method of change detection using a test statistic based on the likelihood ratio test and the improved Kittler and Illingworth (K&I minimum-error thresholding algorithm is introduced in this paper. The test statistic is used to generate the comparison image (CI of the multi-temporal PolSAR images, and improved K&I using a generalized Gaussian model simulates the distribution of the CI. As a result of these advantages, we can obtain the change detection map using an optimum threshold. The efficiency of the proposed method is demonstrated by the use of multi-temporal PolSAR images acquired by RADARSAT-2 over Wuhan, China. The experimental results show that the proposed method is effective and highly accurate.

WEIBULL MULTIPLICATIVE MODEL AND MACHINE LEARNING MODELS FOR FULL-AUTOMATIC DARK-SPOT DETECTION FROM SAR IMAGES

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A. Taravat

2013-09-01

Full Text Available As a major aspect of marine pollution, oil release into the sea has serious biological and environmental impacts. Among remote sensing systems (which is a tool that offers a non-destructive investigation method, synthetic aperture radar (SAR can provide valuable synoptic information about the position and size of the oil spill due to its wide area coverage and day/night, and all-weather capabilities. In this paper we present a new automated method for oil-spill monitoring. A new approach is based on the combination of Weibull Multiplicative Model and machine learning techniques to differentiate between dark spots and the background. First, the filter created based on Weibull Multiplicative Model is applied to each sub-image. Second, the sub-image is segmented by two different neural networks techniques (Pulsed Coupled Neural Networks and Multilayer Perceptron Neural Networks. As the last step, a very simple filtering process is used to eliminate the false targets. The proposed approaches were tested on 20 ENVISAT and ERS2 images which contained dark spots. The same parameters were used in all tests. For the overall dataset, the average accuracies of 94.05 % and 95.20 % were obtained for PCNN and MLP methods, respectively. The average computational time for dark-spot detection with a 256 × 256 image in about 4 s for PCNN segmentation using IDL software which is the fastest one in this field at present. Our experimental results demonstrate that the proposed approach is very fast, robust and effective. The proposed approach can be applied to the future spaceborne SAR images.

Weibull Multiplicative Model and Machine Learning Models for Full-Automatic Dark-Spot Detection from SAR Images

Science.gov (United States)

Taravat, A.; Del Frate, F.

2013-09-01

As a major aspect of marine pollution, oil release into the sea has serious biological and environmental impacts. Among remote sensing systems (which is a tool that offers a non-destructive investigation method), synthetic aperture radar (SAR) can provide valuable synoptic information about the position and size of the oil spill due to its wide area coverage and day/night, and all-weather capabilities. In this paper we present a new automated method for oil-spill monitoring. A new approach is based on the combination of Weibull Multiplicative Model and machine learning techniques to differentiate between dark spots and the background. First, the filter created based on Weibull Multiplicative Model is applied to each sub-image. Second, the sub-image is segmented by two different neural networks techniques (Pulsed Coupled Neural Networks and Multilayer Perceptron Neural Networks). As the last step, a very simple filtering process is used to eliminate the false targets. The proposed approaches were tested on 20 ENVISAT and ERS2 images which contained dark spots. The same parameters were used in all tests. For the overall dataset, the average accuracies of 94.05 % and 95.20 % were obtained for PCNN and MLP methods, respectively. The average computational time for dark-spot detection with a 256 × 256 image in about 4 s for PCNN segmentation using IDL software which is the fastest one in this field at present. Our experimental results demonstrate that the proposed approach is very fast, robust and effective. The proposed approach can be applied to the future spaceborne SAR images.

Robust tie points selection for InSAR image coregistration

Science.gov (United States)

Skanderi, Takieddine; Chabira, Boulerbah; Afifa, Belkacem; Belhadj Aissa, Aichouche

2013-10-01

Image coregistration is an important step in SAR interferometry which is a well known method for DEM generation and surface displacement monitoring. A practical and widely used automatic coregistration algorithm is based on selecting a number of tie points in the master image and looking for the correspondence of each point in the slave image using correlation technique. The characteristics of these points, their number and their distribution have a great impact on the reliability of the estimated transformation. In this work, we present a method for automatic selection of suitable tie points that are well distributed over the common area without decreasing the desired tie points' number. First we select candidate points using Harris operator. Then from these points we select tie points depending on their cornerness measure (the highest first). Once a tie point is selected, its correspondence is searched for in the slave image, if the similarity measure maximum is less than a given threshold or it is at the border of the search window, this point is discarded and we proceed to the next Harris point, else, the cornerness of the remaining candidates Harris points are multiplied by a spatially radially increasing function centered at the selected point to disadvantage the points in a neighborhood of a radius determined from the size of the common area and the desired number of points. This is repeated until the desired number of points is selected. Results of an ERS1/2 tandem pair are presented and discussed.

Comparing Accuracy of Airborne Laser Scanning and TerraSAR-X Radar Images in the Estimation of Plot-Level Forest Variables

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Juha Hyyppä

2010-01-01

Full Text Available In this study we compared the accuracy of low-pulse airborne laser scanning (ALS data, multi-temporal high-resolution noninterferometric TerraSAR-X radar data and a combined feature set derived from these data in the estimation of forest variables at plot level. The TerraSAR-X data set consisted of seven dual-polarized (HH/HV or VH/VV Stripmap mode images from all seasons of the year. We were especially interested in distinguishing between the tree species. The dependent variables estimated included mean volume, basal area, mean height, mean diameter and tree species-specific mean volumes. Selection of best possible feature set was based on a genetic algorithm (GA. The nonparametric k-nearest neighbour (k-NN algorithm was applied to the estimation. The research material consisted of 124 circular plots measured at tree level and located in the vicinity of Espoo, Finland. There are large variations in the elevation and forest structure in the study area, making it demanding for image interpretation. The best feature set contained 12 features, nine of them originating from the ALS data and three from the TerraSAR-X data. The relative RMSEs for the best performing feature set were 34.7% (mean volume, 28.1% (basal area, 14.3% (mean height, 21.4% (mean diameter, 99.9% (mean volume of Scots pine, 61.6% (mean volume of Norway spruce and 91.6% (mean volume of deciduous tree species. The combined feature set outperformed an ALS-based feature set marginally; in fact, the latter was better in the case of species-specific volumes. Features from TerraSAR-X alone performed poorly. However, due to favorable temporal resolution, satellite-borne radar imaging is a promising data source for updating large-area forest inventories based on low-pulse ALS.

Rice status and microwave characteristics: Analysis of rice paddy fields at Kojima Bay [Okayama, Japan] using multi-frequency and polarimetric Pi-SAR radar data images

International Nuclear Information System (INIS)

Ishitsuka, N.; Saito, G.; Ouchi, K.; Davidson, G.; Mohri, K.; Uratsuka, S.

2003-01-01

Abstract South-east Asia has a rainy-season at the crop growing period, and it is difficult to observe agricultural land in this season using optical remote sensing. Synthetic Aperture Radar (SAR) can observe the earth's surface without being influenced by of clouds. However, it is less useful for observing agricultural land, because satellite SAR has only one data band. Recently, SAR is able to provide multi band and multi polarimetric data. Pi-SAR, an airborne SAR developed by NASDA and CRL, can provide L and X bands and fully polarimetric data. Rice is the main crop in Asia, and we studied the characteristic microwave scatter on rice paddy fields using Pi-SAR data. Our study area was the rice paddy fields in Kojima reclaimed land in Japan. We had two fully polarimetric data sets from 13 July 1999 and 4 October 2000. First, we processed the color polarimetric composite image. Next we calibrated the phase of each polarimetric data using river area by the Kimura method. After that we performed decomposition analysis and drew polarimetric signatures for understanding the status of rice paddy fields. At the rice planting period, rice paddy fields are filled with water and rice plants are very small. The SAR microwave scatters on water surfaces like a mirror, called 'mirror (or specular) reflection'. This phenomenon makes backscatter a small value at the water-covered area. The image from July is about one month after trans-planting and rice plants are 20-40 cm in height. X-band microwave scatters on the rice surface, but L-band microwave passes through rice bodies and shows mirror refraction on water surfaces. Some strong backscatter occur on rice paddy fields especially VV polarization because of bragg scattering. The fields where bragg scattering returns strong VV scatter because the space between rice stems cause resonation in the L-band wavelength. We can easily understand bragg scatter by using polarimetric data. Using the image from October at

Discrimination of Different Water Layers with TerraSAR X Images in "La Albufera de Valencia"

Science.gov (United States)

García Fernández, M. A.; Miguelsanz Muñoz, P.

2009-04-01

To analyze the capabilities of TerraSAR X Strip Map images in order to discriminate different water layers in the "Parque de la Albufera de Valencia", Spain, a test project was carried out. This place is a rice crop area under European and National Agro environmental regulation which obliges to preserve the habitat and to keep the rice plots flooded out of crop season, from October to January

Geocoding of SAR Image Using the Orbit and Attitude Determination of RADARSAT

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Jin Wook So

1998-06-01

Full Text Available The Synthetic Aperture Radar (SAR image and the Digital Elevation Model (DEM of an target area are put into use to generate three dimensional image map. An method of image map generation is explained. The orbit and attitude determination of satellite makes it possible to model signal acquisition configuration precisely, which is a key to mapping image coordinates to geographic coordinates of concerned area. An application is made to RADARSAT in the purpose of testing its validity. To determine the orbit, zero Doppler range is used. And to determine the attitude, Doppler centroid frequency, which is the frequency observed when target is in the center of antenna's view, is used. Conventional geocoding has been performed on the basis of direct method(mapping image coordinates to geographic coordinates, but in this research the inverse method (mapping from geographic coordinates to image coordinates is taken. This paper shows that precise signal acquisition modeling based on the orbit and attitude determination of satellite as a platform leads to a satellite-centered accurate geocoding process. It also shows how to model relative motion between spaceborne radar and target. And the relative motion is described in ECIC (earth-centered initial coordinates using Doppler equation and signal acquisition geometry.

An Adaptive Moving Target Imaging Method for Bistatic Forward-Looking SAR Using Keystone Transform and Optimization NLCS.

Science.gov (United States)

Li, Zhongyu; Wu, Junjie; Huang, Yulin; Yang, Haiguang; Yang, Jianyu

2017-01-23

Bistatic forward-looking SAR (BFSAR) is a kind of bistatic synthetic aperture radar (SAR) system that can image forward-looking terrain in the flight direction of an aircraft. Until now, BFSAR imaging theories and methods for a stationary scene have been researched thoroughly. However, for moving-target imaging with BFSAR, the non-cooperative movement of the moving target induces some new issues: (I) large and unknown range cell migration (RCM) (including range walk and high-order RCM); (II) the spatial-variances of the Doppler parameters (including the Doppler centroid and high-order Doppler) are not only unknown, but also nonlinear for different point-scatterers. In this paper, we put forward an adaptive moving-target imaging method for BFSAR. First, the large and unknown range walk is corrected by applying keystone transform over the whole received echo, and then, the relationships among the unknown high-order RCM, the nonlinear spatial-variances of the Doppler parameters, and the speed of the mover, are established. After that, using an optimization nonlinear chirp scaling (NLCS) technique, not only can the unknown high-order RCM be accurately corrected, but also the nonlinear spatial-variances of the Doppler parameters can be balanced. At last, a high-order polynomial filter is applied to compress the whole azimuth data of the moving target. Numerical simulations verify the effectiveness of the proposed method.

An Adaptive Moving Target Imaging Method for Bistatic Forward-Looking SAR Using Keystone Transform and Optimization NLCS

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Zhongyu Li

2017-01-01

Full Text Available Bistatic forward-looking SAR (BFSAR is a kind of bistatic synthetic aperture radar (SAR system that can image forward-looking terrain in the flight direction of an aircraft. Until now, BFSAR imaging theories and methods for a stationary scene have been researched thoroughly. However, for moving-target imaging with BFSAR, the non-cooperative movement of the moving target induces some new issues: (I large and unknown range cell migration (RCM (including range walk and high-order RCM; (II the spatial-variances of the Doppler parameters (including the Doppler centroid and high-order Doppler are not only unknown, but also nonlinear for different point-scatterers. In this paper, we put forward an adaptive moving-target imaging method for BFSAR. First, the large and unknown range walk is corrected by applying keystone transform over the whole received echo, and then, the relationships among the unknown high-order RCM, the nonlinear spatial-variances of the Doppler parameters, and the speed of the mover, are established. After that, using an optimization nonlinear chirp scaling (NLCS technique, not only can the unknown high-order RCM be accurately corrected, but also the nonlinear spatial-variances of the Doppler parameters can be balanced. At last, a high-order polynomial filter is applied to compress the whole azimuth data of the moving target. Numerical simulations verify the effectiveness of the proposed method.

Space Radar Image of Manaus, Brazil

Science.gov (United States)

1999-01-01

These two images were created using data from the Spaceborne Imaging Radar-C/X-Band Synthetic Aperture Radar (SIR-C/X-SAR). On the left is a false-color image of Manaus, Brazil acquired April 12, 1994, onboard space shuttle Endeavour. In the center of this image is the Solimoes River just west of Manaus before it combines with the Rio Negro to form the Amazon River. The scene is around 8 by 8 kilometers (5 by 5 miles) with north toward the top. The radar image was produced in L-band where red areas correspond to high backscatter at HH polarization, while green areas exhibit high backscatter at HV polarization. Blue areas show low backscatter at VV polarization. The image on the right is a classification map showing the extent of flooding beneath the forest canopy. The classification map was developed by SIR-C/X-SAR science team members at the University of California,Santa Barbara. The map uses the L-HH, L-HV, and L-VV images to classify the radar image into six categories: Red flooded forest Green unflooded tropical rain forest Blue open water, Amazon river Yellow unflooded fields, some floating grasses Gray flooded shrubs Black floating and flooded grasses Data like these help scientists evaluate flood damage on a global scale. Floods are highly episodic and much of the area inundated is often tree-covered. Spaceborne Imaging Radar-C and X-Synthetic Aperture Radar (SIR-C/X-SAR) is part of NASA's Mission to Planet Earth. The radars illuminate Earth with microwaves allowing detailed observations at any time, regardless of weather or sunlight conditions. SIR-C/X-SAR uses three microwave wavelengths: L-band (24 cm), C-band (6 cm) and X-band (3 cm). The multi-frequency data will be used by the international scientific community to better understand the global environment and how it is changing. The SIR-C/X-SAR data, complemented by aircraft and ground studies, will give scientists clearer insights into those environmental changes which are caused by nature and those

MULTI-TEMPORAL SAR INTERFEROMETRY FOR LANDSLIDE MONITORING

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R. Dwivedi

2016-06-01

Full Text Available In the past few years, SAR Interferometry specially InSAR and D-InSAR were extensively used for deformation monitoring related applications. Due to temporal and spatial decorrelation in dense vegetated areas, effectiveness of InSAR and D-InSAR observations were always under scrutiny. Multi-temporal InSAR methods are developed in recent times to retrieve the deformation signal from pixels with different scattering characteristics. Presently, two classes of multi-temporal InSAR algorithms are available- Persistent Scatterer (PS and Small Baseline (SB methods. This paper discusses the Stanford Method for Persistent Scatterer (StaMPS based PS-InSAR and the Small Baselines Subset (SBAS techniques to estimate the surface deformation in Tehri dam reservoir region in Uttarkhand, India. Both PS-InSAR and SBAS approaches used sixteen ENVISAT ASAR C-Band images for generating single master and multiple master interferograms stack respectively and their StaMPS processing resulted in time series 1D-Line of Sight (LOS mean velocity maps which are indicative of deformation in terms of movement towards and away from the satellites. From 1D LOS velocity maps, localization of landslide is evident along the reservoir rim area which was also investigated in the previous studies. Both PS-InSAR and SBAS effectively extract measurement pixels in the study region, and the general results provided by both approaches show a similar deformation pattern along the Tehri reservoir region. Further, we conclude that StaMPS based PS-InSAR method performs better in terms of extracting more number of measurement pixels and in the estimation of mean Line of Sight (LOS velocity as compared to SBAS method. It is also proposed to take up a few major landslides area in Uttarakhand for slope stability assessment.

A Multi-Scale Flood Monitoring System Based on Fully Automatic MODIS and TerraSAR-X Processing Chains

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Enrico Stein

2013-10-01

Full Text Available A two-component fully automated flood monitoring system is described and evaluated. This is a result of combining two individual flood services that are currently under development at DLR’s (German Aerospace Center Center for Satellite based Crisis Information (ZKI to rapidly support disaster management activities. A first-phase monitoring component of the system systematically detects potential flood events on a continental scale using daily-acquired medium spatial resolution optical data from the Moderate Resolution Imaging Spectroradiometer (MODIS. A threshold set controls the activation of the second-phase crisis component of the system, which derives flood information at higher spatial detail using a Synthetic Aperture Radar (SAR based satellite mission (TerraSAR-X. The proposed activation procedure finds use in the identification of flood situations in different spatial resolutions and in the time-critical and on demand programming of SAR satellite acquisitions at an early stage of an evolving flood situation. The automated processing chains of the MODIS (MFS and the TerraSAR-X Flood Service (TFS include data pre-processing, the computation and adaptation of global auxiliary data, thematic classification, and the subsequent dissemination of flood maps using an interactive web-client. The system is operationally demonstrated and evaluated via the monitoring two recent flood events in Russia 2013 and Albania/Montenegro 2013.

Comparison between Multitemporal and Polarimetric SAR Data for Land Cover Classification

DEFF Research Database (Denmark)

Skriver, Henning

2008-01-01

The investigation focuses on the determination of the land cover type using SAR data, including single polarisation, dual polarisation and fully polarimetric data, at L-band. The analysed data set was acquired during the AgriSAR 2006 campaign by the airborne ESAR system over the Gormin agricultural...... site (Northeast Germany). The multitemporal acquisitions significantly improve the classification results for single and dual polarization configurations. The best results for the single and dual polarization configurations are better than for the polarimetric mode. Overall, the cross...

Robust Ground Target Detection by SAR and IR Sensor Fusion Using Adaboost-Based Feature Selection

Science.gov (United States)

Kim, Sungho; Song, Woo-Jin; Kim, So-Hyun

2016-01-01

Long-range ground targets are difficult to detect in a noisy cluttered environment using either synthetic aperture radar (SAR) images or infrared (IR) images. SAR-based detectors can provide a high detection rate with a high false alarm rate to background scatter noise. IR-based approaches can detect hot targets but are affected strongly by the weather conditions. This paper proposes a novel target detection method by decision-level SAR and IR fusion using an Adaboost-based machine learning scheme to achieve a high detection rate and low false alarm rate. The proposed method consists of individual detection, registration, and fusion architecture. This paper presents a single framework of a SAR and IR target detection method using modified Boolean map visual theory (modBMVT) and feature-selection based fusion. Previous methods applied different algorithms to detect SAR and IR targets because of the different physical image characteristics. One method that is optimized for IR target detection produces unsuccessful results in SAR target detection. This study examined the image characteristics and proposed a unified SAR and IR target detection method by inserting a median local average filter (MLAF, pre-filter) and an asymmetric morphological closing filter (AMCF, post-filter) into the BMVT. The original BMVT was optimized to detect small infrared targets. The proposed modBMVT can remove the thermal and scatter noise by the MLAF and detect extended targets by attaching the AMCF after the BMVT. Heterogeneous SAR and IR images were registered automatically using the proposed RANdom SAmple Region Consensus (RANSARC)-based homography optimization after a brute-force correspondence search using the detected target centers and regions. The final targets were detected by feature-selection based sensor fusion using Adaboost. The proposed method showed good SAR and IR target detection performance through feature selection-based decision fusion on a synthetic database generated

Robust Ground Target Detection by SAR and IR Sensor Fusion Using Adaboost-Based Feature Selection

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Sungho Kim

2016-07-01

Full Text Available Long-range ground targets are difficult to detect in a noisy cluttered environment using either synthetic aperture radar (SAR images or infrared (IR images. SAR-based detectors can provide a high detection rate with a high false alarm rate to background scatter noise. IR-based approaches can detect hot targets but are affected strongly by the weather conditions. This paper proposes a novel target detection method by decision-level SAR and IR fusion using an Adaboost-based machine learning scheme to achieve a high detection rate and low false alarm rate. The proposed method consists of individual detection, registration, and fusion architecture. This paper presents a single framework of a SAR and IR target detection method using modified Boolean map visual theory (modBMVT and feature-selection based fusion. Previous methods applied different algorithms to detect SAR and IR targets because of the different physical image characteristics. One method that is optimized for IR target detection produces unsuccessful results in SAR target detection. This study examined the image characteristics and proposed a unified SAR and IR target detection method by inserting a median local average filter (MLAF, pre-filter and an asymmetric morphological closing filter (AMCF, post-filter into the BMVT. The original BMVT was optimized to detect small infrared targets. The proposed modBMVT can remove the thermal and scatter noise by the MLAF and detect extended targets by attaching the AMCF after the BMVT. Heterogeneous SAR and IR images were registered automatically using the proposed RANdom SAmple Region Consensus (RANSARC-based homography optimization after a brute-force correspondence search using the detected target centers and regions. The final targets were detected by feature-selection based sensor fusion using Adaboost. The proposed method showed good SAR and IR target detection performance through feature selection-based decision fusion on a synthetic

RAMP AMM-1 SAR Image Mosaic of Antarctica

Data.gov (United States)

National Aeronautics and Space Administration — In 1997, the Canadian RADARSAT-1 satellite was rotated in orbit so that its Synthetic Aperture Radar (SAR) antenna looked south towards Antarctica. This permitted...

Electrical Signaling, Photosynthesis and Systemic Acquired Acclimation

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Magdalena Szechyńska-Hebda

2017-09-01

Full Text Available Electrical signaling in higher plants is required for the appropriate intracellular and intercellular communication, stress responses, growth and development. In this review, we have focus on recent findings regarding the electrical signaling, as a major regulator of the systemic acquired acclimation (SAA and the systemic acquired resistance (SAR. The electric signaling on its own cannot confer the required specificity of information to trigger SAA and SAR, therefore, we have also discussed a number of other mechanisms and signaling systems that can operate in combination with electric signaling. We have emphasized the interrelation between ionic mechanism of electrical activity and regulation of photosynthesis, which is intrinsic to a proper induction of SAA and SAR. In a special way, we have summarized the role of non-photochemical quenching and its regulator PsbS. Further, redox status of the cell, calcium and hydraulic waves, hormonal circuits and stomatal aperture regulation have been considered as components of the signaling. Finally, a model of light-dependent mechanisms of electrical signaling propagation has been presented together with the systemic regulation of light-responsive genes encoding both, ion channels and proteins involved in regulation of their activity. Due to space limitations, we have not addressed many other important aspects of hormonal and ROS signaling, which were presented in a number of recent excellent reviews.

An Improved InSAR Image Co-Registration Method for Pairs with Relatively Big Distortions or Large Incoherent Areas

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Zhenwei Chen

2016-09-01

Full Text Available Co-registration is one of the most important steps in interferometric synthetic aperture radar (InSAR data processing. The standard offset-measurement method based on cross-correlating uniformly distributed patches takes no account of specific geometric transformation between images or characteristics of ground scatterers. Hence, it is inefficient and difficult to obtain satisfying co-registration results for image pairs with relatively big distortion or large incoherent areas. Given this, an improved co-registration strategy is proposed in this paper which takes both the geometric features and image content into consideration. Firstly, some geometric transformations including scale, flip, rotation, and shear between images were eliminated based on the geometrical information, and the initial co-registration polynomial was obtained. Then the registration points were automatically detected by integrating the signal-to-clutter-ratio (SCR thresholds and the amplitude information, and a further co-registration process was performed to refine the polynomial. Several comparison experiments were carried out using 2 TerraSAR-X data from the Hong Kong airport and 21 PALSAR data from the Donghai Bridge. Experiment results demonstrate that the proposed method brings accuracy and efficiency improvements for co-registration and processing abilities in the cases of big distortion between images or large incoherent areas in the images. For most co-registrations, the proposed method can enhance the reliability and applicability of co-registration and thus promote the automation to a higher level.

An Improved InSAR Image Co-Registration Method for Pairs with Relatively Big Distortions or Large Incoherent Areas.

Science.gov (United States)

Chen, Zhenwei; Zhang, Lei; Zhang, Guo

2016-09-17

Co-registration is one of the most important steps in interferometric synthetic aperture radar (InSAR) data processing. The standard offset-measurement method based on cross-correlating uniformly distributed patches takes no account of specific geometric transformation between images or characteristics of ground scatterers. Hence, it is inefficient and difficult to obtain satisfying co-registration results for image pairs with relatively big distortion or large incoherent areas. Given this, an improved co-registration strategy is proposed in this paper which takes both the geometric features and image content into consideration. Firstly, some geometric transformations including scale, flip, rotation, and shear between images were eliminated based on the geometrical information, and the initial co-registration polynomial was obtained. Then the registration points were automatically detected by integrating the signal-to-clutter-ratio (SCR) thresholds and the amplitude information, and a further co-registration process was performed to refine the polynomial. Several comparison experiments were carried out using 2 TerraSAR-X data from the Hong Kong airport and 21 PALSAR data from the Donghai Bridge. Experiment results demonstrate that the proposed method brings accuracy and efficiency improvements for co-registration and processing abilities in the cases of big distortion between images or large incoherent areas in the images. For most co-registrations, the proposed method can enhance the reliability and applicability of co-registration and thus promote the automation to a higher level.