One new method for road data shape change detection

Science.gov (United States)

Tang, Luliang; Li, Qingquan; Xu, Feng; Chang, Xiaomeng

2009-10-01

Similarity is a psychological cognition; this paper defines the Difference Distance and puts forward the Similarity Measuring Model for linear spatial data (SMM-L) based on the integration of the Distance View and the Feature Set View which are the views for similarity cognition. Based on the study of the relationship between the spatial data change and the similarity, a change detection algorithm for linear spatial data is developed, and a test on road data change detection is realized.

Object-Based Change Detection in Urban Areas: The Effects of Segmentation Strategy, Scale, and Feature Space on Unsupervised Methods

Directory of Open Access Journals (Sweden)

Lei Ma

2016-09-01

Full Text Available Object-based change detection (OBCD has recently been receiving increasing attention as a result of rapid improvements in the resolution of remote sensing data. However, some OBCD issues relating to the segmentation of high-resolution images remain to be explored. For example, segmentation units derived using different segmentation strategies, segmentation scales, feature space, and change detection methods have rarely been assessed. In this study, we have tested four common unsupervised change detection methods using different segmentation strategies and a series of segmentation scale parameters on two WorldView-2 images of urban areas. We have also evaluated the effect of adding extra textural and Normalized Difference Vegetation Index (NDVI information instead of using only spectral information. Our results indicated that change detection methods performed better at a medium scale than at a fine scale where close to the pixel size. Multivariate Alteration Detection (MAD always outperformed the other methods tested, at the same confidence level. The overall accuracy appeared to benefit from using a two-date segmentation strategy rather than single-date segmentation. Adding textural and NDVI information appeared to reduce detection accuracy, but the magnitude of this reduction was not consistent across the different unsupervised methods and segmentation strategies. We conclude that a two-date segmentation strategy is useful for change detection in high-resolution imagery, but that the optimization of thresholds is critical for unsupervised change detection methods. Advanced methods need be explored that can take advantage of additional textural or other parameters.

The Regularized Iteratively Reweighted MAD Method for Change Detection in Multi- and Hyperspectral Data

DEFF Research Database (Denmark)

Nielsen, Allan Aasbjerg

2007-01-01

This paper describes new extensions to the previously published multivariate alteration detection (MAD) method for change detection in bi-temporal, multi- and hypervariate data such as remote sensing imagery. Much like boosting methods often applied in data mining work, the iteratively reweighted...... to observations that show little change, i.e., for which the sum of squared, standardized MAD variates is small, and small weights are assigned to observations for which the sum is large. Like the original MAD method, the iterative extension is invariant to linear (affine) transformations of the original...... an agricultural region in Kenya, and hyperspectral airborne HyMap data from a small rural area in southeastern Germany are given. The latter case demonstrates the need for regularization....

Unsupervised Condition Change Detection In Large Diesel Engines

DEFF Research Database (Denmark)

Pontoppidan, Niels Henrik; Larsen, Jan

2003-01-01

This paper presents a new method for unsupervised change detection which combines independent component modeling and probabilistic outlier etection. The method further provides a compact data representation, which is amenable to interpretation, i.e., the detected condition changes can be investig...... be investigated further. The method is successfully applied to unsupervised condition change detection in large diesel engines from acoustical emission sensor signal and compared to more classical techniques based on principal component analysis and Gaussian mixture models.......This paper presents a new method for unsupervised change detection which combines independent component modeling and probabilistic outlier etection. The method further provides a compact data representation, which is amenable to interpretation, i.e., the detected condition changes can...

A Hybrid Change Detection Approach for Damage Detection and Recovery Monitoring

Science.gov (United States)

de Alwis Pitts, Dilkushi; Wieland, Marc; Wang, Shifeng; So, Emily; Pittore, Massimiliano

2014-05-01

Following a disaster, change detection via pre- and post-event very high resolution remote sensing images is an essential technique for damage assessment and recovery monitoring over large areas in complex urban environments. Most assessments to date focus on detection, destruction and recovery of man-made objects that facilitate shelter and accessibility, such as buildings, roads, bridges, etc., as indicators for assessment and better decision making. Moreover, many current change-detection mechanisms do not use all the data and knowledge which are often available for the pre-disaster state. Recognizing the continuous rather than dichotomous character of the data-rich/data-poor distinction permits the incorporation of ancillary data and existing knowledge into the processing flow. Such incorporation could improve the reliability of the results and thereby enhance the usability of robust methods for disaster management. This study proposes an application-specific and robust change detection method from multi-temporal very high resolution multi-spectral satellite images. This hybrid indicator-specific method uses readily available pre-disaster GIS data and integrates existing knowledge into the processing flow to optimize the change detection while offering the possibility to target specific types of changes to man-made objects. The indicator-specific information of the GIS objects is used as a series of masks to treat the GIS objects with similar characteristics similarly for better accuracy. The proposed approach is based on a fusion of a multi-index change detection method based on gradient, texture and edge similarity filters. The change detection index is flexible for disaster cases in which the pre-disaster and post-disaster images are not of the same resolution. The proposed automated method is evaluated with QuickBird and Ikonos datasets for abrupt changes soon after disaster. The method could also be extended in a semi-automated way for monitoring

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.

Considerations and methods for the changes detection using satellite images in the Municipality of Paipa

International Nuclear Information System (INIS)

Riano M, Orlando

2002-01-01

In this article the considerations and methods are presented for the changes detection in the earth covering, using two images Landsat TM of different dates for an area of the municipality of Paipa, Boyaca. The changes detection has become an important application of the multi-spectral data and multi-temporal of the satellites programs for studies of natural resources Landsat, TM and Spot, in such a way that is possible to determine the types and extension of the changes that are given in the environment. To carry out this process some digital techniques they have been used for changes detection, such as: images superposition, differences between images and analysis of main components. These techniques allowed to observe and to analyze changes in the use and covering of the earth in this municipality

Probabilistic BPRRC: Robust Change Detection against Illumination Changes and Background Movements

Science.gov (United States)

Yokoi, Kentaro

This paper presents Probabilistic Bi-polar Radial Reach Correlation (PrBPRRC), a change detection method that is robust against illumination changes and background movements. Most of the traditional change detection methods are robust against either illumination changes or background movements; BPRRC is one of the illumination-robust change detection methods. We introduce a probabilistic background texture model into BPRRC and add the robustness against background movements including foreground invasions such as moving cars, walking people, swaying trees, and falling snow. We show the superiority of PrBPRRC in the environment with illumination changes and background movements by using three public datasets and one private dataset: ATON Highway data, Karlsruhe traffic sequence data, PETS 2007 data, and Walking-in-a-room data.

SWCD: a sliding window and self-regulated learning-based background updating method for change detection in videos

Science.gov (United States)

Işık, Şahin; Özkan, Kemal; Günal, Serkan; Gerek, Ömer Nezih

2018-03-01

Change detection with background subtraction process remains to be an unresolved issue and attracts research interest due to challenges encountered on static and dynamic scenes. The key challenge is about how to update dynamically changing backgrounds from frames with an adaptive and self-regulated feedback mechanism. In order to achieve this, we present an effective change detection algorithm for pixelwise changes. A sliding window approach combined with dynamic control of update parameters is introduced for updating background frames, which we called sliding window-based change detection. Comprehensive experiments on related test videos show that the integrated algorithm yields good objective and subjective performance by overcoming illumination variations, camera jitters, and intermittent object motions. It is argued that the obtained method makes a fair alternative in most types of foreground extraction scenarios; unlike case-specific methods, which normally fail for their nonconsidered scenarios.

Detecting abrupt dynamic change based on changes in the fractal properties of spatial images

Science.gov (United States)

Liu, Qunqun; He, Wenping; Gu, Bin; Jiang, Yundi

2017-10-01

Many abrupt climate change events often cannot be detected timely by conventional abrupt detection methods until a few years after these events have occurred. The reason for this lag in detection is that abundant and long-term observational data are required for accurate abrupt change detection by these methods, especially for the detection of a regime shift. So, these methods cannot help us understand and forecast the evolution of the climate system in a timely manner. Obviously, spatial images, generated by a coupled spatiotemporal dynamical model, contain more information about a dynamic system than a single time series, and we find that spatial images show the fractal properties. The fractal properties of spatial images can be quantitatively characterized by the Hurst exponent, which can be estimated by two-dimensional detrended fluctuation analysis (TD-DFA). Based on this, TD-DFA is used to detect an abrupt dynamic change of a coupled spatiotemporal model. The results show that the TD-DFA method can effectively detect abrupt parameter changes in the coupled model by monitoring the changing in the fractal properties of spatial images. The present method provides a new way for abrupt dynamic change detection, which can achieve timely and efficient abrupt change detection results.

Detecting anthropogenic climate change with an optimal fingerprint method

International Nuclear Information System (INIS)

Hegerl, G.C.; Storch, H. von; Hasselmann, K.; Santer, B.D.; Jones, P.D.

1994-01-01

We propose a general fingerprint strategy to detect anthropogenic climate change and present application to near surface temperature trends. An expected time-space-variable pattern of anthropogenic climate change (the 'signal') is identified through application of an appropriate optimally matched space-time filter (the 'fingerprint') to the observations. The signal and the fingerprint are represented in a space with sufficient observed and simulated data. The signal pattern is derived from a model-generated prediction of anthropogenic climate change. Application of the fingerprint filter to the data yields a scalar detection variable. The statistically optimal fingerprint is obtained by weighting the model-predicted pattern towards low-noise directions. A combination of model output and observations is used to estimate the noise characteristics of the detection variable, arising from the natural variability of climate in the absence of external forcing. We test then the null hypothesis that the observed climate change is part of natural climate variability. We conclude that a statistically significant externally induced warming has been observed, with the caveat of a possibly inadequate estimate of the internal climate variability. In order to attribute this warming uniquely to anthropogenic greenhouse gas forcing, more information on the climate's response to other forcing mechanisms (e.g. changes in solar radiation, volcanic or anthropogenic aerosols) and their interaction is needed. (orig./KW)

Minimal changes in health status questionnaires: distinction between minimally detectable change and minimally important change

Directory of Open Access Journals (Sweden)

Knol Dirk L

2006-08-01

Full Text Available Abstract Changes in scores on health status questionnaires are difficult to interpret. Several methods to determine minimally important changes (MICs have been proposed which can broadly be divided in distribution-based and anchor-based methods. Comparisons of these methods have led to insight into essential differences between these approaches. Some authors have tried to come to a uniform measure for the MIC, such as 0.5 standard deviation and the value of one standard error of measurement (SEM. Others have emphasized the diversity of MIC values, depending on the type of anchor, the definition of minimal importance on the anchor, and characteristics of the disease under study. A closer look makes clear that some distribution-based methods have been merely focused on minimally detectable changes. For assessing minimally important changes, anchor-based methods are preferred, as they include a definition of what is minimally important. Acknowledging the distinction between minimally detectable and minimally important changes is useful, not only to avoid confusion among MIC methods, but also to gain information on two important benchmarks on the scale of a health status measurement instrument. Appreciating the distinction, it becomes possible to judge whether the minimally detectable change of a measurement instrument is sufficiently small to detect minimally important changes.

Detecting change-points in extremes

KAUST Repository

Dupuis, D. J.; Sun, Ying; Wang, Huixia Judy

2015-01-01

Even though most work on change-point estimation focuses on changes in the mean, changes in the variance or in the tail distribution can lead to more extreme events. In this paper, we develop a new method of detecting and estimating the change

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

Directory of Open Access Journals (Sweden)

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.

On Radar Resolution in Coherent Change Detection.

Energy Technology Data Exchange (ETDEWEB)

Bickel, Douglas L. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2015-11-01

It is commonly observed that resolution plays a role in coherent change detection. Although this is the case, the relationship of the resolution in coherent change detection is not yet defined . In this document, we present an analytical method of evaluating this relationship using detection theory. Specifically we examine the effect of resolution on receiver operating characteristic curves for coherent change detection.

Climate change and precipitation: Detecting changes Climate change and precipitation: Detecting changes

International Nuclear Information System (INIS)

Van Boxel, John H

2001-01-01

Precipitation is one of the most, if not the most important climate parameter In most studies on climate change the emphasis is on temperature and sea level rise. Often too little attention is given to precipitation. For a large part this is due to the large spatial en temporal variability of precipitation, which makes the detection of changes difficult. This paper describes methods to detect changes in precipitation. In order to arrive at statistically significant changes one must use long time series and spatial averages containing the information from several stations. In the Netherlands the average yearly precipitation increased by 11% during the 20th century .In the temperate latitudes on the Northern Hemisphere (40-60QN) the average increase was about 7% over the 20th century and the globally averaged precipitation increased by about 3%. During the 20th century 38% of the land surface of the earth became wetter, 42% experienced little change (less than 5% change) and 20% became dryer. More important than the average precipitation is the occurrence of extremes. In the Netherlands there is a tendency to more extreme precipitations, whereas the occurrence of relatively dry months has not changed. Also in many other countries increases in heavy precipitation events are observed. All climate models predict a further increase of mean global precipitation if the carbon dioxide concentration doubles. Nevertheless some areas get dryer, others have little change and consequently there are also areas where the increase is much more than the global average. On a regional scale however there are large differences between the models. Climate models do not yet provide adequate information on changes in extreme precipitations

A Saliency Guided Semi-Supervised Building Change Detection Method for High Resolution Remote Sensing Images

Directory of Open Access Journals (Sweden)

Bin Hou

2016-08-01

Full Text Available Characterizations of up to date information of the Earth’s surface are an important application providing insights to urban planning, resources monitoring and environmental studies. A large number of change detection (CD methods have been developed to solve them by utilizing remote sensing (RS images. The advent of high resolution (HR remote sensing images further provides challenges to traditional CD methods and opportunities to object-based CD methods. While several kinds of geospatial objects are recognized, this manuscript mainly focuses on buildings. Specifically, we propose a novel automatic approach combining pixel-based strategies with object-based ones for detecting building changes with HR remote sensing images. A multiresolution contextual morphological transformation called extended morphological attribute profiles (EMAPs allows the extraction of geometrical features related to the structures within the scene at different scales. Pixel-based post-classification is executed on EMAPs using hierarchical fuzzy clustering. Subsequently, the hierarchical fuzzy frequency vector histograms are formed based on the image-objects acquired by simple linear iterative clustering (SLIC segmentation. Then, saliency and morphological building index (MBI extracted on difference images are used to generate a pseudo training set. Ultimately, object-based semi-supervised classification is implemented on this training set by applying random forest (RF. Most of the important changes are detected by the proposed method in our experiments. This study was checked for effectiveness using visual evaluation and numerical evaluation.

A comprehensive change detection method for updating the National Land Cover Database to circa 2011

Science.gov (United States)

Jin, Suming; Yang, Limin; Danielson, Patrick; Homer, Collin G.; Fry, Joyce; Xian, George

2013-01-01

The importance of characterizing, quantifying, and monitoring land cover, land use, and their changes has been widely recognized by global and environmental change studies. Since the early 1990s, three U.S. National Land Cover Database (NLCD) products (circa 1992, 2001, and 2006) have been released as free downloads for users. The NLCD 2006 also provides land cover change products between 2001 and 2006. To continue providing updated national land cover and change datasets, a new initiative in developing NLCD 2011 is currently underway. We present a new Comprehensive Change Detection Method (CCDM) designed as a key component for the development of NLCD 2011 and the research results from two exemplar studies. The CCDM integrates spectral-based change detection algorithms including a Multi-Index Integrated Change Analysis (MIICA) model and a novel change model called Zone, which extracts change information from two Landsat image pairs. The MIICA model is the core module of the change detection strategy and uses four spectral indices (CV, RCVMAX, dNBR, and dNDVI) to obtain the changes that occurred between two image dates. The CCDM also includes a knowledge-based system, which uses critical information on historical and current land cover conditions and trends and the likelihood of land cover change, to combine the changes from MIICA and Zone. For NLCD 2011, the improved and enhanced change products obtained from the CCDM provide critical information on location, magnitude, and direction of potential change areas and serve as a basis for further characterizing land cover changes for the nation. An accuracy assessment from the two study areas show 100% agreement between CCDM mapped no-change class with reference dataset, and 18% and 82% disagreement for the change class for WRS path/row p22r39 and p33r33, respectively. The strength of the CCDM is that the method is simple, easy to operate, widely applicable, and capable of capturing a variety of natural and

OBJECT-ORIENTED CHANGE DETECTION BASED ON MULTI-SCALE APPROACH

Directory of Open Access Journals (Sweden)

Y. Jia

2016-06-01

Full Text Available The change detection of remote sensing images means analysing the change information quantitatively and recognizing the change types of the surface coverage data in different time phases. With the appearance of high resolution remote sensing image, object-oriented change detection method arises at this historic moment. In this paper, we research multi-scale approach for high resolution images, which includes multi-scale segmentation, multi-scale feature selection and multi-scale classification. Experimental results show that this method has a stronger advantage than the traditional single-scale method of high resolution remote sensing image change detection.

Illumination Invariant Change Detection (iicd): from Earth to Mars

Science.gov (United States)

Wan, X.; Liu, J.; Qin, M.; Li, S. Y.

2018-04-01

Multi-temporal Earth Observation and Mars orbital imagery data with frequent repeat coverage provide great capability for planetary surface change detection. When comparing two images taken at different times of day or in different seasons for change detection, the variation of topographic shades and shadows caused by the change of sunlight angle can be so significant that it overwhelms the real object and environmental changes, making automatic detection unreliable. An effective change detection algorithm therefore has to be robust to the illumination variation. This paper presents our research on developing and testing an Illumination Invariant Change Detection (IICD) method based on the robustness of phase correlation (PC) to the variation of solar illumination for image matching. The IICD is based on two key functions: i) initial change detection based on a saliency map derived from pixel-wise dense PC matching and ii) change quantization which combines change type identification, motion estimation and precise appearance change identification. Experiment using multi-temporal Landsat 7 ETM+ satellite images, Rapid eye satellite images and Mars HiRiSE images demonstrate that our frequency based image matching method can reach sub-pixel accuracy and thus the proposed IICD method can effectively detect and precisely segment large scale change such as landslide as well as small object change such as Mars rover, under daily and seasonal sunlight changes.

Rapid Change Detection Algorithm for Disaster Management

Science.gov (United States)

Michel, U.; Thunig, H.; Ehlers, M.; Reinartz, P.

2012-07-01

This paper focuses on change detection applications in areas where catastrophic events took place which resulted in rapid destruction especially of manmade objects. Standard methods for automated change detection prove not to be sufficient; therefore a new method was developed and tested. The presented method allows a fast detection and visualization of change in areas of crisis or catastrophes. While often new methods of remote sensing are developed without user oriented aspects, organizations and authorities are not able to use these methods because of absence of remote sensing know how. Therefore a semi-automated procedure was developed. Within a transferable framework, the developed algorithm can be implemented for a set of remote sensing data among different investigation areas. Several case studies are the base for the retrieved results. Within a coarse dividing into statistical parts and the segmentation in meaningful objects, the framework is able to deal with different types of change. By means of an elaborated Temporal Change Index (TCI) only panchromatic datasets are used to extract areas which are destroyed, areas which were not affected and in addition areas where rebuilding has already started.

Learning a Transferable Change Rule from a Recurrent Neural Network for Land Cover Change Detection

Directory of Open Access Journals (Sweden)

Haobo Lyu

2016-06-01

Full Text Available When exploited in remote sensing analysis, a reliable change rule with transfer ability can detect changes accurately and be applied widely. However, in practice, the complexity of land cover changes makes it difficult to use only one change rule or change feature learned from a given multi-temporal dataset to detect any other new target images without applying other learning processes. In this study, we consider the design of an efficient change rule having transferability to detect both binary and multi-class changes. The proposed method relies on an improved Long Short-Term Memory (LSTM model to acquire and record the change information of long-term sequence remote sensing data. In particular, a core memory cell is utilized to learn the change rule from the information concerning binary changes or multi-class changes. Three gates are utilized to control the input, output and update of the LSTM model for optimization. In addition, the learned rule can be applied to detect changes and transfer the change rule from one learned image to another new target multi-temporal image. In this study, binary experiments, transfer experiments and multi-class change experiments are exploited to demonstrate the superiority of our method. Three contributions of this work can be summarized as follows: (1 the proposed method can learn an effective change rule to provide reliable change information for multi-temporal images; (2 the learned change rule has good transferability for detecting changes in new target images without any extra learning process, and the new target images should have a multi-spectral distribution similar to that of the training images; and (3 to the authors’ best knowledge, this is the first time that deep learning in recurrent neural networks is exploited for change detection. In addition, under the framework of the proposed method, changes can be detected under both binary detection and multi-class change detection.

Change Detection in Social Networks

National Research Council Canada - National Science Library

McCulloh, Ian; Webb, Matthew; Graham, John; Carley, Kathleen; Horn, Daniel B

2008-01-01

.... This project proposes a new method for detecting change in social networks over time, by applying a cumulative sum statistical process control statistic to normally distributed network measures...

Distance Measurement Methods for Improved Insider Threat Detection

Directory of Open Access Journals (Sweden)

Owen Lo

2018-01-01

Full Text Available Insider threats are a considerable problem within cyber security and it is often difficult to detect these threats using signature detection. Increasing machine learning can provide a solution, but these methods often fail to take into account changes of behaviour of users. This work builds on a published method of detecting insider threats and applies Hidden Markov method on a CERT data set (CERT r4.2 and analyses a number of distance vector methods (Damerau–Levenshtein Distance, Cosine Distance, and Jaccard Distance in order to detect changes of behaviour, which are shown to have success in determining different insider threats.

Metode za otkrivanje promjena kod daljinskih istraživanja : Methods for change detection in remote sensing

Directory of Open Access Journals (Sweden)

Admir Mulahusić

2011-03-01

Full Text Available U ovom radu predstavljeni su različiti načini identifikovanja promjena kod daljinskih istraživanja. Različiti autori su predstavljali različite metode otkrivanja promjena na površini zemlje. Otkrivanje promjena je, između ostalog, veoma važno zbog praćenja promjena, kao i procjene promjena i međusobnih odnosa prirodnih i vještačkih objekata. Sve to vodi ka boljem razumijevanju potencijalnih uzroka promjena. : In this paper, the different ways to identify changes in remote sensing are given. Various authors have presented different methods of detecting changes on the Earth's surface. Detection of changes, among other things, are very important for tracking changes, as well as assessment and evaluation of changes and interrelations of natural and artificial objects. All this leads to better understanding of potential causes of change.

A Review of the Detection Methods for Climate Regime Shifts

Directory of Open Access Journals (Sweden)

Qunqun Liu

2016-01-01

Full Text Available An abrupt climate change means that the climate system shifts from a steady state to another steady state. Study on the phenomenon and theory of the abrupt climate change is a new research field of modern climatology, and it is of great significance for the prediction of future climate change. The climate regime shift is one of the most common forms of abrupt climate change, which mainly refers to the statistical significant changes on the variable of climate system at one time scale. These detection methods can be roughly divided into five categories based on different types of abrupt changes, namely, abrupt mean value change, abrupt variance change, abrupt frequency change, abrupt probability density change, and the multivariable analysis. The main research progress of abrupt climate change detection methods is reviewed. What is more, some actual applications of those methods in observational data are provided. With the development of nonlinear science, many new methods have been presented for detecting an abrupt dynamic change in recent years, which is useful supplement for the abrupt change detection methods.

Adaptively detecting changes in Autonomic Grid Computing

KAUST Repository

Zhang, Xiangliang

2010-10-01

Detecting the changes is the common issue in many application fields due to the non-stationary distribution of the applicative data, e.g., sensor network signals, web logs and gridrunning logs. Toward Autonomic Grid Computing, adaptively detecting the changes in a grid system can help to alarm the anomalies, clean the noises, and report the new patterns. In this paper, we proposed an approach of self-adaptive change detection based on the Page-Hinkley statistic test. It handles the non-stationary distribution without the assumption of data distribution and the empirical setting of parameters. We validate the approach on the EGEE streaming jobs, and report its better performance on achieving higher accuracy comparing to the other change detection methods. Meanwhile this change detection process could help to discover the device fault which was not claimed in the system logs. © 2010 IEEE.

A simple and effective radiometric correction method to improve landscape change detection across sensors and across time

Science.gov (United States)

Chen, X.; Vierling, Lee; Deering, D.

2005-01-01

Satellite data offer unrivaled utility in monitoring and quantifying large scale land cover change over time. Radiometric consistency among collocated multi-temporal imagery is difficult to maintain, however, due to variations in sensor characteristics, atmospheric conditions, solar angle, and sensor view angle that can obscure surface change detection. To detect accurate landscape change using multi-temporal images, we developed a variation of the pseudoinvariant feature (PIF) normalization scheme: the temporally invariant cluster (TIC) method. Image data were acquired on June 9, 1990 (Landsat 4), June 20, 2000 (Landsat 7), and August 26, 2001 (Landsat 7) to analyze boreal forests near the Siberian city of Krasnoyarsk using the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and reduced simple ratio (RSR). The temporally invariant cluster (TIC) centers were identified via a point density map of collocated pixel VIs from the base image and the target image, and a normalization regression line was created to intersect all TIC centers. Target image VI values were then recalculated using the regression function so that these two images could be compared using the resulting common radiometric scale. We found that EVI was very indicative of vegetation structure because of its sensitivity to shadowing effects and could thus be used to separate conifer forests from deciduous forests and grass/crop lands. Conversely, because NDVI reduced the radiometric influence of shadow, it did not allow for distinctions among these vegetation types. After normalization, correlations of NDVI and EVI with forest leaf area index (LAI) field measurements combined for 2000 and 2001 were significantly improved; the r 2 values in these regressions rose from 0.49 to 0.69 and from 0.46 to 0.61, respectively. An EVI "cancellation effect" where EVI was positively related to understory greenness but negatively related to forest canopy coverage was evident across a

Transistor-based particle detection systems and methods

Science.gov (United States)

Jain, Ankit; Nair, Pradeep R.; Alam, Muhammad Ashraful

2015-06-09

Transistor-based particle detection systems and methods may be configured to detect charged and non-charged particles. Such systems may include a supporting structure contacting a gate of a transistor and separating the gate from a dielectric of the transistor, and the transistor may have a near pull-in bias and a sub-threshold region bias to facilitate particle detection. The transistor may be configured to change current flow through the transistor in response to a change in stiffness of the gate caused by securing of a particle to the gate, and the transistor-based particle detection system may configured to detect the non-charged particle at least from the change in current flow.

Adaptive 4d Psi-Based Change Detection

Science.gov (United States)

Yang, Chia-Hsiang; Soergel, Uwe

2018-04-01

In a previous work, we proposed a PSI-based 4D change detection to detect disappearing and emerging PS points (3D) along with their occurrence dates (1D). Such change points are usually caused by anthropic events, e.g., building constructions in cities. This method first divides an entire SAR image stack into several subsets by a set of break dates. The PS points, which are selected based on their temporal coherences before or after a break date, are regarded as change candidates. Change points are then extracted from these candidates according to their change indices, which are modelled from their temporal coherences of divided image subsets. Finally, we check the evolution of the change indices for each change point to detect the break date that this change occurred. The experiment validated both feasibility and applicability of our method. However, two questions still remain. First, selection of temporal coherence threshold associates with a trade-off between quality and quantity of PS points. This selection is also crucial for the amount of change points in a more complex way. Second, heuristic selection of change index thresholds brings vulnerability and causes loss of change points. In this study, we adapt our approach to identify change points based on statistical characteristics of change indices rather than thresholding. The experiment validates this adaptive approach and shows increase of change points compared with the old version. In addition, we also explore and discuss optimal selection of temporal coherence threshold.

Detection and Attribution of Anthropogenic Climate Change Impacts

Science.gov (United States)

Rosenzweig, Cynthia; Neofotis, Peter

2013-01-01

Human-influenced climate change is an observed phenomenon affecting physical and biological systems across the globe. The majority of observed impacts are related to temperature changes and are located in the northern high- and midlatitudes. However, new evidence is emerging that demonstrates that impacts are related to precipitation changes as well as temperature, and that climate change is impacting systems and sectors beyond the Northern Hemisphere. In this paper, we highlight some of this new evidence-focusing on regions and sectors that the Intergovernmental Panel on Climate Change Fourth Assessment Report (IPCC AR4) noted as under-represented-in the context of observed climate change impacts, direct and indirect drivers of change (including carbon dioxide itself), and methods of detection. We also present methods and studies attributing observed impacts to anthropogenic forcing. We argue that the expansion of methods of detection (in terms of a broader array of climate variables and data sources, inclusion of the major modes of climate variability, and incorporation of other drivers of change) is key to discerning the climate sensitivities of sectors and systems in regions where the impacts of climate change currently remain elusive. Attributing such changes to human forcing of the climate system, where possible, is important for development of effective mitigation and adaptation. Current challenges in documenting adaptation and the role of indigenous knowledge in detection and attribution are described.

MULTI-TEMPORAL CLASSIFICATION AND CHANGE DETECTION USING UAV IMAGES

Directory of Open Access Journals (Sweden)

S. Makuti

2018-05-01

Full Text Available In this paper different methodologies for the classification and change detection of UAV image blocks are explored. UAV is not only the cheapest platform for image acquisition but it is also the easiest platform to operate in repeated data collections over a changing area like a building construction site. Two change detection techniques have been evaluated in this study: the pre-classification and the post-classification algorithms. These methods are based on three main steps: feature extraction, classification and change detection. A set of state of the art features have been used in the tests: colour features (HSV, textural features (GLCM and 3D geometric features. For classification purposes Conditional Random Field (CRF has been used: the unary potential was determined using the Random Forest algorithm while the pairwise potential was defined by the fully connected CRF. In the performed tests, different feature configurations and settings have been considered to assess the performance of these methods in such challenging task. Experimental results showed that the post-classification approach outperforms the pre-classification change detection method. This was analysed using the overall accuracy, where by post classification have an accuracy of up to 62.6 % and the pre classification change detection have an accuracy of 46.5 %. These results represent a first useful indication for future works and developments.

Comparison of feature extraction methods within a spatio-temporal land cover change detection framework

CSIR Research Space (South Africa)

Kleynhans, W

2011-07-01

Full Text Available OF FEATURE EXTRACTION METHODS WITHIN A SPATIO-TEMPORAL LAND COVER CHANGE DETECTION FRAMEWORK ??W. Kleynhans,, ??B.P. Salmon, ?J.C. Olivier, ?K.J. Wessels, ?F. van den Bergh ? Electrical, Electronic and Computer Engi- neering University of Pretoria, South... Bergh, and K. Steenkamp, ?Improving land cover class separation using an extended Kalman filter on MODIS NDVI time series data,? IEEE Geoscience and Remote Sensing Letters, vol. 7, no. 2, pp. 381?385, Apr. 2010. ...

A Doubly Stochastic Change Point Detection Algorithm for Noisy Biological Signals

Directory of Open Access Journals (Sweden)

Nathan Gold

2018-01-01

Full Text Available Experimentally and clinically collected time series data are often contaminated with significant confounding noise, creating short, noisy time series. This noise, due to natural variability and measurement error, poses a challenge to conventional change point detection methods. We propose a novel and robust statistical method for change point detection for noisy biological time sequences. Our method is a significant improvement over traditional change point detection methods, which only examine a potential anomaly at a single time point. In contrast, our method considers all suspected anomaly points and considers the joint probability distribution of the number of change points and the elapsed time between two consecutive anomalies. We validate our method with three simulated time series, a widely accepted benchmark data set, two geological time series, a data set of ECG recordings, and a physiological data set of heart rate variability measurements of fetal sheep model of human labor, comparing it to three existing methods. Our method demonstrates significantly improved performance over the existing point-wise detection methods.

Graph-Theoretic Statistical Methods for Detecting and Localizing Distributional Change in Multivariate Data

Science.gov (United States)

2015-06-01

context of regression. Tran, Gaber , and Sattler (2014) describe recent change-detection efforts as applied to streaming data. -2 -1 0 1 2 3 4 -2 -1 0 1 Y...human monitors: A signal detection analysis. Human-Computer Interaction, 1(1), 49–75. Tran, D. H., Gaber , M. M., & Sattler, K. U. (2014). Change

Advancing Research Methods to Detect Impact of Climate Change on Health in Grand'Anse, Haiti

Science.gov (United States)

Barnhart, S.; Coq, R. N.; Frederic, R.; DeRiel, E.; Camara, H.; Barnhart, K. R.

2013-12-01

Haiti is considered particularly vulnerable to the effects of climate change, but directly linking climate change to health effects is limited by the lack of robust data and the multiple determinants of health. Worsening storms and rising temperatures in this rugged country with high poverty is likely to adversely affect economic activity, population growth and other determinants of health. For the past two years, the Univ. of Washington has supported the public hospital in the department of Grand'Anse. Grand'Anse, a relatively contained region in SW Haiti with an area of 11,912 km2, is predominantly rural with a population of 350,000 and is bounded to the south by peaks up to 2,347 m. Grand'Anse would serve as an excellent site to assess the interface between climate change and health. The Demographic and Health Survey (DHS) shows health status is low relative to other countries. Estimates of climate change for Jeremie, the largest city in Grand'Anse, predict the mean monthly temperature will increase from 26.1 to 27.3 oC while mean monthly rainfall will decrease from 80.5 to 73.5 mm over the next 60 years. The potential impact of these changes ranges from threatening food security to greater mortality. Use of available secondary data such as indicators of climate change and DHS health status are not likely to offer sufficient resolution to detect positive or negative impacts of climate change on health. How might a mixed methods approach incorporating secondary data and quantitative and qualitative survey data on climate, economic activity, health and determinants of health address the hypothesis: Climate change does not adversely affect health? For example, in Haiti most women deliver at home. Maternal mortality is high at 350 deaths/100,000 deliveries. This compares to deliveries in facilities where the median rate is less than 100/100,000. Thus, maternal mortality is closely linked to access to health care in this rugged mountainous country. Climate change

UPDATING NATIONAL TOPOGRAPHIC DATA BASE USING CHANGE DETECTION METHODS

Directory of Open Access Journals (Sweden)

E. Keinan

2016-06-01

Full Text Available The traditional method for updating a topographic database on a national scale is a complex process that requires human resources, time and the development of specialized procedures. In many National Mapping and Cadaster Agencies (NMCA, the updating cycle takes a few years. Today, the reality is dynamic and the changes occur every day, therefore, the users expect that the existing database will portray the current reality. Global mapping projects which are based on community volunteers, such as OSM, update their database every day based on crowdsourcing. In order to fulfil user's requirements for rapid updating, a new methodology that maps major interest areas while preserving associated decoding information, should be developed. Until recently, automated processes did not yield satisfactory results, and a typically process included comparing images from different periods. The success rates in identifying the objects were low, and most were accompanied by a high percentage of false alarms. As a result, the automatic process required significant editorial work that made it uneconomical. In the recent years, the development of technologies in mapping, advancement in image processing algorithms and computer vision, together with the development of digital aerial cameras with NIR band and Very High Resolution satellites, allow the implementation of a cost effective automated process. The automatic process is based on high-resolution Digital Surface Model analysis, Multi Spectral (MS classification, MS segmentation, object analysis and shape forming algorithms. This article reviews the results of a novel change detection methodology as a first step for updating NTDB in the Survey of Israel.

Updating National Topographic Data Base Using Change Detection Methods

Science.gov (United States)

Keinan, E.; Felus, Y. A.; Tal, Y.; Zilberstien, O.; Elihai, Y.

2016-06-01

The traditional method for updating a topographic database on a national scale is a complex process that requires human resources, time and the development of specialized procedures. In many National Mapping and Cadaster Agencies (NMCA), the updating cycle takes a few years. Today, the reality is dynamic and the changes occur every day, therefore, the users expect that the existing database will portray the current reality. Global mapping projects which are based on community volunteers, such as OSM, update their database every day based on crowdsourcing. In order to fulfil user's requirements for rapid updating, a new methodology that maps major interest areas while preserving associated decoding information, should be developed. Until recently, automated processes did not yield satisfactory results, and a typically process included comparing images from different periods. The success rates in identifying the objects were low, and most were accompanied by a high percentage of false alarms. As a result, the automatic process required significant editorial work that made it uneconomical. In the recent years, the development of technologies in mapping, advancement in image processing algorithms and computer vision, together with the development of digital aerial cameras with NIR band and Very High Resolution satellites, allow the implementation of a cost effective automated process. The automatic process is based on high-resolution Digital Surface Model analysis, Multi Spectral (MS) classification, MS segmentation, object analysis and shape forming algorithms. This article reviews the results of a novel change detection methodology as a first step for updating NTDB in the Survey of Israel.

Remote sensing change detection methods to track deforestation and growth in threatened rainforests in Madre de Dios, Peru

Science.gov (United States)

Shermeyer, Jacob S.; Haack, Barry N.

2015-01-01

Two forestry-change detection methods are described, compared, and contrasted for estimating deforestation and growth in threatened forests in southern Peru from 2000 to 2010. The methods used in this study rely on freely available data, including atmospherically corrected Landsat 5 Thematic Mapper and Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation continuous fields (VCF). The two methods include a conventional supervised signature extraction method and a unique self-calibrating method called MODIS VCF guided forest/nonforest (FNF) masking. The process chain for each of these methods includes a threshold classification of MODIS VCF, training data or signature extraction, signature evaluation, k-nearest neighbor classification, analyst-guided reclassification, and postclassification image differencing to generate forest change maps. Comparisons of all methods were based on an accuracy assessment using 500 validation pixels. Results of this accuracy assessment indicate that FNF masking had a 5% higher overall accuracy and was superior to conventional supervised classification when estimating forest change. Both methods succeeded in classifying persistently forested and nonforested areas, and both had limitations when classifying forest change.

Detection of cardiac activity changes from human speech

Science.gov (United States)

Tovarek, Jaromir; Partila, Pavol; Voznak, Miroslav; Mikulec, Martin; Mehic, Miralem

2015-05-01

Impact of changes in blood pressure and pulse from human speech is disclosed in this article. The symptoms of increased physical activity are pulse, systolic and diastolic pressure. There are many methods of measuring and indicating these parameters. The measurements must be carried out using devices which are not used in everyday life. In most cases, the measurement of blood pressure and pulse following health problems or other adverse feelings. Nowadays, research teams are trying to design and implement modern methods in ordinary human activities. The main objective of the proposal is to reduce the delay between detecting the adverse pressure and to the mentioned warning signs and feelings. Common and frequent activity of man is speaking, while it is known that the function of the vocal tract can be affected by the change in heart activity. Therefore, it can be a useful parameter for detecting physiological changes. A method for detecting human physiological changes by speech processing and artificial neural network classification is described in this article. The pulse and blood pressure changes was induced by physical exercises in this experiment. The set of measured subjects was formed by ten healthy volunteers of both sexes. None of the subjects was a professional athlete. The process of the experiment was divided into phases before, during and after physical training. Pulse, systolic, diastolic pressure was measured and voice activity was recorded after each of them. The results of this experiment describe a method for detecting increased cardiac activity from human speech using artificial neural network.

a Landsat Time-Series Stacks Model for Detection of Cropland Change

Science.gov (United States)

Chen, J.; Chen, J.; Zhang, J.

2017-09-01

Global, timely, accurate and cost-effective cropland monitoring with a fine spatial resolution will dramatically improve our understanding of the effects of agriculture on greenhouse gases emissions, food safety, and human health. Time-series remote sensing imagery have been shown particularly potential to describe land cover dynamics. The traditional change detection techniques are often not capable of detecting land cover changes within time series that are severely influenced by seasonal difference, which are more likely to generate pseuso changes. Here,we introduced and tested LTSM ( Landsat time-series stacks model), an improved Continuous Change Detection and Classification (CCDC) proposed previously approach to extract spectral trajectories of land surface change using a dense Landsat time-series stacks (LTS). The method is expected to eliminate pseudo changes caused by phenology driven by seasonal patterns. The main idea of the method is that using all available Landsat 8 images within a year, LTSM consisting of two term harmonic function are estimated iteratively for each pixel in each spectral band .LTSM can defines change area by differencing the predicted and observed Landsat images. The LTSM approach was compared with change vector analysis (CVA) method. The results indicated that the LTSM method correctly detected the "true change" without overestimating the "false" one, while CVA pointed out "true change" pixels with a large number of "false changes". The detection of change areas achieved an overall accuracy of 92.37 %, with a kappa coefficient of 0.676.

Convolutional neural network features based change detection in satellite images

Science.gov (United States)

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

2016-07-01

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

Automated baseline change detection phase I. Final report

Energy Technology Data Exchange (ETDEWEB)

NONE

1995-12-01

The Automated Baseline Change Detection (ABCD) project is supported by the DOE Morgantown Energy Technology Center (METC) as part of its ER&WM cross-cutting technology program in robotics. Phase 1 of the Automated Baseline Change Detection project is summarized in this topical report. The primary objective of this project is to apply robotic and optical sensor technology to the operational inspection of mixed toxic and radioactive waste stored in barrels, using Automated Baseline Change Detection (ABCD), based on image subtraction. Absolute change detection is based on detecting any visible physical changes, regardless of cause, between a current inspection image of a barrel and an archived baseline image of the same barrel. Thus, in addition to rust, the ABCD system can also detect corrosion, leaks, dents, and bulges. The ABCD approach and method rely on precise camera positioning and repositioning relative to the barrel and on feature recognition in images. In support of this primary objective, there are secondary objectives to determine DOE operational inspection requirements and DOE system fielding requirements.

Automated baseline change detection phase I. Final report

International Nuclear Information System (INIS)

1995-12-01

The Automated Baseline Change Detection (ABCD) project is supported by the DOE Morgantown Energy Technology Center (METC) as part of its ER ampersand WM cross-cutting technology program in robotics. Phase 1 of the Automated Baseline Change Detection project is summarized in this topical report. The primary objective of this project is to apply robotic and optical sensor technology to the operational inspection of mixed toxic and radioactive waste stored in barrels, using Automated Baseline Change Detection (ABCD), based on image subtraction. Absolute change detection is based on detecting any visible physical changes, regardless of cause, between a current inspection image of a barrel and an archived baseline image of the same barrel. Thus, in addition to rust, the ABCD system can also detect corrosion, leaks, dents, and bulges. The ABCD approach and method rely on precise camera positioning and repositioning relative to the barrel and on feature recognition in images. In support of this primary objective, there are secondary objectives to determine DOE operational inspection requirements and DOE system fielding requirements

Improving features used for hyper-temporal land cover change detection by reducing the uncertainty in the feature extraction method

CSIR Research Space (South Africa)

Salmon, BP

2017-07-01

Full Text Available the effect which the length of a temporal sliding window has on the success of detecting land cover change. It is shown using a short Fourier transform as a feature extraction method provides meaningful robust input to a machine learning method. In theory...

Regularisation in multi- and hyperspectral remote sensing change detection

DEFF Research Database (Denmark)

Nielsen, Allan Aasbjerg

2005-01-01

Change detection methods for multi- and hypervariate data look for differences in data acquired over the same area at different points in time. These differences may be due to noise or differences in (atmospheric etc.) conditions at the two acquisition time points. To prevent a change detection m...

A comparison of moving object detection methods for real-time moving object detection

Science.gov (United States)

Roshan, Aditya; Zhang, Yun

2014-06-01

Moving object detection has a wide variety of applications from traffic monitoring, site monitoring, automatic theft identification, face detection to military surveillance. Many methods have been developed across the globe for moving object detection, but it is very difficult to find one which can work globally in all situations and with different types of videos. The purpose of this paper is to evaluate existing moving object detection methods which can be implemented in software on a desktop or laptop, for real time object detection. There are several moving object detection methods noted in the literature, but few of them are suitable for real time moving object detection. Most of the methods which provide for real time movement are further limited by the number of objects and the scene complexity. This paper evaluates the four most commonly used moving object detection methods as background subtraction technique, Gaussian mixture model, wavelet based and optical flow based methods. The work is based on evaluation of these four moving object detection methods using two (2) different sets of cameras and two (2) different scenes. The moving object detection methods have been implemented using MatLab and results are compared based on completeness of detected objects, noise, light change sensitivity, processing time etc. After comparison, it is observed that optical flow based method took least processing time and successfully detected boundary of moving objects which also implies that it can be implemented for real-time moving object detection.

Detecting land cover change using a sliding window temporal autocorrelation approach

CSIR Research Space (South Africa)

Kleynhans, W

2012-07-01

Full Text Available There has been recent developments in the use of hypertemporal satellite time series data for land cover change detection and classification. Recently, an Autocorrelation function (ACF) change detection method was proposed to detect the development...

Saliency predicts change detection in pictures of natural scenes.

Science.gov (United States)

Wright, Michael J

2005-01-01

It has been proposed that the visual system encodes the salience of objects in the visual field in an explicit two-dimensional map that guides visual selective attention. Experiments were conducted to determine whether salience measurements applied to regions of pictures of outdoor scenes could predict the detection of changes in those regions. To obtain a quantitative measure of change detection, observers located changes in pairs of colour pictures presented across an interstimulus interval (ISI). Salience measurements were then obtained from different observers for image change regions using three independent methods, and all were positively correlated with change detection. Factor analysis extracted a single saliency factor that accounted for 62% of the variance contained in the four measures. Finally, estimates of the magnitude of the image change in each picture pair were obtained, using nine separate visual filters representing low-level vision features (luminance, colour, spatial frequency, orientation, edge density). None of the feature outputs was significantly associated with change detection or saliency. On the other hand it was shown that high-level (structural) properties of the changed region were related to saliency and to change detection: objects were more salient than shadows and more detectable when changed.

Multivariate methods for the detection of greenhouse-gas-induced climate change

International Nuclear Information System (INIS)

Santer, B.D.; Wigley, T.M.L.; Jones, P.D.; Schlesinger, M.E.

1990-01-01

This investigation considers whether observed changes in surface air temperature are consistent with GCM equilibrium response predictions for a doubling of atmospheric CO 2 . The model considered is a version of the Oregon State University (OSU) atmospheric general circulation model (AGCM). The study consists of three stages. In the first stage the authors examine the spatial structure of changes in the annual mean and annual cycle for surface air temperature, mean sea-level pressure (SLP) and precipitation rate. Signal-to-noise (S/N) ratios or equivalent test statistics are then computed (using the 1 x CO 2 and 2 x CO 2 data) in order to identify variables most useful for detection purposes. Changes in both means and variances are considered as possible detection parameters. The highest S/N ratios are obtained for annual-mean and winter surface air temperature, and the lowest S/N ratios are obtained for SLP. There are significant increases in the temporal and spatial variability of precipitation, and significant decreases in the temporal and spatial variability of surface air temperature

AN INVESTIGATION OF AUTOMATIC CHANGE DETECTION FOR TOPOGRAPHIC MAP UPDATING

Directory of Open Access Journals (Sweden)

P. Duncan

2012-08-01

Full Text Available Changes to the landscape are constantly occurring and it is essential for geospatial and mapping organisations that these changes are regularly detected and captured, so that map databases can be updated to reflect the current status of the landscape. The Chief Directorate of National Geospatial Information (CD: NGI, South Africa's national mapping agency, currently relies on manual methods of detecting changes and capturing these changes. These manual methods are time consuming and labour intensive, and rely on the skills and interpretation of the operator. It is therefore necessary to move towards more automated methods in the production process at CD: NGI. The aim of this research is to do an investigation into a methodology for automatic or semi-automatic change detection for the purpose of updating topographic databases. The method investigated for detecting changes is through image classification as well as spatial analysis and is focussed on urban landscapes. The major data input into this study is high resolution aerial imagery and existing topographic vector data. Initial results indicate the traditional pixel-based image classification approaches are unsatisfactory for large scale land-use mapping and that object-orientated approaches hold more promise. Even in the instance of object-oriented image classification generalization of techniques on a broad-scale has provided inconsistent results. A solution may lie with a hybrid approach of pixel and object-oriented techniques.

[Application of optical flow dynamic texture in land use/cover change detection].

Science.gov (United States)

Yan, Li; Gong, Yi-Long; Zhang, Yi; Duan, Wei

2014-11-01

In the present study, a novel change detection approach for high resolution remote sensing images is proposed based on the optical flow dynamic texture (OFDT), which could achieve the land use & land cover change information automatically with a dynamic description of ground-object changes. This paper describes the ground-object gradual change process from the principle using optical flow theory, which breaks the ground-object sudden change hypothesis in remote sensing change detection methods in the past. As the steps of this method are simple, it could be integrated in the systems and software such as Land Resource Management and Urban Planning software that needs to find ground-object changes. This method takes into account the temporal dimension feature between remote sensing images, which provides a richer set of information for remote sensing change detection, thereby improving the status that most of the change detection methods are mainly dependent on the spatial dimension information. In this article, optical flow dynamic texture is the basic reflection of changes, and it is used in high resolution remote sensing image support vector machine post-classification change detection, combined with spectral information. The texture in the temporal dimension which is considered in this article has a smaller amount of data than most of the textures in the spatial dimensions. The highly automated texture computing has only one parameter to set, which could relax the onerous manual evaluation present status. The effectiveness of the proposed approach is evaluated with the 2011 and 2012 QuickBird datasets covering Duerbert Mongolian Autonomous County of Daqing City, China. Then, the effects of different optical flow smooth coefficient and the impact on the description of the ground-object changes in the method are deeply analyzed: The experiment result is satisfactory, with an 87.29% overall accuracy and an 0.850 7 Kappa index, and the method achieves better

Change detection of medical images using dictionary learning techniques and PCA

Science.gov (United States)

Nika, Varvara; Babyn, Paul; Zhu, Hongmei

2014-03-01

Automatic change detection methods for identifying the changes of serial MR images taken at different times are of great interest to radiologists. The majority of existing change detection methods in medical imaging, and those of brain images in particular, include many preprocessing steps and rely mostly on statistical analysis of MRI scans. Although most methods utilize registration software, tissue classification remains a difficult and overwhelming task. Recently, dictionary learning techniques are used in many areas of image processing, such as image surveillance, face recognition, remote sensing, and medical imaging. In this paper we present the Eigen-Block Change Detection algorithm (EigenBlockCD). It performs local registration and identifies the changes between consecutive MR images of the brain. Blocks of pixels from baseline scan are used to train local dictionaries that are then used to detect changes in the follow-up scan. We use PCA to reduce the dimensionality of the local dictionaries and the redundancy of data. Choosing the appropriate distance measure significantly affects the performance of our algorithm. We examine the differences between L1 and L2 norms as two possible similarity measures in the EigenBlockCD. We show the advantages of L2 norm over L1 norm theoretically and numerically. We also demonstrate the performance of the EigenBlockCD algorithm for detecting changes of MR images and compare our results with those provided in recent literature. Experimental results with both simulated and real MRI scans show that the EigenBlockCD outperforms the previous methods. It detects clinical changes while ignoring the changes due to patient's position and other acquisition artifacts.

Detecting and Attributing Health Burdens to Climate Change.

Science.gov (United States)

Ebi, Kristie L; Ogden, Nicholas H; Semenza, Jan C; Woodward, Alistair

2017-08-07

Detection and attribution of health impacts caused by climate change uses formal methods to determine a ) whether the occurrence of adverse health outcomes has changed, and b ) the extent to which that change could be attributed to climate change. There have been limited efforts to undertake detection and attribution analyses in health. Our goal was to show a range of approaches for conducting detection and attribution analyses. Case studies for heatwaves, Lyme disease in Canada, and Vibrio emergence in northern Europe highlight evidence that climate change is adversely affecting human health. Changes in rates and geographic distribution of adverse health outcomes were detected, and, in each instance, a proportion of the observed changes could, in our judgment, be attributed to changes in weather patterns associated with climate change. The results of detection and attribution studies can inform evidence-based risk management to reduce current, and plan for future, changes in health risks associated with climate change. Gaining a better understanding of the size, timing, and distribution of the climate change burden of disease and injury requires reliable long-term data sets, more knowledge about the factors that confound and modify the effects of climate on health, and refinement of analytic techniques for detection and attribution. At the same time, significant advances are possible in the absence of complete data and statistical certainty: there is a place for well-informed judgments, based on understanding of underlying processes and matching of patterns of health, climate, and other determinants of human well-being. https://doi.org/10.1289/EHP1509.

A pdf-Free Change Detection Test Based on Density Difference Estimation.

Science.gov (United States)

Bu, Li; Alippi, Cesare; Zhao, Dongbin

2018-02-01

The ability to detect online changes in stationarity or time variance in a data stream is a hot research topic with striking implications. In this paper, we propose a novel probability density function-free change detection test, which is based on the least squares density-difference estimation method and operates online on multidimensional inputs. The test does not require any assumption about the underlying data distribution, and is able to operate immediately after having been configured by adopting a reservoir sampling mechanism. Thresholds requested to detect a change are automatically derived once a false positive rate is set by the application designer. Comprehensive experiments validate the effectiveness in detection of the proposed method both in terms of detection promptness and accuracy.

Changing change detection

DEFF Research Database (Denmark)

Kyllingsbæk, Søren; Bundesen, Claus

2009-01-01

The change detection paradigm is a popular way of measuring visual short-term memory capacity. Using the paradigm, researchers have found evidence for a capacity of about four independent visual objects, confirming classic estimates that were based on the number of items that could be reported...

Image Fusion-Based Land Cover Change Detection Using Multi-Temporal High-Resolution Satellite Images

Directory of Open Access Journals (Sweden)

Biao Wang

2017-08-01

Full Text Available Change detection is usually treated as a problem of explicitly detecting land cover transitions in satellite images obtained at different times, and helps with emergency response and government management. This study presents an unsupervised change detection method based on the image fusion of multi-temporal images. The main objective of this study is to improve the accuracy of unsupervised change detection from high-resolution multi-temporal images. Our method effectively reduces change detection errors, since spatial displacement and spectral differences between multi-temporal images are evaluated. To this end, a total of four cross-fused images are generated with multi-temporal images, and the iteratively reweighted multivariate alteration detection (IR-MAD method—a measure for the spectral distortion of change information—is applied to the fused images. In this experiment, the land cover change maps were extracted using multi-temporal IKONOS-2, WorldView-3, and GF-1 satellite images. The effectiveness of the proposed method compared with other unsupervised change detection methods is demonstrated through experimentation. The proposed method achieved an overall accuracy of 80.51% and 97.87% for cases 1 and 2, respectively. Moreover, the proposed method performed better when differentiating the water area from the vegetation area compared to the existing change detection methods. Although the water area beneath moderate and sparse vegetation canopy was captured, vegetation cover and paved regions of the water body were the main sources of omission error, and commission errors occurred primarily in pixels of mixed land use and along the water body edge. Nevertheless, the proposed method, in conjunction with high-resolution satellite imagery, offers a robust and flexible approach to land cover change mapping that requires no ancillary data for rapid implementation.

Variable threshold method for ECG R-peak detection.

Science.gov (United States)

Kew, Hsein-Ping; Jeong, Do-Un

2011-10-01

In this paper, a wearable belt-type ECG electrode worn around the chest by measuring the real-time ECG is produced in order to minimize the inconvenient in wearing. ECG signal is detected using a potential instrument system. The measured ECG signal is transmits via an ultra low power consumption wireless data communications unit to personal computer using Zigbee-compatible wireless sensor node. ECG signals carry a lot of clinical information for a cardiologist especially the R-peak detection in ECG. R-peak detection generally uses the threshold value which is fixed. There will be errors in peak detection when the baseline changes due to motion artifacts and signal size changes. Preprocessing process which includes differentiation process and Hilbert transform is used as signal preprocessing algorithm. Thereafter, variable threshold method is used to detect the R-peak which is more accurate and efficient than fixed threshold value method. R-peak detection using MIT-BIH databases and Long Term Real-Time ECG is performed in this research in order to evaluate the performance analysis.

HMCan-diff: a method to detect changes in histone modifications in cells with different genetic characteristics

KAUST Repository

Ashoor, Haitham

2016-12-19

Comparing histone modification profiles between cancer and normal states, or across different tumor samples, can provide insights into understanding cancer initiation, progression and response to therapy. ChIP-seq histone modification data of cancer samples are distorted by copy number variation innate to any cancer cell. We present HMCan-diff, the first method designed to analyze ChIP-seq data to detect changes in histone modifications between two cancer samples of different genetic backgrounds, or between a cancer sample and a normal control. HMCan-diff explicitly corrects for copy number bias, and for other biases in the ChIP-seq data, which significantly improves prediction accuracy compared to methods that do not consider such corrections. On in silico simulated ChIP-seq data generated using genomes with differences in copy number profiles, HMCan-diff shows a much better performance compared to other methods that have no correction for copy number bias. Additionally, we benchmarked HMCan-diff on four experimental datasets, characterizing two histone marks in two different scenarios. We correlated changes in histone modifications between a cancer and a normal control sample with changes in gene expression. On all experimental datasets, HMCan-diff demonstrated better performance compared to the other methods.

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)

Graph-based structural change detection for rotating machinery monitoring

Science.gov (United States)

Lu, Guoliang; Liu, Jie; Yan, Peng

2018-01-01

Detection of structural changes is critically important in operational monitoring of a rotating machine. This paper presents a novel framework for this purpose, where a graph model for data modeling is adopted to represent/capture statistical dynamics in machine operations. Meanwhile we develop a numerical method for computing temporal anomalies in the constructed graphs. The martingale-test method is employed for the change detection when making decisions on possible structural changes, where excellent performance is demonstrated outperforming exciting results such as the autoregressive-integrated-moving average (ARIMA) model. Comprehensive experimental results indicate good potentials of the proposed algorithm in various engineering applications. This work is an extension of a recent result (Lu et al., 2017).

Short-Term Change Detection in Wetlands Using Sentinel-1 Time Series

DEFF Research Database (Denmark)

Muro, Javier; Canty, Morton; Conradsen, Knut

2016-01-01

Automated monitoring systems that can capture wetlands’ high spatial and temporal variability are essential for their management. SAR-based change detection approaches offer a great opportunity to enhance our understanding of complex and dynamic ecosystems. We test a recently-developed time serie...... certain landscape changes are detected only by either the Landsat-based or the S1-omnibus method. The S1-omnibus method shows a great potential for an automated monitoring of short time changes and accurate delineation of areas of high variability and of slow and gradual changes....

A PCA-Based Change Detection Framework for Multidimensional Data Streams

KAUST Repository

Qahtan, Abdulhakim Ali Ali

2015-08-10

Detecting changes in multidimensional data streams is an important and challenging task. In unsupervised change detection, changes are usually detected by comparing the distribution in a current (test) window with a reference window. It is thus essential to design divergence metrics and density estimators for comparing the data distributions, which are mostly done for univariate data. Detecting changes in multidimensional data streams brings difficulties to the density estimation and comparisons. In this paper, we propose a framework for detecting changes in multidimensional data streams based on principal component analysis, which is used for projecting data into a lower dimensional space, thus facilitating density estimation and change-score calculations. The proposed framework also has advantages over existing approaches by reducing computational costs with an efficient density estimator, promoting the change-score calculation by introducing effective divergence metrics, and by minimizing the efforts required from users on the threshold parameter setting by using the Page-Hinkley test. The evaluation results on synthetic and real data show that our framework outperforms two baseline methods in terms of both detection accuracy and computational costs.

Change detection for synthetic aperture radar images based on pattern and intensity distinctiveness analysis

Science.gov (United States)

Wang, Xiao; Gao, Feng; Dong, Junyu; Qi, Qiang

2018-04-01

Synthetic aperture radar (SAR) image is independent on atmospheric conditions, and it is the ideal image source for change detection. Existing methods directly analysis all the regions in the speckle noise contaminated difference image. The performance of these methods is easily affected by small noisy regions. In this paper, we proposed a novel change detection framework for saliency-guided change detection based on pattern and intensity distinctiveness analysis. The saliency analysis step can remove small noisy regions, and therefore makes the proposed method more robust to the speckle noise. In the proposed method, the log-ratio operator is first utilized to obtain a difference image (DI). Then, the saliency detection method based on pattern and intensity distinctiveness analysis is utilized to obtain the changed region candidates. Finally, principal component analysis and k-means clustering are employed to analysis pixels in the changed region candidates. Thus, the final change map can be obtained by classifying these pixels into changed or unchanged class. The experiment results on two real SAR images datasets have demonstrated the effectiveness of the proposed method.

Detecting Output Pressure Change of Positive-Displacement Pump by Phase Trajectory Method

Directory of Open Access Journals (Sweden)

Jerzy Stojek

2010-06-01

Full Text Available The monitoring of hydraulic system condition change during its exploitation ran its complex problem. The main task is to identifyearly phase damage of hydraulic system elements (pumps, valves, ect. in order to take decision which can avoid hydraulic system breakdown. This paper presents the possibility of phase trajectories use in detecting output pressure change of hydraulic system causedby positive-displacement pump wear.

Towards a Framework for Change Detection in Data Sets

Science.gov (United States)

Böttcher, Mirko; Nauck, Detlef; Ruta, Dymitr; Spott, Martin

Since the world with its markets, innovations and customers is changing faster than ever before, the key to survival for businesses is the ability to detect, assess and respond to changing conditions rapidly and intelligently. Discovering changes and reacting to or acting upon them before others do has therefore become a strategical issue for many companies. However, existing data analysis techniques are insufflent for this task since they typically assume that the domain under consideration is stable over time. This paper presents a framework that detects changes within a data set at virtually any level of granularity. The underlying idea is to derive a rule-based description of the data set at different points in time and to subsequently analyse how these rules change. Nevertheless, further techniques are required to assist the data analyst in interpreting and assessing their changes. Therefore the framework also contains methods to discard rules that are non-drivers for change and to assess the interestingness of detected changes.

Multiscale-Driven approach to detecting change in Synthetic Aperture Radar (SAR) imagery

Science.gov (United States)

Gens, R.; Hogenson, K.; Ajadi, O. A.; Meyer, F. J.; Myers, A.; Logan, T. A.; Arnoult, K., Jr.

2017-12-01

Detecting changes between Synthetic Aperture Radar (SAR) images can be a useful but challenging exercise. SAR with its all-weather capabilities can be an important resource in identifying and estimating the expanse of events such as flooding, river ice breakup, earthquake damage, oil spills, and forest growth, as it can overcome shortcomings of optical methods related to cloud cover. However, detecting change in SAR imagery can be impeded by many factors including speckle, complex scattering responses, low temporal sampling, and difficulty delineating boundaries. In this presentation we use a change detection method based on a multiscale-driven approach. By using information at different resolution levels, we attempt to obtain more accurate change detection maps in both heterogeneous and homogeneous regions. Integrated within the processing flow are processes that 1) improve classification performance by combining Expectation-Maximization algorithms with mathematical morphology, 2) achieve high accuracy in preserving boundaries using measurement level fusion techniques, and 3) combine modern non-local filtering and 2D-discrete stationary wavelet transform to provide robustness against noise. This multiscale-driven approach to change detection has recently been incorporated into the Alaska Satellite Facility (ASF) Hybrid Pluggable Processing Pipeline (HyP3) using radiometrically terrain corrected SAR images. Examples primarily from natural hazards are presented to illustrate the capabilities and limitations of the change detection method.

Imaging, object detection, and change detection with a polarized multistatic GPR array

Science.gov (United States)

Beer, N. Reginald; Paglieroni, David W.

2015-07-21

A polarized detection system performs imaging, object detection, and change detection factoring in the orientation of an object relative to the orientation of transceivers. The polarized detection system may operate on one of several modes of operation based on whether the imaging, object detection, or change detection is performed separately for each transceiver orientation. In combined change mode, the polarized detection system performs imaging, object detection, and change detection separately for each transceiver orientation, and then combines changes across polarizations. In combined object mode, the polarized detection system performs imaging and object detection separately for each transceiver orientation, and then combines objects across polarizations and performs change detection on the result. In combined image mode, the polarized detection system performs imaging separately for each transceiver orientation, and then combines images across polarizations and performs object detection followed by change detection on the result.

Theoretical and numerical investigations into the SPRT method for anomaly detection

International Nuclear Information System (INIS)

Schoonewelle, H.; Hagen, T.H.J.J. van der; Hoogenboom, J.E.

1995-01-01

The sequential probability ratio test developed by Wald is a powerful method of testing an alternative hypothesis against a null hypothesis. This makes the method applicable for anomaly detection. In this paper the method is used to detect a change of the standard deviation of a Gaussian distributed white noise signal. The false alarm probability, the alarm failure probability and the average time to alarm of the method, which are important parameters for anomaly detection, are determined by simulation and compared with theoretical results. Each of the three parameters is presented in dependence of the other two and the ratio of the standard deviation of the anomalous signal and that of the normal signal. Results show that the method is very well suited for anomaly detection. It can detect for example a 50% change in standard deviation within 1 second with a false alarm and alarm failure rate of less than once per month. (author)

Theoretical and numerical investigations into the SPRT method for anomaly detection

Energy Technology Data Exchange (ETDEWEB)

Schoonewelle, H.; Hagen, T.H.J.J. van der; Hoogenboom, J.E. [Interuniversitair Reactor Inst., Delft (Netherlands)

1995-11-01

The sequential probability ratio test developed by Wald is a powerful method of testing an alternative hypothesis against a null hypothesis. This makes the method applicable for anomaly detection. In this paper the method is used to detect a change of the standard deviation of a Gaussian distributed white noise signal. The false alarm probability, the alarm failure probability and the average time to alarm of the method, which are important parameters for anomaly detection, are determined by simulation and compared with theoretical results. Each of the three parameters is presented in dependence of the other two and the ratio of the standard deviation of the anomalous signal and that of the normal signal. Results show that the method is very well suited for anomaly detection. It can detect for example a 50% change in standard deviation within 1 second with a false alarm and alarm failure rate of less than once per month. (author).

Using adversary text to detect adversary phase changes.

Energy Technology Data Exchange (ETDEWEB)

Speed, Ann Elizabeth; Doser, Adele Beatrice; Warrender, Christina E.

2009-05-01

The purpose of this work was to help develop a research roadmap and small proof ofconcept for addressing key problems and gaps from the perspective of using text analysis methods as a primary tool for detecting when a group is undergoing a phase change. Self- rganizing map (SOM) techniques were used to analyze text data obtained from the tworld-wide web. Statistical studies indicate that it may be possible to predict phase changes, as well as detect whether or not an example of writing can be attributed to a group of interest.

Analytical detection methods for irradiated foods

International Nuclear Information System (INIS)

1991-03-01

The present publication is a review of scientific literature on the analytical identification of foods treated with ionizing radiation and the quantitative determination of absorbed dose of radiation. Because of the extremely low level of chemical changes resulting from irradiation or because of the lack of specificity to irradiation of any chemical changes, a few methods of quantitative determination of absorbed dose have shown promise until now. On the other hand, the present review has identified several possible methods, which could be used, following further research and testing, for the identification of irradiated foods. An IAEA Co-ordinated Research Programme on Analytical Detection Methods for Irradiation Treatment of Food ('ADMIT'), established in 1990, is currently investigating many of the methods cited in the present document. Refs and tab

Automated Change Detection for Validation and Update of Geodata

DEFF Research Database (Denmark)

Olsen, Brian Pilemann; Knudsen, Thomas

)is presented. Height information is used to determine the location of object which stands above terrain, and the CIR-Imagery is used to exclude vegetation, leading to a potential buildings mask. Comparing the existing objects in the map database with these extracted objects leads to a validation of the map...... to newer (raster based) remote sensing images in order to detect changes in objects. In this paper an automatic change detection method considering changes in the building theme and based on colourinfrared (CIR) aerial photographs in combination with height information (LIDAR, digital photogrammetry...

Detection of Anomalies and Changes of Rainfall in the Yellow River Basin, China, through Two Graphical Methods

Directory of Open Access Journals (Sweden)

Hao Wu

2017-12-01

Full Text Available This study aims to reveal rainfall anomalies and changes over the Yellow River Basin due to the fragile ecosystem and rainfall-related disasters. Common trend analyses relate to overall trends in mean values. Therefore, we used two graphical methods: the quantile perturbation method (QPM was used to investigate anomalies over time in extreme rainfall, and the partial trend method (PTM was used to analyze rainfall changes at different intensities. A nonparametric bootstrap procedure is proposed in order to identify significant PTM indices. The QPM indicated prevailing positive anomalies in extreme daily rainfall 50 years ago and in the middle reaches during the 1970s and 1980s. The PTM detected significant decreases in annual rainfall mainly in the latter half of the middle reaches, two-thirds of which occurred in high and heavy rainfall. Most stations in the middle and lower reaches showed significant decreases in rainy days. Daily rainfall intensity had a significant increase at 13 stations, where rainy days were generally decreasing. The combined effect of these opposing changes explains the prevailing absence of change in annual rainfall, and the observed decreases in annual rainfall can be attributed to the decreasing number of rainy days. The changes in rainy days and rainfall intensity were dominated by the wet season and dry season, respectively.

Adaptive filtering and change detection

CERN Document Server

Gustafsson, Fredrik

2003-01-01

Adaptive filtering is a classical branch of digital signal processing (DSP). Industrial interest in adaptive filtering grows continuously with the increase in computer performance that allows ever more conplex algorithms to be run in real-time. Change detection is a type of adaptive filtering for non-stationary signals and is also the basic tool in fault detection and diagnosis. Often considered as separate subjects Adaptive Filtering and Change Detection bridges a gap in the literature with a unified treatment of these areas, emphasizing that change detection is a natural extensi

Spectral anomaly methods for aerial detection using KUT nuisance rejection

International Nuclear Information System (INIS)

Detwiler, R.S.; Pfund, D.M.; Myjak, M.J.; Kulisek, J.A.; Seifert, C.E.

2015-01-01

This work discusses the application and optimization of a spectral anomaly method for the real-time detection of gamma radiation sources from an aerial helicopter platform. Aerial detection presents several key challenges over ground-based detection. For one, larger and more rapid background fluctuations are typical due to higher speeds, larger field of view, and geographically induced background changes. As well, the possible large altitude or stand-off distance variations cause significant steps in background count rate as well as spectral changes due to increased gamma-ray scatter with detection at higher altitudes. The work here details the adaptation and optimization of the PNNL-developed algorithm Nuisance-Rejecting Spectral Comparison Ratios for Anomaly Detection (NSCRAD), a spectral anomaly method previously developed for ground-based applications, for an aerial platform. The algorithm has been optimized for two multi-detector systems; a NaI(Tl)-detector-based system and a CsI detector array. The optimization here details the adaptation of the spectral windows for a particular set of target sources to aerial detection and the tailoring for the specific detectors. As well, the methodology and results for background rejection methods optimized for the aerial gamma-ray detection using Potassium, Uranium and Thorium (KUT) nuisance rejection are shown. Results indicate that use of a realistic KUT nuisance rejection may eliminate metric rises due to background magnitude and spectral steps encountered in aerial detection due to altitude changes and geographically induced steps such as at land–water interfaces

Evidential analysis of difference images for change detection of multitemporal remote sensing images

Science.gov (United States)

Chen, Yin; Peng, Lijuan; Cremers, Armin B.

2018-03-01

In this article, we develop two methods for unsupervised change detection in multitemporal remote sensing images based on Dempster-Shafer's theory of evidence (DST). In most unsupervised change detection methods, the probability of difference image is assumed to be characterized by mixture models, whose parameters are estimated by the expectation maximization (EM) method. However, the main drawback of the EM method is that it does not consider spatial contextual information, which may entail rather noisy detection results with numerous spurious alarms. To remedy this, we firstly develop an evidence theory based EM method (EEM) which incorporates spatial contextual information in EM by iteratively fusing the belief assignments of neighboring pixels to the central pixel. Secondly, an evidential labeling method in the sense of maximizing a posteriori probability (MAP) is proposed in order to further enhance the detection result. It first uses the parameters estimated by EEM to initialize the class labels of a difference image. Then it iteratively fuses class conditional information and spatial contextual information, and updates labels and class parameters. Finally it converges to a fixed state which gives the detection result. A simulated image set and two real remote sensing data sets are used to evaluate the two evidential change detection methods. Experimental results show that the new evidential methods are comparable to other prevalent methods in terms of total error rate.

A New Study of Two Divergence Metrics for Change Detection in Data Streams

KAUST Repository

Qahtan, Abdulhakim Ali Ali; Wang, Suojin; Carroll, Raymond; Zhang, Xiangliang

2014-01-01

Streaming data are dynamic in nature with frequent changes. To detect such changes, most methods measure the difference between the data distributions in a current time window and a reference window. Divergence metrics and density estimation are required to measure the difference between the data distributions. Our study shows that the Kullback-Leibler (KL) divergence, the most popular metric for comparing distributions, fails to detect certain changes due to its asymmetric property and its dependence on the variance of the data. We thus consider two metrics for detecting changes in univariate data streams: a symmetric KL-divergence and a divergence metric measuring the intersection area of two distributions. The experimental results show that these two metrics lead to more accurate results in change detection than baseline methods such as Change Finder and using conventional KL-divergence.

A New Study of Two Divergence Metrics for Change Detection in Data Streams

KAUST Repository

Qahtan, Abdulhakim Ali Ali

2014-08-01

Streaming data are dynamic in nature with frequent changes. To detect such changes, most methods measure the difference between the data distributions in a current time window and a reference window. Divergence metrics and density estimation are required to measure the difference between the data distributions. Our study shows that the Kullback-Leibler (KL) divergence, the most popular metric for comparing distributions, fails to detect certain changes due to its asymmetric property and its dependence on the variance of the data. We thus consider two metrics for detecting changes in univariate data streams: a symmetric KL-divergence and a divergence metric measuring the intersection area of two distributions. The experimental results show that these two metrics lead to more accurate results in change detection than baseline methods such as Change Finder and using conventional KL-divergence.

Neutron imaging integrated circuit and method for detecting neutrons

Science.gov (United States)

Nagarkar, Vivek V.; More, Mitali J.

2017-12-05

The present disclosure provides a neutron imaging detector and a method for detecting neutrons. In one example, a method includes providing a neutron imaging detector including plurality of memory cells and a conversion layer on the memory cells, setting one or more of the memory cells to a first charge state, positioning the neutron imaging detector in a neutron environment for a predetermined time period, and reading a state change at one of the memory cells, and measuring a charge state change at one of the plurality of memory cells from the first charge state to a second charge state less than the first charge state, where the charge state change indicates detection of neutrons at said one of the memory cells.

Social Network Change Detection

National Research Council Canada - National Science Library

McCulloh, Ian A; Carley, Kathleen M

2008-01-01

... between group members. The ability to systematically, statistically, effectively and efficiently detect these changes has the potential to enable the anticipation of change, provide early warning of change, and enable...

INTERACTIVE CHANGE DETECTION USING HIGH RESOLUTION REMOTE SENSING IMAGES BASED ON ACTIVE LEARNING WITH GAUSSIAN PROCESSES

Directory of Open Access Journals (Sweden)

H. Ru

2016-06-01

Full Text Available Although there have been many studies for change detection, the effective and efficient use of high resolution remote sensing images is still a problem. Conventional supervised methods need lots of annotations to classify the land cover categories and detect their changes. Besides, the training set in supervised methods often has lots of redundant samples without any essential information. In this study, we present a method for interactive change detection using high resolution remote sensing images with active learning to overcome the shortages of existing remote sensing image change detection techniques. In our method, there is no annotation of actual land cover category at the beginning. First, we find a certain number of the most representative objects in unsupervised way. Then, we can detect the change areas from multi-temporal high resolution remote sensing images by active learning with Gaussian processes in an interactive way gradually until the detection results do not change notably. The artificial labelling can be reduced substantially, and a desirable detection result can be obtained in a few iterations. The experiments on Geo-Eye1 and WorldView2 remote sensing images demonstrate the effectiveness and efficiency of our proposed method.

Stochastic change detection in uncertain nonlinear systems using reduced-order models: classification

International Nuclear Information System (INIS)

Yun, Hae-Bum; Masri, Sami F

2009-01-01

A reliable structural health monitoring methodology (SHM) is proposed to detect relatively small changes in uncertain nonlinear systems. A total of 4000 physical tests were performed using a complex nonlinear magneto-rheological (MR) damper. With the effective (or 'genuine') changes and uncertainties in the system characteristics of the semi-active MR damper, which were precisely controlled with known means and standard deviation of the input current, the tested MR damper was identified with the restoring force method (RFM), a non-parametric system identification method involving two-dimensional orthogonal polynomials. Using the identified RFM coefficients, both supervised and unsupervised pattern recognition techniques (including support vector classification and k-means clustering) were employed to detect system changes in the MR damper. The classification results showed that the identified coefficients with orthogonal basis function can be used as reliable indicators for detecting (small) changes, interpreting the physical meaning of the detected changes without a priori knowledge of the monitored system and quantifying the uncertainty bounds of the detected changes. The classification errors were analyzed using the standard detection theory to evaluate the performance of the developed SHM methodology. An optimal classifier design procedure was also proposed and evaluated to minimize type II (or 'missed') errors

Adaptive Change Detection for Long-Term Machinery Monitoring Using Incremental Sliding-Window

Science.gov (United States)

Wang, Teng; Lu, Guo-Liang; Liu, Jie; Yan, Peng

2017-11-01

Detection of structural changes from an operational process is a major goal in machine condition monitoring. Existing methods for this purpose are mainly based on retrospective analysis, resulting in a large detection delay that limits their usages in real applications. This paper presents a new adaptive real-time change detection algorithm, an extension of the recent research by combining with an incremental sliding-window strategy, to handle the multi-change detection in long-term monitoring of machine operations. In particular, in the framework, Hilbert space embedding of distribution is used to map the original data into the Re-producing Kernel Hilbert Space (RKHS) for change detection; then, a new adaptive threshold strategy can be developed when making change decision, in which a global factor (used to control the coarse-to-fine level of detection) is introduced to replace the fixed value of threshold. Through experiments on a range of real testing data which was collected from an experimental rotating machinery system, the excellent detection performances of the algorithm for engineering applications were demonstrated. Compared with state-of-the-art methods, the proposed algorithm can be more suitable for long-term machinery condition monitoring without any manual re-calibration, thus is promising in modern industries.

CEST ANALYSIS: AUTOMATED CHANGE DETECTION FROM VERY-HIGH-RESOLUTION REMOTE SENSING IMAGES

Directory of Open Access Journals (Sweden)

M. Ehlers

2012-08-01

Full Text Available A fast detection, visualization and assessment of change in areas of crisis or catastrophes are important requirements for coordination and planning of help. Through the availability of new satellites and/or airborne sensors with very high spatial resolutions (e.g., WorldView, GeoEye new remote sensing data are available for a better detection, delineation and visualization of change. For automated change detection, a large number of algorithms has been proposed and developed. From previous studies, however, it is evident that to-date no single algorithm has the potential for being a reliable change detector for all possible scenarios. This paper introduces the Combined Edge Segment Texture (CEST analysis, a decision-tree based cooperative suite of algorithms for automated change detection that is especially designed for the generation of new satellites with very high spatial resolution. The method incorporates frequency based filtering, texture analysis, and image segmentation techniques. For the frequency analysis, different band pass filters can be applied to identify the relevant frequency information for change detection. After transforming the multitemporal images via a fast Fourier transform (FFT and applying the most suitable band pass filter, different methods are available to extract changed structures: differencing and correlation in the frequency domain and correlation and edge detection in the spatial domain. Best results are obtained using edge extraction. For the texture analysis, different 'Haralick' parameters can be calculated (e.g., energy, correlation, contrast, inverse distance moment with 'energy' so far providing the most accurate results. These algorithms are combined with a prior segmentation of the image data as well as with morphological operations for a final binary change result. A rule-based combination (CEST of the change algorithms is applied to calculate the probability of change for a particular location. CEST

A comparison of three time-domain anomaly detection methods

Energy Technology Data Exchange (ETDEWEB)

Schoonewelle, H.; Hagen, T.H.J.J. van der; Hoogenboom, J.E. [Delft University of Technology (Netherlands). Interfaculty Reactor Institute

1996-01-01

Three anomaly detection methods based on a comparison of signal values with predictions from an autoregressive model are presented. These methods are: the extremes method, the {chi}{sup 2} method and the sequential probability ratio test. The methods are used to detect a change of the standard deviation of the residual noise obtained from applying an autoregressive model. They are fast and can be used in on-line applications. For each method some important anomaly detection parameters are determined by calculation or simulation. These parameters are: the false alarm rate, the average time to alarm and - being of minor importance -the alarm failure rate. Each method is optimized with respect to the average time to alarm for a given value of the false alarm rate. The methods are compared with each other, resulting in the sequential probability ratio test being clearly superior. (author).

A comparison of three time-domain anomaly detection methods

International Nuclear Information System (INIS)

Schoonewelle, H.; Hagen, T.H.J.J. van der; Hoogenboom, J.E.

1996-01-01

Three anomaly detection methods based on a comparison of signal values with predictions from an autoregressive model are presented. These methods are: the extremes method, the χ 2 method and the sequential probability ratio test. The methods are used to detect a change of the standard deviation of the residual noise obtained from applying an autoregressive model. They are fast and can be used in on-line applications. For each method some important anomaly detection parameters are determined by calculation or simulation. These parameters are: the false alarm rate, the average time to alarm and - being of minor importance -the alarm failure rate. Each method is optimized with respect to the average time to alarm for a given value of the false alarm rate. The methods are compared with each other, resulting in the sequential probability ratio test being clearly superior. (author)

Symmetrized local co-registration optimization for anomalous change detection

Energy Technology Data Exchange (ETDEWEB)

Wohlberg, Brendt E [Los Alamos National Laboratory; Theiler, James P [Los Alamos National Laboratory

2009-01-01

The goal of anomalous change detection (ACD) is to identify what unusual changes have occurred in a scene, based on two images of the scene taken at different times and under different conditions. The actual anomalous changes need to be distinguished from the incidental differences that occur throughout the imagery, and one of the most common and confounding of these incidental differences is due to the misregistration of the images, due to limitations of the registration pre-processing applied to the image pair. We propose a general method to compensate for residual misregistration in any ACD algorithm which constructs an estimate of the degree of 'anomalousness' for every pixel in the image pair. The method computes a modified misregistration-insensitive anomalousness by making local re-registration adjustments to minimize the local anomalousness. In this paper we describe a symmetrized version of our initial algorithm, and find significant performance improvements in the anomalous change detection ROC curves for a number of real and synthetic data sets.

Land cover change detection in West Jilin using ETM+ images

Institute of Scientific and Technical Information of China (English)

Edward M.Osei,Jr.; ZHOU Yun-xuan

2004-01-01

In order to assess the information content and accuracy ofLandsat ETM+ digital images in land cover change detection,change-detection techniques of image differencing,normalized difference vegetation index,principal components analysis and tasseled-cap transformation were applied to yield 13 images. These images were thresholded into change and no change areas. The thresholded images were then checked in terms of various accuracies. The experiment results show that kappa coefficients of the 13 images range from 48.05 ～78.09. Different images do detect different types of changes. Images associated with changes in the near-infrared-reflectance or greenness detects crop-type changes and changes between vegetative and non-vegetative features. A unique means of using only Landsat imagery without reference data for the assessment of change in arid land are presented. Images of 12th June, 2000 and 2nd June, 2002 are used to validate the means. Analyses of standard accuracy and spatial agreement are performed to compare the new images (hereafter called "change images" ) representing the change between the two dates. Spatial agreement evaluates the conformity in the classified "change pixels" and "no-change pixels" at the same location on different change images and comprehensively examines the different techniques. This method would enable authorities to monitor land degradation efficiently and accurately.

Landslide Change Detection Based on Multi-Temporal Airborne LiDAR-Derived DEMs

Directory of Open Access Journals (Sweden)

Omar E. Mora

2018-01-01

Full Text Available Remote sensing technologies have seen extraordinary improvements in both spatial resolution and accuracy recently. In particular, airborne laser scanning systems can now provide data for surface modeling with unprecedented resolution and accuracy, which can effectively support the detection of sub-meter surface features, vital for landslide mapping. Also, the easy repeatability of data acquisition offers the opportunity to monitor temporal surface changes, which are essential to identifying developing or active slides. Specific methods are needed to detect and map surface changes due to landslide activities. In this paper, we present a methodology that is based on fusing probabilistic change detection and landslide surface feature extraction utilizing multi-temporal Light Detection and Ranging (LiDAR derived Digital Elevation Models (DEMs to map surface changes demonstrating landslide activity. The proposed method was tested in an area with numerous slides ranging from 200 m2 to 27,000 m2 in area under low vegetation and tree cover, Zanesville, Ohio, USA. The surface changes observed are probabilistically evaluated to determine the likelihood of the changes being landslide activity related. Next, based on surface features, a Support Vector Machine (SVM quantifies and maps the topographic signatures of landslides in the entire area. Finally, these two processes are fused to detect landslide prone changes. The results demonstrate that 53 out of 80 inventory mapped landslides were identified using this method. Additionally, some areas that were not mapped in the inventory map displayed changes that are likely to be developing landslides.

Change Analysis in Structural Laser Scanning Point Clouds: The Baseline Method.

Science.gov (United States)

Shen, Yueqian; Lindenbergh, Roderik; Wang, Jinhu

2016-12-24

A method is introduced for detecting changes from point clouds that avoids registration. For many applications, changes are detected between two scans of the same scene obtained at different times. Traditionally, these scans are aligned to a common coordinate system having the disadvantage that this registration step introduces additional errors. In addition, registration requires stable targets or features. To avoid these issues, we propose a change detection method based on so-called baselines. Baselines connect feature points within one scan. To analyze changes, baselines connecting corresponding points in two scans are compared. As feature points either targets or virtual points corresponding to some reconstructable feature in the scene are used. The new method is implemented on two scans sampling a masonry laboratory building before and after seismic testing, that resulted in damages in the order of several centimeters. The centres of the bricks of the laboratory building are automatically extracted to serve as virtual points. Baselines connecting virtual points and/or target points are extracted and compared with respect to a suitable structural coordinate system. Changes detected from the baseline analysis are compared to a traditional cloud to cloud change analysis demonstrating the potential of the new method for structural analysis.

Change Analysis in Structural Laser Scanning Point Clouds: The Baseline Method

Directory of Open Access Journals (Sweden)

Yueqian Shen

2016-12-01

Full Text Available A method is introduced for detecting changes from point clouds that avoids registration. For many applications, changes are detected between two scans of the same scene obtained at different times. Traditionally, these scans are aligned to a common coordinate system having the disadvantage that this registration step introduces additional errors. In addition, registration requires stable targets or features. To avoid these issues, we propose a change detection method based on so-called baselines. Baselines connect feature points within one scan. To analyze changes, baselines connecting corresponding points in two scans are compared. As feature points either targets or virtual points corresponding to some reconstructable feature in the scene are used. The new method is implemented on two scans sampling a masonry laboratory building before and after seismic testing, that resulted in damages in the order of several centimeters. The centres of the bricks of the laboratory building are automatically extracted to serve as virtual points. Baselines connecting virtual points and/or target points are extracted and compared with respect to a suitable structural coordinate system. Changes detected from the baseline analysis are compared to a traditional cloud to cloud change analysis demonstrating the potential of the new method for structural analysis.

Consumer behaviour in district heating systems. Detecting changes

Energy Technology Data Exchange (ETDEWEB)

Jonsson, G.R. [University of Iceland (Iceland). Dept. of Mechanical and Industrial Engineering

2002-10-01

This paper focuses on methods or measures that can be used to detect changes in the consumer behavior regarding hot water use. This is done by estimating models that describe the average daily flow using several climate variables as input variables. (orig.)

Multiscale Region-Level VHR Image Change Detection via Sparse Change Descriptor and Robust Discriminative Dictionary Learning

Directory of Open Access Journals (Sweden)

Yuan Xu

2015-01-01

Full Text Available Very high resolution (VHR image change detection is challenging due to the low discriminative ability of change feature and the difficulty of change decision in utilizing the multilevel contextual information. Most change feature extraction techniques put emphasis on the change degree description (i.e., in what degree the changes have happened, while they ignore the change pattern description (i.e., how the changes changed, which is of equal importance in characterizing the change signatures. Moreover, the simultaneous consideration of the classification robust to the registration noise and the multiscale region-consistent fusion is often neglected in change decision. To overcome such drawbacks, in this paper, a novel VHR image change detection method is proposed based on sparse change descriptor and robust discriminative dictionary learning. Sparse change descriptor combines the change degree component and the change pattern component, which are encoded by the sparse representation error and the morphological profile feature, respectively. Robust change decision is conducted by multiscale region-consistent fusion, which is implemented by the superpixel-level cosparse representation with robust discriminative dictionary and the conditional random field model. Experimental results confirm the effectiveness of the proposed change detection technique.

Explicit behavioral detection of visual changes develops without their implicit neurophysiological detectability

Directory of Open Access Journals (Sweden)

Pessi eLyyra

2012-03-01

Full Text Available Change blindness is a failure of explicitly detecting changes between consecutively presented images when separated, e.g., by a brief blank screen. There is a growing body of evidence of implicit detection of even explicitly undetectable changes, pointing to the possibility of the implicit change detection as a prerequisite for its explicit counterpart. We recorded event-related potentials (ERPs of the electroencephalography in adults during an oddball-variant of change blindness flicker paradigm. In this variant, rare pictures with a change were interspersed with frequent pictures with no change. In separate stimulus blocks, the blank screen between the change and no-change picture was either of 100 ms or 500 ms in duration. In both stimulus conditions the participants eventually explicitly detect the changed pictures, the blank screen of the longer duration only requiring in average 10 % longer exposure to the picture series until the ability emerged. However, during the change blindness, ERPs were displaced towards negative polarity at 200–260 ms after the stimulus onset (visual mismatch negativity only with the blank screens of the shorter ISI. Our finding of ‘implicit change blindness’ for pictorial material that, nevertheless, successfully prepares the visual system for explicit change detection suggests that implicit change detection may not be a necessary condition for explicit change detection and that they may recruit at least partially distinct memory mechanisms.

Supervised / unsupervised change detection

OpenAIRE

de Alwis Pitts, Dilkushi; De Vecchi, Daniele; Harb, Mostapha; So, Emily; Dell'Acqua, Fabio

2014-01-01

The aim of this deliverable is to provide an overview of the state of the art in change detection techniques and a critique of what could be programmed to derive SENSUM products. It is the product of the collaboration between UCAM and EUCENTRE. The document includes as a necessary requirement a discussion about a proposed technique for co-registration. Since change detection techniques require an assessment of a series of images and the basic process involves comparing and contrasting the sim...

Systems and methods for neutron detection using scintillator nano-materials

Science.gov (United States)

Letant, Sonia Edith; Wang, Tzu-Fang

2016-03-08

In one embodiment, a neutron detector includes a three dimensional matrix, having nanocomposite materials and a substantially transparent film material for suspending the nanocomposite materials, a detector coupled to the three dimensional matrix adapted for detecting a change in the nanocomposite materials, and an analyzer coupled to the detector adapted for analyzing the change detected by the detector. In another embodiment, a method for detecting neutrons includes receiving radiation from a source, converting neutrons in the radiation into alpha particles using converter material, converting the alpha particles into photons using quantum dot emitters, detecting the photons, and analyzing the photons to determine neutrons in the radiation.

Land use change detection based on multi-date imagery from different satellite sensor systems

Science.gov (United States)

Stow, Douglas A.; Collins, Doretta; Mckinsey, David

1990-01-01

An empirical study is conducted to assess the accuracy of land use change detection using satellite image data acquired ten years apart by sensors with differing spatial resolutions. The primary goals of the investigation were to (1) compare standard change detection methods applied to image data of varying spatial resolution, (2) assess whether to transform the raster grid of the higher resolution image data to that of the lower resolution raster grid or vice versa in the registration process, (3) determine if Landsat/Thermatic Mapper or SPOT/High Resolution Visible multispectral data provide more accurate detection of land use changes when registered to historical Landsat/MSS data. It is concluded that image ratioing of multisensor, multidate satellite data produced higher change detection accuracies than did principal components analysis, and that it is useful as a land use change enhancement method.

Change detection of medical images using dictionary learning techniques and principal component analysis.

Science.gov (United States)

Nika, Varvara; Babyn, Paul; Zhu, Hongmei

2014-07-01

Automatic change detection methods for identifying the changes of serial MR images taken at different times are of great interest to radiologists. The majority of existing change detection methods in medical imaging, and those of brain images in particular, include many preprocessing steps and rely mostly on statistical analysis of magnetic resonance imaging (MRI) scans. Although most methods utilize registration software, tissue classification remains a difficult and overwhelming task. Recently, dictionary learning techniques are being used in many areas of image processing, such as image surveillance, face recognition, remote sensing, and medical imaging. We present an improved version of the EigenBlockCD algorithm, named the EigenBlockCD-2. The EigenBlockCD-2 algorithm performs an initial global registration and identifies the changes between serial MR images of the brain. Blocks of pixels from a baseline scan are used to train local dictionaries to detect changes in the follow-up scan. We use PCA to reduce the dimensionality of the local dictionaries and the redundancy of data. Choosing the appropriate distance measure significantly affects the performance of our algorithm. We examine the differences between [Formula: see text] and [Formula: see text] norms as two possible similarity measures in the improved EigenBlockCD-2 algorithm. We show the advantages of the [Formula: see text] norm over the [Formula: see text] norm both theoretically and numerically. We also demonstrate the performance of the new EigenBlockCD-2 algorithm for detecting changes of MR images and compare our results with those provided in the recent literature. Experimental results with both simulated and real MRI scans show that our improved EigenBlockCD-2 algorithm outperforms the previous methods. It detects clinical changes while ignoring the changes due to the patient's position and other acquisition artifacts.

Change Detection Algorithm for the Production of Land Cover Change Maps over the European Union Countries

Directory of Open Access Journals (Sweden)

Sebastian Aleksandrowicz

2014-06-01

Full Text Available Contemporary satellite Earth Observation systems provide growing amounts of very high spatial resolution data that can be used in various applications. An increasing number of sensors make it possible to monitor selected areas in great detail. However, in order to handle the volume of data, a high level of automation is required. The semi-automatic change detection methodology described in this paper was developed to annually update land cover maps prepared in the context of the Geoland2. The proposed algorithm was tailored to work with different very high spatial resolution images acquired over different European landscapes. The methodology is a fusion of various change detection methods ranging from: (1 layer arithmetic; (2 vegetation indices (NDVI differentiating; (3 texture calculation; and methods based on (4 canonical correlation analysis (multivariate alteration detection (MAD. User intervention during the production of the change map is limited to the selection of the input data, the size of initial segments and the threshold for texture classification (optionally. To achieve a high level of automation, statistical thresholds were applied in most of the processing steps. Tests showed an overall change recognition accuracy of 89%, and the change type classification methodology can accurately classify transitions between classes.

Comparing registration methods for mapping brain change using tensor-based morphometry.

Science.gov (United States)

Yanovsky, Igor; Leow, Alex D; Lee, Suh; Osher, Stanley J; Thompson, Paul M

2009-10-01

Measures of brain changes can be computed from sequential MRI scans, providing valuable information on disease progression for neuroscientific studies and clinical trials. Tensor-based morphometry (TBM) creates maps of these brain changes, visualizing the 3D profile and rates of tissue growth or atrophy. In this paper, we examine the power of different nonrigid registration models to detect changes in TBM, and their stability when no real changes are present. Specifically, we investigate an asymmetric version of a recently proposed Unbiased registration method, using mutual information as the matching criterion. We compare matching functionals (sum of squared differences and mutual information), as well as large-deformation registration schemes (viscous fluid and inverse-consistent linear elastic registration methods versus Symmetric and Asymmetric Unbiased registration) for detecting changes in serial MRI scans of 10 elderly normal subjects and 10 patients with Alzheimer's Disease scanned at 2-week and 1-year intervals. We also analyzed registration results when matching images corrupted with artificial noise. We demonstrated that the unbiased methods, both symmetric and asymmetric, have higher reproducibility. The unbiased methods were also less likely to detect changes in the absence of any real physiological change. Moreover, they measured biological deformations more accurately by penalizing bias in the corresponding statistical maps.

A FRAMEWORK OF CHANGE DETECTION BASED ON COMBINED MORPHOLOGICA FEATURES AND MULTI-INDEX CLASSIFICATION

Directory of Open Access Journals (Sweden)

S. Li

2017-09-01

Full Text Available Remote sensing images are particularly well suited for analysis of land cover change. In this paper, we present a new framework for detection of changing land cover using satellite imagery. Morphological features and a multi-index are used to extract typical objects from the imagery, including vegetation, water, bare land, buildings, and roads. Our method, based on connected domains, is different from traditional methods; it uses image segmentation to extract morphological features, while the enhanced vegetation index (EVI, the differential water index (NDWI are used to extract vegetation and water, and a fragmentation index is used to the correct extraction results of water. HSV transformation and threshold segmentation extract and remove the effects of shadows on extraction results. Change detection is performed on these results. One of the advantages of the proposed framework is that semantic information is extracted automatically using low-level morphological features and indexes. Another advantage is that the proposed method detects specific types of change without any training samples. A test on ZY-3 images demonstrates that our framework has a promising capability to detect change.

a Framework of Change Detection Based on Combined Morphologica Features and Multi-Index Classification

Science.gov (United States)

Li, S.; Zhang, S.; Yang, D.

2017-09-01

Remote sensing images are particularly well suited for analysis of land cover change. In this paper, we present a new framework for detection of changing land cover using satellite imagery. Morphological features and a multi-index are used to extract typical objects from the imagery, including vegetation, water, bare land, buildings, and roads. Our method, based on connected domains, is different from traditional methods; it uses image segmentation to extract morphological features, while the enhanced vegetation index (EVI), the differential water index (NDWI) are used to extract vegetation and water, and a fragmentation index is used to the correct extraction results of water. HSV transformation and threshold segmentation extract and remove the effects of shadows on extraction results. Change detection is performed on these results. One of the advantages of the proposed framework is that semantic information is extracted automatically using low-level morphological features and indexes. Another advantage is that the proposed method detects specific types of change without any training samples. A test on ZY-3 images demonstrates that our framework has a promising capability to detect change.

Fault diagnosis of downhole drilling incidents using adaptive observers and statistical change detection

DEFF Research Database (Denmark)

Willersrud, Anders; Blanke, Mogens; Imsland, Lars

2015-01-01

Downhole abnormal incidents during oil and gas drilling causes costly delays, any may also potentially lead to dangerous scenarios. Dierent incidents willcause changes to dierent parts of the physics of the process. Estimating thechanges in physical parameters, and correlating these with changes ...... expectedfrom various defects, can be used to diagnose faults while in development.This paper shows how estimated friction parameters and ow rates can de-tect and isolate the type of incident, as well as isolating the position of adefect. Estimates are shown to be subjected to non......-Gaussian,t-distributednoise, and a dedicated multivariate statistical change detection approach isused that detects and isolates faults by detecting simultaneous changes inestimated parameters and ow rates. The properties of the multivariate di-agnosis method are analyzed, and it is shown how detection and false alarmprobabilities...... are assessed and optimized using data-based learning to obtainthresholds for hypothesis testing. Data from a 1400 m horizontal ow loop isused to test the method, and successful diagnosis of the incidents drillstringwashout (pipe leakage), lost circulation, gas in ux, and drill bit plugging aredemonstrated....

Growth Curve Analysis and Change-Points Detection in Extremes

KAUST Repository

Meng, Rui

2016-05-15

The thesis consists of two coherent projects. The first project presents the results of evaluating salinity tolerance in barley using growth curve analysis where different growth trajectories are observed within barley families. The study of salinity tolerance in plants is crucial to understanding plant growth and productivity. Because fully-automated smarthouses with conveyor systems allow non-destructive and high-throughput phenotyping of large number of plants, it is now possible to apply advanced statistical tools to analyze daily measurements and to study salinity tolerance. To compare different growth patterns of barley variates, we use functional data analysis techniques to analyze the daily projected shoot areas. In particular, we apply the curve registration method to align all the curves from the same barley family in order to summarize the family-wise features. We also illustrate how to use statistical modeling to account for spatial variation in microclimate in smarthouses and for temporal variation across runs, which is crucial for identifying traits of the barley variates. In our analysis, we show that the concentrations of sodium and potassium in leaves are negatively correlated, and their interactions are associated with the degree of salinity tolerance. The second project studies change-points detection methods in extremes when multiple time series data are available. Motived by the scientific question of whether the chances to experience extreme weather are different in different seasons of a year, we develop a change-points detection model to study changes in extremes or in the tail of a distribution. Most of existing models identify seasons from multiple yearly time series assuming a season or a change-point location remains exactly the same across years. In this work, we propose a random effect model that allows the change-point to vary from year to year, following a given distribution. Both parametric and nonparametric methods are developed

A New Maximum-Likelihood Change Estimator for Two-Pass SAR Coherent Change Detection.

Energy Technology Data Exchange (ETDEWEB)

Wahl, Daniel E.; Yocky, David A.; Jakowatz, Charles V,

2014-09-01

In this paper, we derive a new optimal change metric to be used in synthetic aperture RADAR (SAR) coherent change detection (CCD). Previous CCD methods tend to produce false alarm states (showing change when there is none) in areas of the image that have a low clutter-to-noise power ratio (CNR). The new estimator does not suffer from this shortcoming. It is a surprisingly simple expression, easy to implement, and is optimal in the maximum-likelihood (ML) sense. The estimator produces very impressive results on the CCD collects that we have tested.

Cortical dynamics of visual change detection based on sensory memory.

Science.gov (United States)

Urakawa, Tomokazu; Inui, Koji; Yamashiro, Koya; Tanaka, Emi; Kakigi, Ryusuke

2010-08-01

Detecting a visual change was suggested to relate closely to the visual sensory memory formed by visual stimuli before the occurrence of the change, because change detection involves identifying a difference between ongoing and preceding sensory conditions. Previous neuroimaging studies showed that an abrupt visual change activates the middle occipital gyrus (MOG). However, it still remains to be elucidated whether the MOG is related to visual change detection based on sensory memory. Here we tried to settle this issue using a new method of stimulation with blue and red LEDs to emphasize a memory-based change detection process. There were two stimuli, a standard trial stimulus and a deviant trial stimulus. The former was a red light lasting 500 ms, and the latter was a red light lasting 250 ms immediately followed by a blue light lasting 250 ms. Effects of the trial-trial interval, 250 approximately 2000 ms, were investigated to know how cortical responses to the abrupt change (from red to blue) were affected by preceding conditions. The brain response to the deviant trial stimulus was recorded by magnetoencephalography. Results of a multi-dipole analysis showed that the activity in the MOG, peaking at around 150 ms after the change onset, decreased in amplitude as the interval increased, but the earlier activity in BA 17/18 was not affected by the interval. These results suggested that the MOG is an important cortical area relating to the sensory memory-based visual change-detecting system. Copyright 2010 Elsevier Inc. All rights reserved.

Detection of kinetic change points in piece-wise linear single molecule motion

Science.gov (United States)

Hill, Flynn R.; van Oijen, Antoine M.; Duderstadt, Karl E.

2018-03-01

Single-molecule approaches present a powerful way to obtain detailed kinetic information at the molecular level. However, the identification of small rate changes is often hindered by the considerable noise present in such single-molecule kinetic data. We present a general method to detect such kinetic change points in trajectories of motion of processive single molecules having Gaussian noise, with a minimum number of parameters and without the need of an assumed kinetic model beyond piece-wise linearity of motion. Kinetic change points are detected using a likelihood ratio test in which the probability of no change is compared to the probability of a change occurring, given the experimental noise. A predetermined confidence interval minimizes the occurrence of false detections. Applying the method recursively to all sub-regions of a single molecule trajectory ensures that all kinetic change points are located. The algorithm presented allows rigorous and quantitative determination of kinetic change points in noisy single molecule observations without the need for filtering or binning, which reduce temporal resolution and obscure dynamics. The statistical framework for the approach and implementation details are discussed. The detection power of the algorithm is assessed using simulations with both single kinetic changes and multiple kinetic changes that typically arise in observations of single-molecule DNA-replication reactions. Implementations of the algorithm are provided in ImageJ plugin format written in Java and in the Julia language for numeric computing, with accompanying Jupyter Notebooks to allow reproduction of the analysis presented here.

Change Analysis and Decision Tree Based Detection Model for Residential Objects across Multiple Scales

Directory of Open Access Journals (Sweden)

CHEN Liyan

2018-03-01

Full Text Available Change analysis and detection plays important role in the updating of multi-scale databases.When overlap an updated larger-scale dataset and a to-be-updated smaller-scale dataset,people usually focus on temporal changes caused by the evolution of spatial entities.Little attention is paid to the representation changes influenced by map generalization.Using polygonal building data as an example,this study examines the changes from different perspectives,such as the reasons for their occurrence,their performance format.Based on this knowledge,we employ decision tree in field of machine learning to establish a change detection model.The aim of the proposed model is to distinguish temporal changes that need to be applied as updates to the smaller-scale dataset from representation changes.The proposed method is validated through tests using real-world building data from Guangzhou city.The experimental results show the overall precision of change detection is more than 90%,which indicates our method is effective to identify changed objects.

Molecular methods for the detection of mutations.

Science.gov (United States)

Monteiro, C; Marcelino, L A; Conde, A R; Saraiva, C; Giphart-Gassler, M; De Nooij-van Dalen, A G; Van Buuren-van Seggelen, V; Van der Keur, M; May, C A; Cole, J; Lehmann, A R; Steinsgrimsdottir, H; Beare, D; Capulas, E; Armour, J A

2000-01-01

We report the results of a collaborative study aimed at developing reliable, direct assays for mutation in human cells. The project used common lymphoblastoid cell lines, both with and without mutagen treatment, as a shared resource to validate the development of new molecular methods for the detection of low-level mutations in the presence of a large excess of normal alleles. As the "gold standard, " hprt mutation frequencies were also measured on the same samples. The methods under development included i) the restriction site mutation (RSM) assay, in which mutations lead to the destruction of a restriction site; ii) minisatellite length-change mutation, in which mutations lead to alleles containing new numbers of tandem repeat units; iii) loss of heterozygosity for HLA epitopes, in which antibodies can be used to direct selection for mutant cells; iv) multiple fluorescence-based long linker arm nucleotides assay (mf-LLA) technology, for the detection of substitutional mutations; v) detection of alterations in the TP53 locus using a (CA) array as the target for the screening; and vi) PCR analysis of lymphocytes for the presence of the BCL2 t(14:18) translocation. The relative merits of these molecular methods are discussed, and a comparison made with more "traditional" methods.

Feature learning and change feature classification based on deep learning for ternary change detection in SAR images

Science.gov (United States)

Gong, Maoguo; Yang, Hailun; Zhang, Puzhao

2017-07-01

Ternary change detection aims to detect changes and group the changes into positive change and negative change. It is of great significance in the joint interpretation of spatial-temporal synthetic aperture radar images. In this study, sparse autoencoder, convolutional neural networks (CNN) and unsupervised clustering are combined to solve ternary change detection problem without any supervison. Firstly, sparse autoencoder is used to transform log-ratio difference image into a suitable feature space for extracting key changes and suppressing outliers and noise. And then the learned features are clustered into three classes, which are taken as the pseudo labels for training a CNN model as change feature classifier. The reliable training samples for CNN are selected from the feature maps learned by sparse autoencoder with certain selection rules. Having training samples and the corresponding pseudo labels, the CNN model can be trained by using back propagation with stochastic gradient descent. During its training procedure, CNN is driven to learn the concept of change, and more powerful model is established to distinguish different types of changes. Unlike the traditional methods, the proposed framework integrates the merits of sparse autoencoder and CNN to learn more robust difference representations and the concept of change for ternary change detection. Experimental results on real datasets validate the effectiveness and superiority of the proposed framework.

A New Outlier Detection Method for Multidimensional Datasets

KAUST Repository

Abdel Messih, Mario A.

2012-07-01

This study develops a novel hybrid method for outlier detection (HMOD) that combines the idea of distance based and density based methods. The proposed method has two main advantages over most of the other outlier detection methods. The first advantage is that it works well on both dense and sparse datasets. The second advantage is that, unlike most other outlier detection methods that require careful parameter setting and prior knowledge of the data, HMOD is not very sensitive to small changes in parameter values within certain parameter ranges. The only required parameter to set is the number of nearest neighbors. In addition, we made a fully parallelized implementation of HMOD that made it very efficient in applications. Moreover, we proposed a new way of using the outlier detection for redundancy reduction in datasets where the confidence level that evaluates how accurate the less redundant dataset can be used to represent the original dataset can be specified by users. HMOD is evaluated on synthetic datasets (dense and mixed “dense and sparse”) and a bioinformatics problem of redundancy reduction of dataset of position weight matrices (PWMs) of transcription factor binding sites. In addition, in the process of assessing the performance of our redundancy reduction method, we developed a simple tool that can be used to evaluate the confidence level of reduced dataset representing the original dataset. The evaluation of the results shows that our method can be used in a wide range of problems.

A dual-process account of auditory change detection.

Science.gov (United States)

McAnally, Ken I; Martin, Russell L; Eramudugolla, Ranmalee; Stuart, Geoffrey W; Irvine, Dexter R F; Mattingley, Jason B

2010-08-01

Listeners can be "deaf" to a substantial change in a scene comprising multiple auditory objects unless their attention has been directed to the changed object. It is unclear whether auditory change detection relies on identification of the objects in pre- and post-change scenes. We compared the rates at which listeners correctly identify changed objects with those predicted by change-detection models based on signal detection theory (SDT) and high-threshold theory (HTT). Detected changes were not identified as accurately as predicted by models based on either theory, suggesting that some changes are detected by a process that does not support change identification. Undetected changes were identified as accurately as predicted by the HTT model but much less accurately than predicted by the SDT models. The process underlying change detection was investigated further by determining receiver-operating characteristics (ROCs). ROCs did not conform to those predicted by either a SDT or a HTT model but were well modeled by a dual-process that incorporated HTT and SDT components. The dual-process model also accurately predicted the rates at which detected and undetected changes were correctly identified.

Research on pipeline leak detection method based on pressure and dynamic pressure

Energy Technology Data Exchange (ETDEWEB)

Wang, Likun; Xiong, Min; Zhao, Jinyun; Wang, Hongchao; Xu, Bin; Yu, DongLiang; Sun, Yi; Cai, Yongjun [RnD center of PetroChina Pipeline Company, Langfang, Hebei, (China)

2010-07-01

Pipeline leakages are very frequent and need to be detected as fast as possible to avoid safety and environment issues. Many leakage detection processes have been developed. Acoustic wave methods based on static pressure and dynamic pressure are both used for pipeline leakage detection. This study investigated a new pipeline leak detection method based on joint pressure and dynamic pressure. A dynamic pressure transmitter was designed based on a piezoelectric dynamic pressure sensor. The study showed that the dynamic pressure signal should be used for pipeline leak detection with a quick-change in pipeline internal pressure, while the static pressure signal provides better results with a slow-change of pipeline internal pressure. The in-field results showed that the location error of dynamic pressure is reduced to 80 m with a leakage ratio of 0.6 % pipeline throughput.

DIAGNOSTIC METHODS IN BREAST CANCER DETECTION

Directory of Open Access Journals (Sweden)

Kristijana Hertl

2018-02-01

Full Text Available Background. In the world as well as in Slovenia, breast cancer is the most frequent female cancer. Due to its high incidence, it appears to be a serious health and economic problem. Content. Among other, tumour size at diagnosis, is an important prognostic factors of the course of the disease. The probability of axillary lymph node involvement as well as distant metastases is greater in larger tumours. This is the reason that encouraged the development of various diagnostic methods for early detection of small, clinically non-palpable breast tumours. Mammography, however, remains the »golden standard« of early breast cancer detection. It is the basic diagnostic method applied in all symptomatic women over 35 years of age and in asymptomatic women over 40 years of age. Ultrasonography (US, additional projections, magnetic resonance imaging (MRI and ductography are regarded as complementary diagnostic breast imaging techniques in addition to mammography. The detected changes in the breast can be further confirmed by US-, MR-guided or stereotactic biopsy. If necessary, surgical biopsy and the excision of a tissue sample, after wire or isotope localisation of the nonpalpable lesion, can be performed. Conclusions. Any of the above mentioned diagnostic methods has advantages as well as drawbacks and only detailed knowledge and understanding of each of them may assure the best option.

Attribute and topology based change detection in a constellation of previously detected objects

Science.gov (United States)

Paglieroni, David W.; Beer, Reginald N.

2016-01-19

A system that applies attribute and topology based change detection to networks of objects that were detected on previous scans of a structure, roadway, or area of interest. The attributes capture properties or characteristics of the previously detected objects, such as location, time of detection, size, elongation, orientation, etc. The topology of the network of previously detected objects is maintained in a constellation database that stores attributes of previously detected objects and implicitly captures the geometrical structure of the network. A change detection system detects change by comparing the attributes and topology of new objects detected on the latest scan to the constellation database of previously detected objects.

Spatio-temporal change detection from multidimensional arrays: Detecting deforestation from MODIS time series

Science.gov (United States)

Lu, Meng; Pebesma, Edzer; Sanchez, Alber; Verbesselt, Jan

2016-07-01

Growing availability of long-term satellite imagery enables change modeling with advanced spatio-temporal statistical methods. Multidimensional arrays naturally match the structure of spatio-temporal satellite data and can provide a clean modeling process for complex spatio-temporal analysis over large datasets. Our study case illustrates the detection of breakpoints in MODIS imagery time series for land cover change in the Brazilian Amazon using the BFAST (Breaks For Additive Season and Trend) change detection framework. BFAST includes an Empirical Fluctuation Process (EFP) to alarm the change and a change point time locating process. We extend the EFP to account for the spatial autocorrelation between spatial neighbors and assess the effects of spatial correlation when applying BFAST on satellite image time series. In addition, we evaluate how sensitive EFP is to the assumption that its time series residuals are temporally uncorrelated, by modeling it as an autoregressive process. We use arrays as a unified data structure for the modeling process, R to execute the analysis, and an array database management system to scale computation. Our results point to BFAST as a robust approach against mild temporal and spatial correlation, to the use of arrays to ease the modeling process of spatio-temporal change, and towards communicable and scalable analysis.

Detection and monitoring of invasive exotic plants: a comparison of four sampling methods

Science.gov (United States)

Cynthia D. Huebner

2007-01-01

The ability to detect and monitor exotic invasive plants is likely to vary depending on the sampling method employed. Methods with strong qualitative thoroughness for species detection often lack the intensity necessary to monitor vegetation change. Four sampling methods (systematic plot, stratified-random plot, modified Whittaker, and timed meander) in hemlock and red...

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.

Indigenous people's detection of rapid ecological change.

Science.gov (United States)

Aswani, Shankar; Lauer, Matthew

2014-06-01

When sudden catastrophic events occur, it becomes critical for coastal communities to detect and respond to environmental transformations because failure to do so may undermine overall ecosystem resilience and threaten people's livelihoods. We therefore asked how capable of detecting rapid ecological change following massive environmental disruptions local, indigenous people are. We assessed the direction and periodicity of experimental learning of people in the Western Solomon Islands after a tsunami in 2007. We compared the results of marine science surveys with local ecological knowledge of the benthos across 3 affected villages and 3 periods before and after the tsunami. We sought to determine how people recognize biophysical changes in the environment before and after catastrophic events such as earthquakes and tsunamis and whether people have the ability to detect ecological changes over short time scales or need longer time scales to recognize changes. Indigenous people were able to detect changes in the benthos over time. Detection levels differed between marine science surveys and local ecological knowledge sources over time, but overall patterns of statistically significant detection of change were evident for various habitats. Our findings have implications for marine conservation, coastal management policies, and disaster-relief efforts because when people are able to detect ecological changes, this, in turn, affects how they exploit and manage their marine resources. © 2014 Society for Conservation Biology.

Change Detection of High-Resolution Remote Sensing Images Based on Adaptive Fusion of Multiple Features

Science.gov (United States)

Wang, G. H.; Wang, H. B.; Fan, W. F.; Liu, Y.; Chen, C.

2018-04-01

In view of the traditional change detection algorithm mainly depends on the spectral information image spot, failed to effectively mining and fusion of multi-image feature detection advantage, the article borrows the ideas of object oriented analysis proposed a multi feature fusion of remote sensing image change detection algorithm. First by the multi-scale segmentation of image objects based; then calculate the various objects of color histogram and linear gradient histogram; utilizes the color distance and edge line feature distance between EMD statistical operator in different periods of the object, using the adaptive weighted method, the color feature distance and edge in a straight line distance of combination is constructed object heterogeneity. Finally, the curvature histogram analysis image spot change detection results. The experimental results show that the method can fully fuse the color and edge line features, thus improving the accuracy of the change detection.

Detecting Damaged Building Regions Based on Semantic Scene Change from Multi-Temporal High-Resolution Remote Sensing Images

Directory of Open Access Journals (Sweden)

Jihui Tu

2017-04-01

Full Text Available The detection of damaged building regions is crucial to emergency response actions and rescue work after a disaster. Change detection methods using multi-temporal remote sensing images are widely used for this purpose. Differing from traditional methods based on change detection for damaged building regions, semantic scene change can provide a new point of view since it can indicate the land-use variation at the semantic level. In this paper, a novel method is proposed for detecting damaged building regions based on semantic scene change in a visual Bag-of-Words model. Pre- and post-disaster scene change in building regions are represented by a uniform visual codebook frequency. The scene change of damaged and non-damaged building regions is discriminated using the Support Vector Machine (SVM classifier. An evaluation of experimental results, for a selected study site of the Longtou hill town of Yunnan, China, which was heavily damaged in the Ludian earthquake of 14 March 2013, shows that this method is feasible and effective for detecting damaged building regions. For the experiments, WorldView-2 optical imagery and aerial imagery is used.

Analysis and detection of climate change

International Nuclear Information System (INIS)

Thejll, P.; Stendel, M.

2001-01-01

The authors first discuss the concepts 'climate' and 'climate change detection', outlining the difficulties of the latter in terms of the properties of the former. In more detail they then discuss the analysis and detection, carried out at the Danish Climate Centre, of anthropogenic climate change and the nonanthropogenic changes regarding anthropogenic climate change the emphasis is on the improvement of global and regional climate models, and the reconstruction of past climates regarding non-anthropogenic changes the authors describe two case studies of potential solar influence on climate. (LN)

A statistical method (cross-validation) for bone loss region detection after spaceflight

Science.gov (United States)

Zhao, Qian; Li, Wenjun; Li, Caixia; Chu, Philip W.; Kornak, John; Lang, Thomas F.

2010-01-01

Astronauts experience bone loss after the long spaceflight missions. Identifying specific regions that undergo the greatest losses (e.g. the proximal femur) could reveal information about the processes of bone loss in disuse and disease. Methods for detecting such regions, however, remains an open problem. This paper focuses on statistical methods to detect such regions. We perform statistical parametric mapping to get t-maps of changes in images, and propose a new cross-validation method to select an optimum suprathreshold for forming clusters of pixels. Once these candidate clusters are formed, we use permutation testing of longitudinal labels to derive significant changes. PMID:20632144

Change Detection in High-Resolution Remote Sensing Images Using Levene-Test and Fuzzy Evaluation

Science.gov (United States)

Wang, G. H.; Wang, H. B.; Fan, W. F.; Liu, Y.; Liu, H. J.

2018-04-01

High-resolution remote sensing images possess complex spatial structure and rich texture information, according to these, this paper presents a new method of change detection based on Levene-Test and Fuzzy Evaluation. It first got map-spots by segmenting two overlapping images which had been pretreated, extracted features such as spectrum and texture. Then, changed information of all map-spots which had been treated by the Levene-Test were counted to obtain the candidate changed regions, hue information (H component) was extracted through the IHS Transform and conducted change vector analysis combined with the texture information. Eventually, the threshold was confirmed by an iteration method, the subject degrees of candidate changed regions were calculated, and final change regions were determined. In this paper experimental results on multi-temporal ZY-3 high-resolution images of some area in Jiangsu Province show that: Through extracting map-spots of larger difference as the candidate changed regions, Levene-Test decreases the computing load, improves the precision of change detection, and shows better fault-tolerant capacity for those unchanged regions which are of relatively large differences. The combination of Hue-texture features and fuzzy evaluation method can effectively decrease omissions and deficiencies, improve the precision of change detection.

Quantile index for gradual and abrupt change detection from CFB boiler sensor data in online settings

NARCIS (Netherlands)

Maslov, A.; Pechenizkiy, M.; Kärkkäinen, T.; Tähtinen, M.

2012-01-01

In this paper we consider the problem of online detection of gradual and abrupt changes in sensor data having high levels of noise and outliers. We propose a simple heuristic method based on the Quantile Index (QI) and study how robust this method is for detecting both gradual and abrupt changes

A Bi-Band Binary Mask Based Land-Use Change Detection Using Landsat 8 OLI Imagery

Directory of Open Access Journals (Sweden)

Xian Li

2017-03-01

Full Text Available Land use and cover change (LUCC is important for the global biogeochemical cycle and ecosystem. This paper introduced a change detection method based on a bi-band binary mask and an improved fuzzy c-means algorithm to research the LUCC. First, the bi-band binary mask approach with the core concept being the correlation coefficients between bands from different images are used to locate target areas with a likelihood of having changed areas. Second, the improved fuzzy c-means (FCM algorithm was used to execute classification on the target areas. This improved algorithm used distances to the Voronoi cell of the cluster instead of the Euclidean distance to the cluster center in the calculation of membership, and some other improvements were also used to decrease the loops and save time. Third, the post classification comparison was executed to get more accurate change information. As references, change detection using univariate band binary mask and NDVI binary mask were executed. The change detection methods were applied to Landsat 8 OLI images acquired in 2013 and 2015 to map LUCC in Chengwu, north China. The accuracy assessment was executed on classification results and change detection results. The overall accuracy of classification results of the improved FCM is 95.70% and the standard FCM is 84.40%. The average accuracy of change detection results using bi-band mask is 88.92%, using NDVI mask is 81.95%, and using univariate band binary mask is 56.01%. The result of the bi-band mask change detection shows that the change from farmland to built land is the main change type in the study area: total area is 9.03 km2. The developed method in the current study can be an effective approach to evaluate the LUCC and the results helpful for the land policy makers.

Accuracy of detecting stenotic changes on coronary cineangiograms using computer image processing

International Nuclear Information System (INIS)

Sugahara, Tetsuo; Kimura, Koji; Maeda, Hirofumi.

1990-01-01

To accurately interprets stenotic changes on coronary cineangiograms, an automatic method of detecting stenotic lesion using computer image processing was developed. First, tracing of artery was performed. The vessel edges were then determined by unilateral Gaussian fitting. The stenotic change was detected on the basis of the reference diameter estimated by Hough transformation. This method was evaluated in 132 segments of 27 arteries in 18 patients. Three observers carried out visual interpretation and computer-aided interpretation. The rate of detection by visual interpretation was 6.1, 28.8 and 20.5%, and by computer-aided interpretation, 39.4, 39.4 and 45.5%. With computer-aided interpretation, the agreement between any two observers on lesions and non-lesions was 40.2% and 59.8%, respectively. Therefore, visual interpretation tended to underestimate the stenotic changes on coronary cineangiograms. We think that computer-aided interpretation increase the reliability of diagnosis on coronary cineangiograms. (author)

Detecting significant changes in protein abundance

Directory of Open Access Journals (Sweden)

Kai Kammers

2015-06-01

Full Text Available We review and demonstrate how an empirical Bayes method, shrinking a protein's sample variance towards a pooled estimate, leads to far more powerful and stable inference to detect significant changes in protein abundance compared to ordinary t-tests. Using examples from isobaric mass labelled proteomic experiments we show how to analyze data from multiple experiments simultaneously, and discuss the effects of missing data on the inference. We also present easy to use open source software for normalization of mass spectrometry data and inference based on moderated test statistics.

Unsupervised land cover change detection: meaningful sequential time series analysis

CSIR Research Space (South Africa)

Salmon, BP

2011-06-01

Full Text Available An automated land cover change detection method is proposed that uses coarse spatial resolution hyper-temporal earth observation satellite time series data. The study compared three different unsupervised clustering approaches that operate on short...

Pigeons (Columba livia) show change blindness in a color-change detection task.

Science.gov (United States)

Herbranson, Walter T; Jeffers, Jacob S

2017-07-01

Change blindness is a phenomenon whereby changes to a stimulus are more likely go unnoticed under certain circumstances. Pigeons learned a change detection task, in which they observed sequential stimulus displays consisting of individual colors back-projected onto three response keys. The color of one response key changed during each sequence and pecks to the key that displayed the change were reinforced. Pigeons showed a change blindness effect, in that change detection accuracy was worse when there was an inter-stimulus interval interrupting the transition between consecutive stimulus displays. Birds successfully transferred to stimulus displays involving novel colors, indicating that pigeons learned a general change detection rule. Furthermore, analysis of responses to specific color combinations showed that pigeons could detect changes involving both spectral and non-spectral colors and that accuracy was better for changes involving greater differences in wavelength. These results build upon previous investigations of change blindness in both humans and pigeons and suggest that change blindness may be a general consequence of selective visual attention relevant to multiple species and stimulus dimensions.

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.

Remote detection device and detection method therefor

International Nuclear Information System (INIS)

Kogure, Sumio; Yoshida, Yoji; Matsuo, Takashiro; Takehara, Hidetoshi; Kojima, Shinsaku.

1997-01-01

The present invention provides a non-destructive detection device for collectively, efficiently and effectively conducting maintenance and detection for confirming the integrity of a nuclear reactor by way of a shielding member for shielding radiation rays generated from an objective portion to be detected. Namely, devices for direct visual detection using an under water TV camera as a sensor, an eddy current detection using a coil as a sensor and each magnetic powder flow detection are integrated and applied collectively. Specifically, the visual detection by using the TV camera and the eddy current flaw detection are adopted together. The flaw detection with magnetic powder is applied as a means for confirming the results of the two kinds of detections by other method. With such procedures, detection techniques using respective specific theories are combined thereby enabling to enhance the accuracy for the evaluation of the detection. (I.S.)

Short-term change detection for UAV video

Science.gov (United States)

Saur, Günter; Krüger, Wolfgang

2012-11-01

In the last years, there has been an increased use of unmanned aerial vehicles (UAV) for video reconnaissance and surveillance. An important application in this context is change detection in UAV video data. Here we address short-term change detection, in which the time between observations ranges from several minutes to a few hours. We distinguish this task from video motion detection (shorter time scale) and from long-term change detection, based on time series of still images taken between several days, weeks, or even years. Examples for relevant changes we are looking for are recently parked or moved vehicles. As a pre-requisite, a precise image-to-image registration is needed. Images are selected on the basis of the geo-coordinates of the sensor's footprint and with respect to a certain minimal overlap. The automatic imagebased fine-registration adjusts the image pair to a common geometry by using a robust matching approach to handle outliers. The change detection algorithm has to distinguish between relevant and non-relevant changes. Examples for non-relevant changes are stereo disparity at 3D structures of the scene, changed length of shadows, and compression or transmission artifacts. To detect changes in image pairs we analyzed image differencing, local image correlation, and a transformation-based approach (multivariate alteration detection). As input we used color and gradient magnitude images. To cope with local misalignment of image structures we extended the approaches by a local neighborhood search. The algorithms are applied to several examples covering both urban and rural scenes. The local neighborhood search in combination with intensity and gradient magnitude differencing clearly improved the results. Extended image differencing performed better than both the correlation based approach and the multivariate alternation detection. The algorithms are adapted to be used in semi-automatic workflows for the ABUL video exploitation system of Fraunhofer

Detecting long-term growth trends using tree rings: a critical evaluation of methods.

Science.gov (United States)

Peters, Richard L; Groenendijk, Peter; Vlam, Mart; Zuidema, Pieter A

2015-05-01

Tree-ring analysis is often used to assess long-term trends in tree growth. A variety of growth-trend detection methods (GDMs) exist to disentangle age/size trends in growth from long-term growth changes. However, these detrending methods strongly differ in approach, with possible implications for their output. Here, we critically evaluate the consistency, sensitivity, reliability and accuracy of four most widely used GDMs: conservative detrending (CD) applies mathematical functions to correct for decreasing ring widths with age; basal area correction (BAC) transforms diameter into basal area growth; regional curve standardization (RCS) detrends individual tree-ring series using average age/size trends; and size class isolation (SCI) calculates growth trends within separate size classes. First, we evaluated whether these GDMs produce consistent results applied to an empirical tree-ring data set of Melia azedarach, a tropical tree species from Thailand. Three GDMs yielded similar results - a growth decline over time - but the widely used CD method did not detect any change. Second, we assessed the sensitivity (probability of correct growth-trend detection), reliability (100% minus probability of detecting false trends) and accuracy (whether the strength of imposed trends is correctly detected) of these GDMs, by applying them to simulated growth trajectories with different imposed trends: no trend, strong trends (-6% and +6% change per decade) and weak trends (-2%, +2%). All methods except CD, showed high sensitivity, reliability and accuracy to detect strong imposed trends. However, these were considerably lower in the weak or no-trend scenarios. BAC showed good sensitivity and accuracy, but low reliability, indicating uncertainty of trend detection using this method. Our study reveals that the choice of GDM influences results of growth-trend studies. We recommend applying multiple methods when analysing trends and encourage performing sensitivity and reliability

An ensemble classification approach for improved Land use/cover change detection

Science.gov (United States)

Chellasamy, M.; Ferré, T. P. A.; Humlekrog Greve, M.; Larsen, R.; Chinnasamy, U.

2014-11-01

Change Detection (CD) methods based on post-classification comparison approaches are claimed to provide potentially reliable results. They are considered to be most obvious quantitative method in the analysis of Land Use Land Cover (LULC) changes which provides from - to change information. But, the performance of post-classification comparison approaches highly depends on the accuracy of classification of individual images used for comparison. Hence, we present a classification approach that produce accurate classified results which aids to obtain improved change detection results. Machine learning is a part of broader framework in change detection, where neural networks have drawn much attention. Neural network algorithms adaptively estimate continuous functions from input data without mathematical representation of output dependence on input. A common practice for classification is to use Multi-Layer-Perceptron (MLP) neural network with backpropogation learning algorithm for prediction. To increase the ability of learning and prediction, multiple inputs (spectral, texture, topography, and multi-temporal information) are generally stacked to incorporate diversity of information. On the other hand literatures claims backpropagation algorithm to exhibit weak and unstable learning in use of multiple inputs, while dealing with complex datasets characterized by mixed uncertainty levels. To address the problem of learning complex information, we propose an ensemble classification technique that incorporates multiple inputs for classification unlike traditional stacking of multiple input data. In this paper, we present an Endorsement Theory based ensemble classification that integrates multiple information, in terms of prediction probabilities, to produce final classification results. Three different input datasets are used in this study: spectral, texture and indices, from SPOT-4 multispectral imagery captured on 1998 and 2003. Each SPOT image is classified

An Unsupervised Algorithm for Change Detection in Hyperspectral Remote Sensing Data Using Synthetically Fused Images and Derivative Spectral Profiles

Directory of Open Access Journals (Sweden)

Youkyung Han

2017-01-01

Full Text Available Multitemporal hyperspectral remote sensing data have the potential to detect altered areas on the earth’s surface. However, dissimilar radiometric and geometric properties between the multitemporal data due to the acquisition time or position of the sensors should be resolved to enable hyperspectral imagery for detecting changes in natural and human-impacted areas. In addition, data noise in the hyperspectral imagery spectrum decreases the change-detection accuracy when general change-detection algorithms are applied to hyperspectral images. To address these problems, we present an unsupervised change-detection algorithm based on statistical analyses of spectral profiles; the profiles are generated from a synthetic image fusion method for multitemporal hyperspectral images. This method aims to minimize the noise between the spectra corresponding to the locations of identical positions by increasing the change-detection rate and decreasing the false-alarm rate without reducing the dimensionality of the original hyperspectral data. Using a quantitative comparison of an actual dataset acquired by airborne hyperspectral sensors, we demonstrate that the proposed method provides superb change-detection results relative to the state-of-the-art unsupervised change-detection algorithms.

A colorimetric method for highly sensitive and accurate detection of iodide by finding the critical color in a color change process using silver triangular nanoplates

Energy Technology Data Exchange (ETDEWEB)

Yang, Xiu-Hua; Ling, Jian, E-mail: lingjian@ynu.edu.cn; Peng, Jun; Cao, Qiu-E., E-mail: qecao@ynu.edu.cn; Ding, Zhong-Tao; Bian, Long-Chun

2013-10-10

Graphical abstract: -- Highlights: •Demonstrated a new colorimetric strategy for iodide detection by silver nanoplates. •The colorimetric strategy is to find the critical color in a color change process. •The colorimetric strategy is more accurate and sensitive than common colorimetry. •Discovered a new morphological transformation phenomenon of silver nanoplates. -- Abstract: In this contribution, we demonstrated a novel colorimetric method for highly sensitive and accurate detection of iodide using citrate-stabilized silver triangular nanoplates (silver TNPs). Very lower concentration of iodide can induce an appreciable color change of silver TNPs solution from blue to yellow by fusing of silver TNPs to nanoparticles, as confirmed by UV–vis absorption spectroscopy and transmission electron microscopy (TEM). The principle of this colorimetric assay is not an ordinary colorimetry, but a new colorimetric strategy by finding the critical color in a color change process. With this strategy, 0.1 μM of iodide can be recognized within 30 min by naked-eyes observation, and lower concentration of iodide down to 8.8 nM can be detected using a spectrophotometer. Furthermore, this high sensitive colorimetric assay has good accuracy, stability and reproducibility comparing with other ordinary colorimetry. We believe this new colorimetric method will open up a fresh insight of simple, rapid and reliable detection of iodide and can find its future application in the biochemical analysis or clinical diagnosis.

A colorimetric method for highly sensitive and accurate detection of iodide by finding the critical color in a color change process using silver triangular nanoplates

International Nuclear Information System (INIS)

Yang, Xiu-Hua; Ling, Jian; Peng, Jun; Cao, Qiu-E.; Ding, Zhong-Tao; Bian, Long-Chun

2013-01-01

Graphical abstract: -- Highlights: •Demonstrated a new colorimetric strategy for iodide detection by silver nanoplates. •The colorimetric strategy is to find the critical color in a color change process. •The colorimetric strategy is more accurate and sensitive than common colorimetry. •Discovered a new morphological transformation phenomenon of silver nanoplates. -- Abstract: In this contribution, we demonstrated a novel colorimetric method for highly sensitive and accurate detection of iodide using citrate-stabilized silver triangular nanoplates (silver TNPs). Very lower concentration of iodide can induce an appreciable color change of silver TNPs solution from blue to yellow by fusing of silver TNPs to nanoparticles, as confirmed by UV–vis absorption spectroscopy and transmission electron microscopy (TEM). The principle of this colorimetric assay is not an ordinary colorimetry, but a new colorimetric strategy by finding the critical color in a color change process. With this strategy, 0.1 μM of iodide can be recognized within 30 min by naked-eyes observation, and lower concentration of iodide down to 8.8 nM can be detected using a spectrophotometer. Furthermore, this high sensitive colorimetric assay has good accuracy, stability and reproducibility comparing with other ordinary colorimetry. We believe this new colorimetric method will open up a fresh insight of simple, rapid and reliable detection of iodide and can find its future application in the biochemical analysis or clinical diagnosis

3D change detection at street level using mobile laser scanning point clouds and terrestrial images

Science.gov (United States)

Qin, Rongjun; Gruen, Armin

2014-04-01

Automatic change detection and geo-database updating in the urban environment are difficult tasks. There has been much research on detecting changes with satellite and aerial images, but studies have rarely been performed at the street level, which is complex in its 3D geometry. Contemporary geo-databases include 3D street-level objects, which demand frequent data updating. Terrestrial images provides rich texture information for change detection, but the change detection with terrestrial images from different epochs sometimes faces problems with illumination changes, perspective distortions and unreliable 3D geometry caused by the lack of performance of automatic image matchers, while mobile laser scanning (MLS) data acquired from different epochs provides accurate 3D geometry for change detection, but is very expensive for periodical acquisition. This paper proposes a new method for change detection at street level by using combination of MLS point clouds and terrestrial images: the accurate but expensive MLS data acquired from an early epoch serves as the reference, and terrestrial images or photogrammetric images captured from an image-based mobile mapping system (MMS) at a later epoch are used to detect the geometrical changes between different epochs. The method will automatically mark the possible changes in each view, which provides a cost-efficient method for frequent data updating. The methodology is divided into several steps. In the first step, the point clouds are recorded by the MLS system and processed, with data cleaned and classified by semi-automatic means. In the second step, terrestrial images or mobile mapping images at a later epoch are taken and registered to the point cloud, and then point clouds are projected on each image by a weighted window based z-buffering method for view dependent 2D triangulation. In the next step, stereo pairs of the terrestrial images are rectified and re-projected between each other to check the geometrical

Comprehensive methods for earlier detection and monitoring of forest decline

Science.gov (United States)

Jennifer Pontius; Richard Hallett

2014-01-01

Forested ecosystems are threatened by invasive pests, pathogens, and unusual climatic events brought about by climate change. Earlier detection of incipient forest health problems and a quantitatively rigorous assessment method is increasingly important. Here, we describe a method that is adaptable across tree species and stress agents and practical for use in the...

A Fast Framework for Abrupt Change Detection Based on Binary Search Trees and Kolmogorov Statistic

Science.gov (United States)

Qi, Jin-Peng; Qi, Jie; Zhang, Qing

2016-01-01

Change-Point (CP) detection has attracted considerable attention in the fields of data mining and statistics; it is very meaningful to discuss how to quickly and efficiently detect abrupt change from large-scale bioelectric signals. Currently, most of the existing methods, like Kolmogorov-Smirnov (KS) statistic and so forth, are time-consuming, especially for large-scale datasets. In this paper, we propose a fast framework for abrupt change detection based on binary search trees (BSTs) and a modified KS statistic, named BSTKS (binary search trees and Kolmogorov statistic). In this method, first, two binary search trees, termed as BSTcA and BSTcD, are constructed by multilevel Haar Wavelet Transform (HWT); second, three search criteria are introduced in terms of the statistic and variance fluctuations in the diagnosed time series; last, an optimal search path is detected from the root to leaf nodes of two BSTs. The studies on both the synthetic time series samples and the real electroencephalograph (EEG) recordings indicate that the proposed BSTKS can detect abrupt change more quickly and efficiently than KS, t-statistic (t), and Singular-Spectrum Analyses (SSA) methods, with the shortest computation time, the highest hit rate, the smallest error, and the highest accuracy out of four methods. This study suggests that the proposed BSTKS is very helpful for useful information inspection on all kinds of bioelectric time series signals. PMID:27413364

A Fast Framework for Abrupt Change Detection Based on Binary Search Trees and Kolmogorov Statistic.

Science.gov (United States)

Qi, Jin-Peng; Qi, Jie; Zhang, Qing

2016-01-01

Change-Point (CP) detection has attracted considerable attention in the fields of data mining and statistics; it is very meaningful to discuss how to quickly and efficiently detect abrupt change from large-scale bioelectric signals. Currently, most of the existing methods, like Kolmogorov-Smirnov (KS) statistic and so forth, are time-consuming, especially for large-scale datasets. In this paper, we propose a fast framework for abrupt change detection based on binary search trees (BSTs) and a modified KS statistic, named BSTKS (binary search trees and Kolmogorov statistic). In this method, first, two binary search trees, termed as BSTcA and BSTcD, are constructed by multilevel Haar Wavelet Transform (HWT); second, three search criteria are introduced in terms of the statistic and variance fluctuations in the diagnosed time series; last, an optimal search path is detected from the root to leaf nodes of two BSTs. The studies on both the synthetic time series samples and the real electroencephalograph (EEG) recordings indicate that the proposed BSTKS can detect abrupt change more quickly and efficiently than KS, t-statistic (t), and Singular-Spectrum Analyses (SSA) methods, with the shortest computation time, the highest hit rate, the smallest error, and the highest accuracy out of four methods. This study suggests that the proposed BSTKS is very helpful for useful information inspection on all kinds of bioelectric time series signals.

A new ultrasonic signal amplification method for detection of bacteria

Science.gov (United States)

Kant Shukla, Shiva; Resa López, Pablo; Sierra Sánchez, Carlos; Urréjola, José; Segura, Luis Elvira

2012-10-01

A new method is presented that increases the sensitivity of ultrasound-based techniques for detection of bacteria. The technique was developed for the detection of catalase-positive microorganisms. It uses a bubble trapping medium containing hydrogen peroxide that is mixed with the sample for microbiological evaluation. The enzyme catalase is present in catalase-positive bacteria, which induces a rapid hydrolysis of hydrogen peroxide, forming bubbles which remain in the medium. This reaction results in the amplification of the mechanical changes that the microorganisms produce in the medium. The effect can be detected by means of ultrasonic wave amplitude continuous measurement since the bubbles increase the ultrasonic attenuation significantly. It is shown that microorganism concentrations of the order of 105 cells ml-1 can be detected using this method. This allows an improvement of three orders of magnitude in the ultrasonic detection threshold of microorganisms in conventional culture media, and is competitive with modern rapid microbiological methods. It can also be used for the characterization of the enzymatic activity.

A new ultrasonic signal amplification method for detection of bacteria

International Nuclear Information System (INIS)

Shukla, Shiva Kant; López, Pablo Resa; Sánchez, Carlos Sierra; Segura, Luis Elvira; Urréjola, José

2012-01-01

A new method is presented that increases the sensitivity of ultrasound-based techniques for detection of bacteria. The technique was developed for the detection of catalase-positive microorganisms. It uses a bubble trapping medium containing hydrogen peroxide that is mixed with the sample for microbiological evaluation. The enzyme catalase is present in catalase-positive bacteria, which induces a rapid hydrolysis of hydrogen peroxide, forming bubbles which remain in the medium. This reaction results in the amplification of the mechanical changes that the microorganisms produce in the medium. The effect can be detected by means of ultrasonic wave amplitude continuous measurement since the bubbles increase the ultrasonic attenuation significantly. It is shown that microorganism concentrations of the order of 10 5 cells ml −1 can be detected using this method. This allows an improvement of three orders of magnitude in the ultrasonic detection threshold of microorganisms in conventional culture media, and is competitive with modern rapid microbiological methods. It can also be used for the characterization of the enzymatic activity. (paper)

Structural Damage Detection Using Changes in Natural Frequencies: Theory and Applications

Science.gov (United States)

He, K.; Zhu, W. D.

2011-07-01

A vibration-based method that uses changes in natural frequencies of a structure to detect damage has advantages over conventional nondestructive tests in detecting various types of damage, including loosening of bolted joints, using minimum measurement data. Two major challenges associated with applications of the vibration-based damage detection method to engineering structures are addressed: accurate modeling of structures and the development of a robust inverse algorithm to detect damage, which are defined as the forward and inverse problems, respectively. To resolve the forward problem, new physics-based finite element modeling techniques are developed for fillets in thin-walled beams and for bolted joints, so that complex structures can be accurately modeled with a reasonable model size. To resolve the inverse problem, a logistical function transformation is introduced to convert the constrained optimization problem to an unconstrained one, and a robust iterative algorithm using a trust-region method, called the Levenberg-Marquardt method, is developed to accurately detect the locations and extent of damage. The new methodology can ensure global convergence of the iterative algorithm in solving under-determined system equations and deal with damage detection problems with relatively large modeling error and measurement noise. The vibration-based damage detection method is applied to various structures including lightning masts, a space frame structure and one of its components, and a pipeline. The exact locations and extent of damage can be detected in the numerical simulation where there is no modeling error and measurement noise. The locations and extent of damage can be successfully detected in experimental damage detection.

Structural Damage Detection Using Changes in Natural Frequencies: Theory and Applications

International Nuclear Information System (INIS)

He, K; Zhu, W D

2011-01-01

A vibration-based method that uses changes in natural frequencies of a structure to detect damage has advantages over conventional nondestructive tests in detecting various types of damage, including loosening of bolted joints, using minimum measurement data. Two major challenges associated with applications of the vibration-based damage detection method to engineering structures are addressed: accurate modeling of structures and the development of a robust inverse algorithm to detect damage, which are defined as the forward and inverse problems, respectively. To resolve the forward problem, new physics-based finite element modeling techniques are developed for fillets in thin-walled beams and for bolted joints, so that complex structures can be accurately modeled with a reasonable model size. To resolve the inverse problem, a logistical function transformation is introduced to convert the constrained optimization problem to an unconstrained one, and a robust iterative algorithm using a trust-region method, called the Levenberg-Marquardt method, is developed to accurately detect the locations and extent of damage. The new methodology can ensure global convergence of the iterative algorithm in solving under-determined system equations and deal with damage detection problems with relatively large modeling error and measurement noise. The vibration-based damage detection method is applied to various structures including lightning masts, a space frame structure and one of its components, and a pipeline. The exact locations and extent of damage can be detected in the numerical simulation where there is no modeling error and measurement noise. The locations and extent of damage can be successfully detected in experimental damage detection.

Video change detection for fixed wing UAVs

Science.gov (United States)

Bartelsen, Jan; Müller, Thomas; Ring, Jochen; Mück, Klaus; Brüstle, Stefan; Erdnüß, Bastian; Lutz, Bastian; Herbst, Theresa

2017-10-01

In this paper we proceed the work of Bartelsen et al.1 We present the draft of a process chain for an image based change detection which is designed for videos acquired by fixed wing unmanned aerial vehicles (UAVs). From our point of view, automatic video change detection for aerial images can be useful to recognize functional activities which are typically caused by the deployment of improvised explosive devices (IEDs), e.g. excavations, skid marks, footprints, left-behind tooling equipment, and marker stones. Furthermore, in case of natural disasters, like flooding, imminent danger can be recognized quickly. Due to the necessary flight range, we concentrate on fixed wing UAVs. Automatic change detection can be reduced to a comparatively simple photogrammetric problem when the perspective change between the "before" and "after" image sets is kept as small as possible. Therefore, the aerial image acquisition demands a mission planning with a clear purpose including flight path and sensor configuration. While the latter can be enabled simply by a fixed and meaningful adjustment of the camera, ensuring a small perspective change for "before" and "after" videos acquired by fixed wing UAVs is a challenging problem. Concerning this matter, we have performed tests with an advanced commercial off the shelf (COTS) system which comprises a differential GPS and autopilot system estimating the repetition accuracy of its trajectory. Although several similar approaches have been presented,23 as far as we are able to judge, the limits for this important issue are not estimated so far. Furthermore, we design a process chain to enable the practical utilization of video change detection. It consists of a front-end of a database to handle large amounts of video data, an image processing and change detection implementation, and the visualization of the results. We apply our process chain on the real video data acquired by the advanced COTS fixed wing UAV and synthetic data. For the

Early detection of structual changes in random signal

International Nuclear Information System (INIS)

Kuroda, Yoshiteru; Yokota, Katsuhiro

1981-01-01

Early detection of structual changes in observed random signal is very important from the point of system diagnosis. In this paper, the following procedures are applied to this problem and the results are compared. (1) auto-regressive model to random signal to calculate the prediction error, i.e., the defference between observed and predicted values. (2) auto-regressive method to caluculate the sum of the prediction error. (3) a method is based on AIC (Akaike Information Criterion). Simulation is made of these procedures, indicating their merits and demerits as a diagostic tools. (author)

A comparison of change detection measurements using object-based and pixel-based classification methods on western juniper dominated woodlands in eastern Oregon

Directory of Open Access Journals (Sweden)

Ryan G. Howell

2017-03-01

Full Text Available Encroachment of pinyon (Pinus spp and juniper (Juniperus spp. woodlands in western North America is considered detrimental due to its effects on ecohydrology, plant community structure, and soil stability. Management plans at the federal, state, and private level often include juniper removal for improving habitat of sensitive species and maintaining sustainable ecosystem processes. Remote sensing has become a useful tool in determining changes in juniper woodland structure because of its uses in comparing archived historic imagery with newly available multispectral images to provide information on changes that are no longer detectable by field measurements. Change in western juniper (J. occidentalis cover was detected following juniper removal treatments between 1995 and 2011 using panchromatic 1-meter NAIP and 4-band 1-meter NAIP imagery, respectively. Image classification was conducted using remotely sensed images taken at the Roaring Springs Ranch in southeastern Oregon. Feature Analyst for ArcGIS (object-based extraction and a supervised classification with ENVI 5.2 (pixel-based extraction were used to delineate juniper canopy cover. Image classification accuracy was calculated using an Accuracy Assessment and Kappa Statistic. Both methods showed approximately a 76% decrease in western juniper cover, although differing in total canopy cover area, with object-based classification being more accurate. Classification results for the 2011 imagery were much more accurate (0.99 Kappa statistic because of its low juniper density and the presence of an infrared band. The development of methods for detecting change in juniper cover can lead to more accurate and efficient data acquisition and subsequently improved land management and monitoring practices. These data can subsequently be used to assess and quantify juniper invasion and succession, potential ecological impacts, and plant community resilience.

Street-side vehicle detection, classification and change detection using mobile laser scanning data

Science.gov (United States)

Xiao, Wen; Vallet, Bruno; Schindler, Konrad; Paparoditis, Nicolas

2016-04-01

Statistics on street-side car parks, e.g. occupancy rates, parked vehicle types, parking durations, are of great importance for urban planning and policy making. Related studies, e.g. vehicle detection and classification, mostly focus on static images or video. Whereas mobile laser scanning (MLS) systems are increasingly utilized for urban street environment perception due to their direct 3D information acquisition, high accuracy and movability. In this paper, we design a complete system for car park monitoring, including vehicle recognition, localization, classification and change detection, from laser scanning point clouds. The experimental data are acquired by an MLS system using high frequency laser scanner which scans the streets vertically along the system's moving trajectory. The point clouds are firstly classified as ground, building façade, and street objects which are then segmented using state-of-the-art methods. Each segment is treated as an object hypothesis, and its geometric features are extracted. Moreover, a deformable vehicle model is fitted to each object. By fitting an explicit model to the vehicle points, detailed information, such as precise position and orientation, can be obtained. The model parameters are also treated as vehicle features. Together with the geometric features, they are applied to a supervised learning procedure for vehicle or non-vehicle recognition. The classes of detected vehicles are also investigated. Whether vehicles have changed across two datasets acquired at different times is detected to estimate the durations. Here, vehicles are trained pair-wisely. Two same or different vehicles are paired up as training samples. As a result, the vehicle recognition, classification and change detection accuracies are 95.9%, 86.0% and 98.7%, respectively. Vehicle modelling improves not only the recognition rate, but also the localization precision compared to bounding boxes.

Is air-displacement plethysmography a reliable method of detecting ongoing changes in percent body fat within obese children involved in a weight management program?

DEFF Research Database (Denmark)

Ewane, Cecile; McConkey, Stacy A; Kreiter, Clarence D

2010-01-01

(percent body fat) over time. The gold standard method, hydrodensitometry, has severe limitations for the pediatric population. OBJECTIVE: This study examines the reliability of air-displacement plethysmography (ADP) in detecting percent body fat changes within obese children over time. METHODS: Percent...... body fat by ADP, weight, and body mass index (BMI) were measured for eight obese children aged 5-12 years enrolled in a weight management program over a 12-month period. These measurements were taken at initial evaluation, 1.5 months, 3 months, 6 months, and 12 months to monitor the progress...... of the subjects and detect any changes in these measures over time. Statistical analysis was used to determine the reliability of the data collected. RESULTS: The reliability estimate for percent body fat by ADP was 0.78. This was much lower than the reliability of BMI, 0.98, and weight measurements, 0...

Doppler method leak detection for LMFBR steam generators. Pt. 3. Investigation of detection sensitivity and method

International Nuclear Information System (INIS)

Kumagai, Hiromichi; Kinoshita, Izumi

2001-01-01

To prevent the expansion of tube damage and to maintain structural integrity in the steam generators (SGs) of a fast breeder reactor (FBR), it is necessary to detect precisely and immediately any leakage of water from heat transfer tubes. Therefore, the Doppler method was developed. Previous studies have revealed that, in the SG full-sector model that simulates actual SGs, the Doppler method can detect bubbles of 0.4 l/s within a few seconds. However in consideration of the dissolution rate of hydrogen generated by a sodium-water reaction even from a small water leak, it is necessary to detect smaller leakages of water from the heat transfer tubes. The detection sensitivity of the Doppler method and the influence of background noise were experimentally investigated. In-water experiments were performed using the SG model. The results show that the Doppler method can detect bubbles of 0.01 l/s (equivalent to a water leak rate of about 0.01 g/s) within a few seconds and that the background noise has little effect on water leak detection performance. The Doppler method thus has great potential for the detection of water leakage in SGs. (author)

Reflectance conversion methods for the VIS/NIR imaging spectrometer aboard the Chang'E-3 lunar rover: based on ground validation experiment data

International Nuclear Information System (INIS)

Liu Bin; Liu Jian-Zhong; Zhang Guang-Liang; Zou Yong-Liao; Ling Zong-Cheng; Zhang Jiang; He Zhi-Ping; Yang Ben-Yong

2013-01-01

The second phase of the Chang'E Program (also named Chang'E-3) has the goal to land and perform in-situ detection on the lunar surface. A VIS/NIR imaging spectrometer (VNIS) will be carried on the Chang'E-3 lunar rover to detect the distribution of lunar minerals and resources. VNIS is the first mission in history to perform in-situ spectral measurement on the surface of the Moon, the reflectance data of which are fundamental for interpretation of lunar composition, whose quality would greatly affect the accuracy of lunar element and mineral determination. Until now, in-situ detection by imaging spectrometers was only performed by rovers on Mars. We firstly review reflectance conversion methods for rovers on Mars (Viking landers, Pathfinder and Mars Exploration rovers, etc). Secondly, we discuss whether these conversion methods used on Mars can be applied to lunar in-situ detection. We also applied data from a laboratory bidirectional reflectance distribution function (BRDF) using simulated lunar soil to test the availability of this method. Finally, we modify reflectance conversion methods used on Mars by considering differences between environments on the Moon and Mars and apply the methods to experimental data obtained from the ground validation of VNIS. These results were obtained by comparing reflectance data from the VNIS measured in the laboratory with those from a standard spectrometer obtained at the same time and under the same observing conditions. The shape and amplitude of the spectrum fits well, and the spectral uncertainty parameters for most samples are within 8%, except for the ilmenite sample which has a low albedo. In conclusion, our reflectance conversion method is suitable for lunar in-situ detection.

A Gaussian Process Based Online Change Detection Algorithm for Monitoring Periodic Time Series

Energy Technology Data Exchange (ETDEWEB)

Chandola, Varun [ORNL; Vatsavai, Raju [ORNL

2011-01-01

Online time series change detection is a critical component of many monitoring systems, such as space and air-borne remote sensing instruments, cardiac monitors, and network traffic profilers, which continuously analyze observations recorded by sensors. Data collected by such sensors typically has a periodic (seasonal) component. Most existing time series change detection methods are not directly applicable to handle such data, either because they are not designed to handle periodic time series or because they cannot operate in an online mode. We propose an online change detection algorithm which can handle periodic time series. The algorithm uses a Gaussian process based non-parametric time series prediction model and monitors the difference between the predictions and actual observations within a statistically principled control chart framework to identify changes. A key challenge in using Gaussian process in an online mode is the need to solve a large system of equations involving the associated covariance matrix which grows with every time step. The proposed algorithm exploits the special structure of the covariance matrix and can analyze a time series of length T in O(T^2) time while maintaining a O(T) memory footprint, compared to O(T^4) time and O(T^2) memory requirement of standard matrix manipulation methods. We experimentally demonstrate the superiority of the proposed algorithm over several existing time series change detection algorithms on a set of synthetic and real time series. Finally, we illustrate the effectiveness of the proposed algorithm for identifying land use land cover changes using Normalized Difference Vegetation Index (NDVI) data collected for an agricultural region in Iowa state, USA. Our algorithm is able to detect different types of changes in a NDVI validation data set (with ~80% accuracy) which occur due to crop type changes as well as disruptive changes (e.g., natural disasters).

A Deep Convolutional Coupling Network for Change Detection Based on Heterogeneous Optical and Radar Images.

Science.gov (United States)

Liu, Jia; Gong, Maoguo; Qin, Kai; Zhang, Puzhao

2018-03-01

We propose an unsupervised deep convolutional coupling network for change detection based on two heterogeneous images acquired by optical sensors and radars on different dates. Most existing change detection methods are based on homogeneous images. Due to the complementary properties of optical and radar sensors, there is an increasing interest in change detection based on heterogeneous images. The proposed network is symmetric with each side consisting of one convolutional layer and several coupling layers. The two input images connected with the two sides of the network, respectively, are transformed into a feature space where their feature representations become more consistent. In this feature space, the different map is calculated, which then leads to the ultimate detection map by applying a thresholding algorithm. The network parameters are learned by optimizing a coupling function. The learning process is unsupervised, which is different from most existing change detection methods based on heterogeneous images. Experimental results on both homogenous and heterogeneous images demonstrate the promising performance of the proposed network compared with several existing approaches.

Machine learning plus optical flow: a simple and sensitive method to detect cardioactive drugs

Science.gov (United States)

Lee, Eugene K.; Kurokawa, Yosuke K.; Tu, Robin; George, Steven C.; Khine, Michelle

2015-07-01

Current preclinical screening methods do not adequately detect cardiotoxicity. Using human induced pluripotent stem cell-derived cardiomyocytes (iPS-CMs), more physiologically relevant preclinical or patient-specific screening to detect potential cardiotoxic effects of drug candidates may be possible. However, one of the persistent challenges for developing a high-throughput drug screening platform using iPS-CMs is the need to develop a simple and reliable method to measure key electrophysiological and contractile parameters. To address this need, we have developed a platform that combines machine learning paired with brightfield optical flow as a simple and robust tool that can automate the detection of cardiomyocyte drug effects. Using three cardioactive drugs of different mechanisms, including those with primarily electrophysiological effects, we demonstrate the general applicability of this screening method to detect subtle changes in cardiomyocyte contraction. Requiring only brightfield images of cardiomyocyte contractions, we detect changes in cardiomyocyte contraction comparable to - and even superior to - fluorescence readouts. This automated method serves as a widely applicable screening tool to characterize the effects of drugs on cardiomyocyte function.

The role of iconic memory in change-detection tasks.

Science.gov (United States)

Becker, M W; Pashler, H; Anstis, S M

2000-01-01

In three experiments, subjects attempted to detect the change of a single item in a visually presented array of items. Subjects' ability to detect a change was greatly reduced if a blank interstimulus interval (ISI) was inserted between the original array and an array in which one item had changed ('change blindness'). However, change detection improved when the location of the change was cued during the blank ISI. This suggests that people represent more information of a scene than change blindness might suggest. We test two possible hypotheses why, in the absence of a cue, this representation fails to produce good change detection. The first claims that the intervening events employed to create change blindness result in multiple neural transients which co-occur with the to-be-detected change. Poor detection rates occur because a serial search of all the transient locations is required to detect the change, during which time the representation of the original scene fades. The second claims that the occurrence of the second frame overwrites the representation of the first frame, unless that information is insulated against overwriting by attention. The results support the second hypothesis. We conclude that people may have a fairly rich visual representation of a scene while the scene is present, but fail to detect changes because they lack the ability to simultaneously represent two complete visual representations.

Alcohol Detection in Exhaled Air by NDIR Method

Science.gov (United States)

Fujitsuka, Norio; Yonemura, Masatoshi; Sakakibara, Kiyomi; Taguchi, Toshiyuki; Wakita, Toshihiro

In recent years, the increase in traffic accidents associated with drunk driving has become a serious social issue. Therefore, there is a need for an in-vehicle system that can detect the fact that the driver is under the influence of alcohol. We thought a method for alcohol detection in the breath of the driver, based on a nondispersive infrared (NDIR) method, is suitable for this system. Since alcohol content in the driver's breath is significantly diluted at the sensor device, it is necessary that the sensor is highly sensitive to detect diluted alcohol. A quantum cascade laser was used to produce highly intense infrared light source. An infrared hollow fiber used in medical treatment was utilized as a gas absorption cell. Since the core of the fiber is hollow, gas is introduced for analyzer. The flexibility of the fiber allowed it to be looped so that 2 m fiber in length could be formed into a compact coil of 29 cm in diameter. It was clarified that the light intensity change of light output from the hollow fiber with ethanol density, and rarefied ethanol as small as 1 ppm in density was detected.

Detecting Changes in Forest Structure over Time with Bi-Temporal Terrestrial Laser Scanning Data

Directory of Open Access Journals (Sweden)

Timo Melkas

2012-10-01

Full Text Available Changes to stems caused by natural forces and timber harvesting constitute an essential input for many forestry-related applications and ecological studies, especially forestry inventories based on the use of permanent sample plots. Conventional field measurement is widely acknowledged as being time-consuming and labor-intensive. More automated and efficient alternatives or supportive methods are needed. Terrestrial laser scanning (TLS has been demonstrated to be a promising method in forestry field inventories. Nevertheless, the applicability of TLS in recording changes in the structure of forest plots has not been studied in detail. This paper presents a fully automated method for detecting changes in forest structure over time using bi-temporal TLS data. The developed method was tested on five densely populated forest plots including 137 trees and 50 harvested trees in point clouds. The present study demonstrated that 90 percent of tree stem changes could be automatically located from single-scan TLS data. These changes accounted for 92 percent of the changed basal area. The results indicate that the processing of TLS data collected at different times to detect tree stem changes can be fully automated.

Support Vector Machines for Multitemporal and Multisensor Change Detection in a Mining Area

Science.gov (United States)

Hecheltjen, Antje; Waske, Bjorn; Thonfeld, Frank; Braun, Matthias; Menz, Gunter

2010-12-01

Long-term change detection often implies the challenge of incorporating multitemporal data from different sensors. Most of the conventional change detection algorithms are designed for bi-temporal datasets from the same sensors detecting only the existence of changes. The labeling of change areas remains a difficult task. To overcome such drawbacks, much attention has been given lately to algorithms arising from machine learning, such as Support Vector Machines (SVMs). While SVMs have been applied successfully for land cover classifications, the exploitation of this approach for change detection is still in its infancy. Few studies have already proven the applicability of SVMs for bi- and multitemporal change detection using data from one sensor only. In this paper we demonstrate the application of SVM for multitemporal and -sensor change detection. Our study site covers lignite open pit mining areas in the German state North Rhine-Westphalia. The dataset consists of bi-temporal Landsat data and multi-temporal ERS SAR data covering two time slots (2001 and 2009). The SVM is conducted using the IDL program imageSVM. Change is deduced from one time slot to the next resulting in two change maps. In contrast to change detection, which is based on post-classification comparison, change detection is seen here as a specific classification problem. Thus, changes are directly classified from a layer-stack of the two years. To reduce the number of change classes, we created a change mask using the magnitude of Change Vector Analysis (CVA). Training data were selected for different change classes (e.g. forest to mining or mining to agriculture) as well as for the no-change classes (e.g. agriculture). Subsequently, they were divided in two independent sets for training the SVMs and accuracy assessment, respectively. Our study shows the applicability of SVMs to classify changes via SVMs. The proposed method yielded a change map of reclaimed and active mines. The use of ERS SAR

Detection of food irradiation - two analytical methods

International Nuclear Information System (INIS)

1994-01-01

This publication summarizes the activities of Nordic countries in the field of detection of irradiated food. The National Food Agency of Denmark has coordinated the project. The two analytical methods investigated were: the gas-chromatographic determination of the hydrocarbon/lipid ratio in irradiated chicken meat, and a bioassay based on microelectrophoresis of DNA from single cells. Also a method for determination of o-tyrosine in the irradiated and non-irradiated chicken meat has been tested. The first method based on radiolytical changes in fatty acids, contained in chicken meat, has been tested and compared in the four Nordic countries. Four major hydrocarbons (C16:2, C16:3, C17:1 and C17:2) have been determined and reasonable agreement was observed between the dose level and hydrocarbons concentration. Results of a bioassay, where strand breaks of DNA are demonstrated by microelectrophoresis of single cells, prove a correlation between the dose levels and the pattern of DNA fragments migration. The hydrocarbon method can be applied to detect other irradiated, fat-containing foods, while the DNA method can be used for some animal and some vegetable foods as well.Both methods allow to determine the fact of food irradiation beyond any doubt, thus making them suitable for food control analysis. The detailed determination protocols are given. (EG)

Change detection on LOD 2 building models with very high resolution spaceborne stereo imagery

Science.gov (United States)

Qin, Rongjun

2014-10-01

Due to the fast development of the urban environment, the need for efficient maintenance and updating of 3D building models is ever increasing. Change detection is an essential step to spot the changed area for data (map/3D models) updating and urban monitoring. Traditional methods based on 2D images are no longer suitable for change detection in building scale, owing to the increased spectral variability of the building roofs and larger perspective distortion of the very high resolution (VHR) imagery. Change detection in 3D is increasingly being investigated using airborne laser scanning data or matched Digital Surface Models (DSM), but rare study has been conducted regarding to change detection on 3D city models with VHR images, which is more informative but meanwhile more complicated. This is due to the fact that the 3D models are abstracted geometric representation of the urban reality, while the VHR images record everything. In this paper, a novel method is proposed to detect changes directly on LOD (Level of Detail) 2 building models with VHR spaceborne stereo images from a different date, with particular focus on addressing the special characteristics of the 3D models. In the first step, the 3D building models are projected onto a raster grid, encoded with building object, terrain object, and planar faces. The DSM is extracted from the stereo imagery by hierarchical semi-global matching (SGM). In the second step, a multi-channel change indicator is extracted between the 3D models and stereo images, considering the inherent geometric consistency (IGC), height difference, and texture similarity for each planar face. Each channel of the indicator is then clustered with the Self-organizing Map (SOM), with "change", "non-change" and "uncertain change" status labeled through a voting strategy. The "uncertain changes" are then determined with a Markov Random Field (MRF) analysis considering the geometric relationship between faces. In the third step, buildings are

Comparison of Methods for Oscillation Detection

DEFF Research Database (Denmark)

Odgaard, Peter Fogh; Trangbæk, Klaus

2006-01-01

This paper compares a selection of methods for detecting oscillations in control loops. The methods are tested on measurement data from a coal-fired power plant, where some oscillations are occurring. Emphasis is put on being able to detect oscillations without having a system model and without...... using process knowledge. The tested methods show potential for detecting the oscillations, however, transient components in the signals cause false detections as well, motivating usage of models in order to remove the expected signals behavior....

Building change detection via a combination of CNNs using only RGB aerial imageries

Science.gov (United States)

Nemoto, Keisuke; Hamaguchi, Ryuhei; Sato, Masakazu; Fujita, Aito; Imaizumi, Tomoyuki; Hikosaka, Shuhei

2017-10-01

Building change information extracted from remote sensing imageries is important for various applications such as urban management and marketing planning. The goal of this work is to develop a methodology for automatically capturing building changes from remote sensing imageries. Recent studies have addressed this goal by exploiting 3-D information as a proxy for building height. In contrast, because in practice it is expensive or impossible to prepare 3-D information, we do not rely on 3-D data but focus on using only RGB aerial imageries. Instead, we employ deep convolutional neural networks (CNNs) to extract effective features, and improve change detection accuracy in RGB remote sensing imageries. We consider two aspects of building change detection, building detection and subsequent change detection. Our proposed methodology was tested on several areas, which has some differences such as dominant building characteristics and varying brightness values. On all over the tested areas, the proposed method provides good results for changed objects, with recall values over 75 % with a strict overlap requirement of over 50% in intersection-over-union (IoU). When the IoU threshold was relaxed to over 10%, resulting recall values were over 81%. We conclude that use of CNNs enables accurate detection of building changes without employing 3-D information.

A Voltage Quality Detection Method

DEFF Research Database (Denmark)

Chen, Zhe; Wei, Mu

2008-01-01

This paper presents a voltage quality detection method based on a phase-locked loop (PLL) technique. The technique can detect the voltage magnitude and phase angle of each individual phase under both normal and fault power system conditions. The proposed method has the potential to evaluate various...

General survey of detection methods for irradiation foods

International Nuclear Information System (INIS)

Yang, J. S.

1997-01-01

The development of detection techniques is needed, in order for regulating authorities to determine whether or not a particular food sample has been irradiated, and label it accordingly so that a consumer's free choice can be exercised. The chemical and physical changes brought about in foods by practical doses of irradiation are very small, and therefore very sensitive methods are required. A number of promising approaches have been developed and evaluated. These include chemical, physical and biological methods ranging from the very simple to highly sophisticated techniques. (author)

Leak detection by vibrational diagnostic methods

International Nuclear Information System (INIS)

Siklossy, P.

1983-01-01

The possibilities and methods of leak detection due to mechanical failures in nuclear power plants are reviewed on the basis of the literature. Great importance is attributed to vibrational diagnostic methods for their adventageous characteristics which enable them to become final leak detecting methods. The problems of noise analysis, e.g. leak detection by impact sound measurements, probe characteristics, gain problems, probe selection, off-line analysis and correlation functions, types of leak noises etc. are summarized. Leak detection based on noise analysis can be installed additionally to power plants. Its maintenance and testing is simple. On the other hand, it requires special training and measuring methods. (Sz.J.)

Numerical investigations on applicability of permanent magnet method to crack detection in HTS film

Energy Technology Data Exchange (ETDEWEB)

Kamitani, A., E-mail: kamitani@yz.yamagata-u.ac.jp [Yamagata University, 4-3-16, Johnan, Yonezawa, Yamagata 992-8510 (Japan); Takayama, T. [Yamagata University, 4-3-16, Johnan, Yonezawa, Yamagata 992-8510 (Japan); Saitoh, A. [University of Hyogo, 2167, Shosha, Himeji, Hyogo 671-2280 (Japan)

2014-09-15

Highlights: • The defect parameter is defined for characterizing a crack position. • The defect parameter shows a remarkable change only near a crack. • A crack detection method is proposed on the basis of the permanent-magnet method. • The high-speed rough detection can be achieved by means of the proposed method. - Abstract: The scanning permanent-magnet (PM) method was originally developed for determining the spatial distribution of the critical current density in a high-temperature superconducting (HTS) film. In the present study, its applicability to the crack detection in an HTS film is investigated numerically. To this end, a defect parameter is defined for characterizing a crack position and it is calculated along various scanning lines. The results of computations show that, only when the scanning position is near a crack, the defect parameter shows a violent change. On the basis of the behavior of the defect parameter, the method for roughly identifying a crack is also proposed.

Numerical investigations on applicability of permanent magnet method to crack detection in HTS film

International Nuclear Information System (INIS)

Kamitani, A.; Takayama, T.; Saitoh, A.

2014-01-01

Highlights: • The defect parameter is defined for characterizing a crack position. • The defect parameter shows a remarkable change only near a crack. • A crack detection method is proposed on the basis of the permanent-magnet method. • The high-speed rough detection can be achieved by means of the proposed method. - Abstract: The scanning permanent-magnet (PM) method was originally developed for determining the spatial distribution of the critical current density in a high-temperature superconducting (HTS) film. In the present study, its applicability to the crack detection in an HTS film is investigated numerically. To this end, a defect parameter is defined for characterizing a crack position and it is calculated along various scanning lines. The results of computations show that, only when the scanning position is near a crack, the defect parameter shows a violent change. On the basis of the behavior of the defect parameter, the method for roughly identifying a crack is also proposed

Ultrasonic defect detection method for socket welding joint

International Nuclear Information System (INIS)

Tominaga, Masaaki; Matsuo, Toshiyuki; Ueno, Akihiro; Watanabe, Kunimichi; Kawamata, Kunio.

1995-01-01

The present invention provides a method of detecting defects over a wide range of a socket weld portion of various kinds of pipelines used, for example, in a nuclear power plant. Namely, an inclined probe is disposed to a jig for detecting defects by ultrasonic waves. This is rotated at least by one turn along the peripheral surface of the material to be detected such as weld tube joints. Defects of weld portion of the material can be detected automatically by using ultrasonic waves during the rotation. The inclined probe for detecting defects by ultrasonic waves comprises a transmission portion having a planar transmittance oscillator disposed to a wedge on the transmission side and a receiving portion comprising a planar receiving oscillator disposed to a wedge on the receiving side. With such a constitution, ultrasonic waves are emitted from the transmission portion to the defect detection portion in the welded portion. If a defect is present, defective echo is reflected to the receiving portion disposed ahead of the probe. Since the defective echo changes depending on the height of the detective portion, the estimation of the height of the defect can be facilitated. (I.S.)

Detection and localization of change points in temporal networks with the aid of stochastic block models

Science.gov (United States)

De Ridder, Simon; Vandermarliere, Benjamin; Ryckebusch, Jan

2016-11-01

A framework based on generalized hierarchical random graphs (GHRGs) for the detection of change points in the structure of temporal networks has recently been developed by Peel and Clauset (2015 Proc. 29th AAAI Conf. on Artificial Intelligence). We build on this methodology and extend it to also include the versatile stochastic block models (SBMs) as a parametric family for reconstructing the empirical networks. We use five different techniques for change point detection on prototypical temporal networks, including empirical and synthetic ones. We find that none of the considered methods can consistently outperform the others when it comes to detecting and locating the expected change points in empirical temporal networks. With respect to the precision and the recall of the results of the change points, we find that the method based on a degree-corrected SBM has better recall properties than other dedicated methods, especially for sparse networks and smaller sliding time window widths.

The fate of object memory traces under change detection and change blindness.

Science.gov (United States)

Busch, Niko A

2013-07-03

Observers often fail to detect substantial changes in a visual scene. This so-called change blindness is often taken as evidence that visual representations are sparse and volatile. This notion rests on the assumption that the failure to detect a change implies that representations of the changing objects are lost all together. However, recent evidence suggests that under change blindness, object memory representations may be formed and stored, but not retrieved. This study investigated the fate of object memory representations when changes go unnoticed. Participants were presented with scenes consisting of real world objects, one of which changed on each trial, while recording event-related potentials (ERPs). Participants were first asked to localize where the change had occurred. In an additional recognition task, participants then discriminated old objects, either from the pre-change or the post-change scene, from entirely new objects. Neural traces of object memories were studied by comparing ERPs for old and novel objects. Participants performed poorly in the detection task and often failed to recognize objects from the scene, especially pre-change objects. However, a robust old/novel effect was observed in the ERP, even when participants were change blind and did not recognize the old object. This implicit memory trace was found both for pre-change and post-change objects. These findings suggest that object memories are stored even under change blindness. Thus, visual representations may not be as sparse and volatile as previously thought. Rather, change blindness may point to a failure to retrieve and use these representations for change detection. Copyright © 2013 Elsevier B.V. All rights reserved.

Building Change Detection from Bi-Temporal Dense-Matching Point Clouds and Aerial Images.

Science.gov (United States)

Pang, Shiyan; Hu, Xiangyun; Cai, Zhongliang; Gong, Jinqi; Zhang, Mi

2018-03-24

In this work, a novel building change detection method from bi-temporal dense-matching point clouds and aerial images is proposed to address two major problems, namely, the robust acquisition of the changed objects above ground and the automatic classification of changed objects into buildings or non-buildings. For the acquisition of changed objects above ground, the change detection problem is converted into a binary classification, in which the changed area above ground is regarded as the foreground and the other area as the background. For the gridded points of each period, the graph cuts algorithm is adopted to classify the points into foreground and background, followed by the region-growing algorithm to form candidate changed building objects. A novel structural feature that was extracted from aerial images is constructed to classify the candidate changed building objects into buildings and non-buildings. The changed building objects are further classified as "newly built", "taller", "demolished", and "lower" by combining the classification and the digital surface models of two periods. Finally, three typical areas from a large dataset are used to validate the proposed method. Numerous experiments demonstrate the effectiveness of the proposed algorithm.

Land-cover change detection

Science.gov (United States)

Chen, Xuexia; Giri, Chandra; Vogelmann, James

2012-01-01

Land cover is the biophysical material on the surface of the earth. Land-cover types include grass, shrubs, trees, barren, water, and man-made features. Land cover changes continuously.  The rate of change can be either dramatic and abrupt, such as the changes caused by logging, hurricanes and fire, or subtle and gradual, such as regeneration of forests and damage caused by insects (Verbesselt et al., 2001).  Previous studies have shown that land cover has changed dramatically during the past sevearal centuries and that these changes have severely affected our ecosystems (Foody, 2010; Lambin et al., 2001). Lambin and Strahlers (1994b) summarized five types of cause for land-cover changes: (1) long-term natural changes in climate conditions, (2) geomorphological and ecological processes, (3) human-induced alterations of vegetation cover and landscapes, (4) interannual climate variability, and (5) human-induced greenhouse effect.  Tools and techniques are needed to detect, describe, and predict these changes to facilitate sustainable management of natural resources.

A current detection based on an extension of the Prony's method

Energy Technology Data Exchange (ETDEWEB)

Deng, C.; Xia, X.; Gong, F. [Changsha Univ. of Science and Technology, Changsha (China). College of Electrical Engineering

2009-07-01

The Prony method for spectrum estimation was combined with an adaptive frequency tracking and current frequency demultiplication method as a harmonic detection system. An injective active power filter was used for effective harmonic wave management. The automated system includes a digital signal processor and a high-speed interfacing device. An algorithm was developed to consider the slow voltage changes in the power grid as well as potential mutations in current harmonics. Widrow-Hoff's LMS algorithm was used to develop rolling steps for the filter. The method was used to detect harmonic waves with a sampling frequency of 400 Hz. Results of the study showed that the detection method can be used in real time to detect fundamental and first harmonics in electric power grids. 10 refs., 2 tabs., 1 fig.

Automatic change detection and quantification of dermatological diseases with an application to psoriasis images

DEFF Research Database (Denmark)

Gomez, David Delgado; Butakoff, C.; Ersbøll, Bjarne Kjær

2007-01-01

Change monitoring in skin lesion analysis has proven to be a useful adjunct in their assessment. This article presents a comparative study of the available change detection techniques applied to change visualization and quantification in bi-temporal psoriasis images. The chosen methods are evalua...

Recent and innovative methods for detection of bacteremia and fungemia

International Nuclear Information System (INIS)

Reller, L.B.

1983-01-01

Advances continue to be made in methods for more reliable or more rapid means of detecting bacteremia and fungemia. The importance of blood sample volume and broth dilution has been established in controlled studies. New technology includes the use of resins that remove antimicrobials from blood samples, detection of radioactivity from organisms given radiolabeled substrate, use of dyes that stain microbial DNA and RNA, use of slides coated with growth media, and lysis-centrifugation for trapping microorganisms. Technology now being considered includes counterimmunoelectrophoresis, head-space gas chromatography, electrical impedance, microcalorimetry, and the use of lasers to detect pH changes and turbidity

Interpreting the change detection error matrix

NARCIS (Netherlands)

Oort, van P.A.J.

2007-01-01

Two different matrices are commonly reported in assessment of change detection accuracy: (1) single date error matrices and (2) binary change/no change error matrices. The third, less common form of reporting, is the transition error matrix. This paper discuses the relation between these matrices.

What lies beneath: detecting sub-canopy changes in savanna woodlands using a three-dimensional classification method

CSIR Research Space (South Africa)

Fisher, JT

2015-07-01

Full Text Available structural diversity. A 3D classification approach was successful in detecting fine-scale, short-term changes between land uses, and can thus be used as amonitoring tool for savannawoody vegetation structure....

Fast and Robust Segmentation and Classification for Change Detection in Urban Point Clouds

Science.gov (United States)

Roynard, X.; Deschaud, J.-E.; Goulette, F.

2016-06-01

Change detection is an important issue in city monitoring to analyse street furniture, road works, car parking, etc. For example, parking surveys are needed but are currently a laborious task involving sending operators in the streets to identify the changes in car locations. In this paper, we propose a method that performs a fast and robust segmentation and classification of urban point clouds, that can be used for change detection. We apply this method to detect the cars, as a particular object class, in order to perform parking surveys automatically. A recently proposed method already addresses the need for fast segmentation and classification of urban point clouds, using elevation images. The interest to work on images is that processing is much faster, proven and robust. However there may be a loss of information in complex 3D cases: for example when objects are one above the other, typically a car under a tree or a pedestrian under a balcony. In this paper we propose a method that retain the three-dimensional information while preserving fast computation times and improving segmentation and classification accuracy. It is based on fast region-growing using an octree, for the segmentation, and specific descriptors with Random-Forest for the classification. Experiments have been performed on large urban point clouds acquired by Mobile Laser Scanning. They show that the method is as fast as the state of the art, and that it gives more robust results in the complex 3D cases.

Ice Sheet Change Detection by Satellite Image Differencing

Science.gov (United States)

Bindschadler, Robert A.; Scambos, Ted A.; Choi, Hyeungu; Haran, Terry M.

2010-01-01

Differencing of digital satellite image pairs highlights subtle changes in near-identical scenes of Earth surfaces. Using the mathematical relationships relevant to photoclinometry, we examine the effectiveness of this method for the study of localized ice sheet surface topography changes using numerical experiments. We then test these results by differencing images of several regions in West Antarctica, including some where changes have previously been identified in altimeter profiles. The technique works well with coregistered images having low noise, high radiometric sensitivity, and near-identical solar illumination geometry. Clouds and frosts detract from resolving surface features. The ETM(plus) sensor on Landsat-7, ALI sensor on EO-1, and MODIS sensor on the Aqua and Terra satellite platforms all have potential for detecting localized topographic changes such as shifting dunes, surface inflation and deflation features associated with sub-glacial lake fill-drain events, or grounding line changes. Availability and frequency of MODIS images favor this sensor for wide application, and using it, we demonstrate both qualitative identification of changes in topography and quantitative mapping of slope and elevation changes.

Neural correlates of change detection and change blindness in a working memory task.

Science.gov (United States)

Pessoa, Luiz; Ungerleider, Leslie G

2004-05-01

Detecting changes in an ever-changing environment is highly advantageous, and this ability may be critical for survival. In the present study, we investigated the neural substrates of change detection in the context of a visual working memory task. Subjects maintained a sample visual stimulus in short-term memory for 6 s, and were asked to indicate whether a subsequent, test stimulus matched or did not match the original sample. To study change detection largely uncontaminated by attentional state, we compared correct change and correct no-change trials at test. Our results revealed that correctly detecting a change was associated with activation of a network comprising parietal and frontal brain regions, as well as activation of the pulvinar, cerebellum, and inferior temporal gyrus. Moreover, incorrectly reporting a change when none occurred led to a very similar pattern of activations. Finally, few regions were differentially activated by trials in which a change occurred but subjects failed to detect it (change blindness). Thus, brain activation was correlated with a subject's report of a change, instead of correlated with the physical change per se. We propose that frontal and parietal regions, possibly assisted by the cerebellum and the pulvinar, might be involved in controlling the deployment of attention to the location of a change, thereby allowing further processing of the visual stimulus. Visual processing areas, such as the inferior temporal gyrus, may be the recipients of top-down feedback from fronto-parietal regions that control the reactive deployment of attention, and thus exhibit increased activation when a change is reported (irrespective of whether it occurred or not). Whereas reporting that a change occurred, be it correctly or incorrectly, was associated with strong activation in fronto-parietal sites, change blindness appears to involve very limited territories.

Rapid methods for detection of bacteria

DEFF Research Database (Denmark)

Corfitzen, Charlotte B.; Andersen, B.Ø.; Miller, M.

2006-01-01

Traditional methods for detection of bacteria in drinking water e.g. Heterotrophic Plate Counts (HPC) or Most Probable Number (MNP) take 48-72 hours to give the result. New rapid methods for detection of bacteria are needed to protect the consumers against contaminations. Two rapid methods...

Comparative study on 4 quantitative detection methods of apoptosis induced by radiation

International Nuclear Information System (INIS)

Yang Yepeng; Chen Guanying; Zhou Mei; Shen Qinjian; Shen Lei; Zhu Yingbao

2004-01-01

Objective: To reveal the capability of 4 apoptosis-detecting methods to discriminate between apoptosis and necrosis and show their respective advantages and shortcomings through comparison of detected results and analysis of detection mechanism. Methods: Four methods, PI staining-flow cytometric detection (P-F method), TUNEL labeling-flow cytometric detection (T-F method), annexing V-FITC/PI vital staining-flow cytometric detection (A-F method) and Hoechst/PI vital staining-fluorescence microscopic observation (H-O method), were used to determine apoptosis and necrosis in human breast cancer MCF-7 cell line induced by γ-rays. Hydroxycamptothecine and sodium azide were used to induce positive controls of apoptosis and necrosis respectively. Results: All 4 methods showed good time-dependent and dose dependent respondence to apoptosis induced by γ-rays and hydroxycamptothecine. Apoptotic cell ratios and curve slopes obtained from P-F method were minimum and, on the contrary, those from T-F method were maximum among these 4 methods. With A-F method and H-O method, two sets of data, apoptosis and necrosis, could be gained respectively and the data gained from these two methods were close to equal. A-F method and H-O method could distinguish necrosis induced by sodium azide from apoptosis while P-F method and T-F method presented false increase of apoptosis. Conclusions: P-F method and T-F method can not discriminate between apoptosis and necrosis. P-F method is less sensitive but more simple, convenient and economical than T-F method. A-F method and H-O method can distinguish necrosis from apoptosis. A-F method is more costly but more quick and reliable than H-O method. H-O method is economical, practical and morphological changes of cells and nucleus can be observed simultaneously with it. (authors)

Object-Based Change Detection Using High-Resolution Remotely Sensed Data and GIS

Science.gov (United States)

Sofina, N.; Ehlers, M.

2012-08-01

High resolution remotely sensed images provide current, detailed, and accurate information for large areas of the earth surface which can be used for change detection analyses. Conventional methods of image processing permit detection of changes by comparing remotely sensed multitemporal images. However, for performing a successful analysis it is desirable to take images from the same sensor which should be acquired at the same time of season, at the same time of a day, and - for electro-optical sensors - in cloudless conditions. Thus, a change detection analysis could be problematic especially for sudden catastrophic events. A promising alternative is the use of vector-based maps containing information about the original urban layout which can be related to a single image obtained after the catastrophe. The paper describes a methodology for an object-based search of destroyed buildings as a consequence of a natural or man-made catastrophe (e.g., earthquakes, flooding, civil war). The analysis is based on remotely sensed and vector GIS data. It includes three main steps: (i) generation of features describing the state of buildings; (ii) classification of building conditions; and (iii) data import into a GIS. One of the proposed features is a newly developed 'Detected Part of Contour' (DPC). Additionally, several features based on the analysis of textural information corresponding to the investigated vector objects are calculated. The method is applied to remotely sensed images of areas that have been subjected to an earthquake. The results show the high reliability of the DPC feature as an indicator for change.

Clinical value of different detection methods in blunt ocular trauma

Directory of Open Access Journals (Sweden)

Yang Li

2018-02-01

Full Text Available Blunt ocular can cause persistent change of eye structure and function, the method of detection which is closely related to eye injury including B-can ultrasonography, UBM, OCT, FFA, scanning laser polarimetry, fundus autofluorescence, each examination with particular emphasis. This paper aims to review the advantages and disadvantages of different inspection methods in order to provide reference for clinical diagnosis and treatment of blunt ocular trauma.

Sensor for detecting changes in magnetic fields

Science.gov (United States)

Praeg, Walter F.

1981-01-01

A sensor for detecting changes in the magnetic field of the equilibrium-field coil of a Tokamak plasma device comprises a pair of bifilar wires disposed circumferentially, one inside and one outside the equilibrium-field coil. Each is shorted at one end. The difference between the voltages detected at the other ends of the bifilar wires provides a measure of changing flux in the equilibrium-field coil. This difference can be used to detect faults in the coil in time to take action to protect the coil.

Detection of oral HPV infection - Comparison of two different specimen collection methods and two HPV detection methods.

Science.gov (United States)

de Souza, Marjorie M A; Hartel, Gunter; Whiteman, David C; Antonsson, Annika

2018-04-01

Very little is known about the natural history of oral HPV infection. Several different methods exist to collect oral specimens and detect HPV, but their respective performance characteristics are unknown. We compared two different methods for oral specimen collection (oral saline rinse and commercial saliva kit) from 96 individuals and then analyzed the samples for HPV by two different PCR detection methods (single GP5+/6+ PCR and nested MY09/11 and GP5+/6+ PCR). For the oral rinse samples, the oral HPV prevalence was 10.4% (GP+ PCR; 10% repeatability) vs 11.5% (nested PCR method; 100% repeatability). For the commercial saliva kit samples, the prevalences were 3.1% vs 16.7% with the GP+ PCR vs the nested PCR method (repeatability 100% for both detection methods). Overall the agreement was fair or poor between samples and methods (kappa 0.06-0.36). Standardizing methods of oral sample collection and HPV detection would ensure comparability between future oral HPV studies. Copyright © 2017 Elsevier Inc. All rights reserved.

Real-time biscuit tile image segmentation method based on edge detection.

Science.gov (United States)

Matić, Tomislav; Aleksi, Ivan; Hocenski, Željko; Kraus, Dieter

2018-05-01

In this paper we propose a novel real-time Biscuit Tile Segmentation (BTS) method for images from ceramic tile production line. BTS method is based on signal change detection and contour tracing with a main goal of separating tile pixels from background in images captured on the production line. Usually, human operators are visually inspecting and classifying produced ceramic tiles. Computer vision and image processing techniques can automate visual inspection process if they fulfill real-time requirements. Important step in this process is a real-time tile pixels segmentation. BTS method is implemented for parallel execution on a GPU device to satisfy the real-time constraints of tile production line. BTS method outperforms 2D threshold-based methods, 1D edge detection methods and contour-based methods. Proposed BTS method is in use in the biscuit tile production line. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Feasibility analysis of EDXRF method to detect heavy metal pollution in ecological environment

Science.gov (United States)

Hao, Zhixu; Qin, Xulei

2018-02-01

The change of heavy metal content in water environment, soil and plant can reflect the change of heavy metal pollution in ecological environment, and it is important to monitor the trend of heavy metal pollution in eco-environment by using water environment, soil and heavy metal content in plant. However, the content of heavy metals in nature is very low, the background elements of water environment, soil and plant samples are complex, and there are many interfering factors in the EDXRF system that will affect the spectral analysis results and reduce the detection accuracy. Through the contrastive analysis of several heavy metal elements detection methods, it is concluded that the EDXRF method is superior to other chemical methods in testing accuracy and method feasibility when the heavy metal pollution in soil is tested in ecological environment.

Effects of rust in the crack face on crack detection based on Sonic-IR method

International Nuclear Information System (INIS)

Harai, Y.; Izumi, Y.; Tanabe, H.; Takamatsu, T.; Sakagami, T.

2015-01-01

Sonic-IR, which is based on the thermographic detection of the temperature rise due to frictional heating at the defect faces under ultrasonic excitation, has an advantage in the detection of closed and small defects. However, this method has a lot of nuclear factors relating to heat generation. In this study, effects of rust in the crack faces on the crack detection based on the sonic-IR method is experimentally investigated by using crack specimens. The heat generation by ultrasonic excitation was observed regularly during rust accelerated test using original device. The distribution of temperature change around the crack was changed with the progress of rust. This change in heat generation, it believed to be due to change in the contact state of the crack surface due to rust. As a result, it was found that heat generation by ultrasonic excitation is affected by rust in the crack faces. And it was also found that crack detection can be conducted by sonic-IR even if rust was generated in the crack faces. (author)

Multisensor Fusion for Change Detection

Science.gov (United States)

Schenk, T.; Csatho, B.

2005-12-01

Combining sensors that record different properties of a 3-D scene leads to complementary and redundant information. If fused properly, a more robust and complete scene description becomes available. Moreover, fusion facilitates automatic procedures for object reconstruction and modeling. For example, aerial imaging sensors, hyperspectral scanning systems, and airborne laser scanning systems generate complementary data. We describe how data from these sensors can be fused for such diverse applications as mapping surface erosion and landslides, reconstructing urban scenes, monitoring urban land use and urban sprawl, and deriving velocities and surface changes of glaciers and ice sheets. An absolute prerequisite for successful fusion is a rigorous co-registration of the sensors involved. We establish a common 3-D reference frame by using sensor invariant features. Such features are caused by the same object space phenomena and are extracted in multiple steps from the individual sensors. After extracting, segmenting and grouping the features into more abstract entities, we discuss ways on how to automatically establish correspondences. This is followed by a brief description of rigorous mathematical models suitable to deal with linear and area features. In contrast to traditional, point-based registration methods, lineal and areal features lend themselves to a more robust and more accurate registration. More important, the chances to automate the registration process increases significantly. The result of the co-registration of the sensors is a unique transformation between the individual sensors and the object space. This makes spatial reasoning of extracted information more versatile; reasoning can be performed in sensor space or in 3-D space where domain knowledge about features and objects constrains reasoning processes, reduces the search space, and helps to make the problem well-posed. We demonstrate the feasibility of the proposed multisensor fusion approach

Drillstring Washout Diagnosis Using Friction Estimation and Statistical Change Detection

DEFF Research Database (Denmark)

Willersrud, Anders; Blanke, Mogens; Imsland, Lars

2015-01-01

In oil and gas drilling, corrosion or tensile stress can give small holes in the drillstring, which can cause leakage and prevent sufficient flow of drilling fluid. If such washout remains undetected and develops, the consequence can be a complete twist-off of the drillstring. Aiming at early...... washout diagnosis, this paper employs an adaptive observer to estimate friction parameters in the nonlinear pro- cess. Non-Gaussian noise is a nuisance in the parameter estimates, and dedicated generalized likelihood tests are developed to make efficient washout detection with the multivariate t...... -distribution encountered in data. Change detection methods are developed using logged sensor data from a horizontal 1400 m managed pressure drilling test rig. Detection scheme design is conducted using probabilities for false alarm and detection to determine thresholds in hypothesis tests. A multivariate...

Sustained change blindness to incremental scene rotation: a dissociation between explicit change detection and visual memory.

Science.gov (United States)

Hollingworth, Andrew; Henderson, John M

2004-07-01

In a change detection paradigm, the global orientation of a natural scene was incrementally changed in 1 degree intervals. In Experiments 1 and 2, participants demonstrated sustained change blindness to incremental rotation, often coming to consider a significantly different scene viewpoint as an unchanged continuation of the original view. Experiment 3 showed that participants who failed to detect the incremental rotation nevertheless reliably detected a single-step rotation back to the initial view. Together, these results demonstrate an important dissociation between explicit change detection and visual memory. Following a change, visual memory is updated to reflect the changed state of the environment, even if the change was not detected.

Early pack-off diagnosis in drilling using an adaptive observer and statistical change detection

DEFF Research Database (Denmark)

Willersrud, Anders; Imsland, Lars; Blanke, Mogens

2015-01-01

in the well. A model-based adaptive observer is used to estimate these friction parameters as well as flow rates. Detecting changes to these estimates can then be used for pack-off diagnosis, which due to measurement noise is done using statistical change detection. Isolation of incident type and location...... is done using a multivariate generalized likelihood ratio test, determining the change direction of the estimated mean values. The method is tested on simulated data from the commercial high-fidelity multi-phase simulator OLGA, where three different pack-offs at different locations and with different...

Continuous Change Detection and Classification Using Hidden Markov Model: A Case Study for Monitoring Urban Encroachment onto Farmland in Beijing

Directory of Open Access Journals (Sweden)

Yuan Yuan

2015-11-01

Full Text Available In this paper, we propose a novel method to continuously monitor land cover change using satellite image time series, which can extract comprehensive change information including change time, location, and “from-to” information. This method is based on a hidden Markov model (HMM trained for each land cover class. Assuming a pixel’s initial class has been obtained, likelihoods of the corresponding model are calculated on incoming time series extracted with a temporal sliding window. By observing the likelihood change over the windows, land cover change can be precisely detected from the dramatic drop of likelihood. The established HMMs are then used for identifying the land cover class after the change. As a case study, the proposed method is applied to monitoring urban encroachment onto farmland in Beijing using 10-year MODIS time series from 2001 to 2010. The performance is evaluated on a validation set for different model structures and thresholds. Compared with other change detection methods, the proposed method shows superior change detection accuracy. In addition, it is also more computationally efficient.

Some methods for the detection of fissionable matter; Quelques methodes de detection des corps fissiles

Energy Technology Data Exchange (ETDEWEB)

Guery, M [Commissariat a l' Energie Atomique, Saclay (France). Centre d' Etudes Nucleaires

1967-03-01

A number of equipments or processes allowing to detect uranium or plutonium in industrial plants, and in particular to measure solution concentrations, are studied here. Each method has its own field of applications and has its own performances, which we have tried to define by calculations and by experiments. The following topics have been treated: {gamma} absorptiometer with an Am source, detection test by neutron multiplication, apparatus for the measurement of the {alpha} activity of a solution, fissionable matter detection by {gamma} emission, fissionable matter detection by neutron emission. (author) [French] On examine ici plusieurs appareils ou procedes qui permettent de detecter l'uranium ou le plutonium dans les installations industrielles, et en particulier de mesurer les concentrations de solutions. Chacune des methodes a son domaine d'application et ses performances, qu'on a tente de definir par le calcul et par des experiences. Les sujets traites sont les suivants: absorptiometre {gamma} a source d'americium, essais de detection par multiplication neutronique, appareil de mesure de l'activite {alpha} d'une solution, detection des matieres fissiles par leur emission {gamma}, detection des matieres fissiles par leur emission neutronique. (auteur)

An Entropy-Based Network Anomaly Detection Method

Directory of Open Access Journals (Sweden)

Przemysław Bereziński

2015-04-01

Full Text Available Data mining is an interdisciplinary subfield of computer science involving methods at the intersection of artificial intelligence, machine learning and statistics. One of the data mining tasks is anomaly detection which is the analysis of large quantities of data to identify items, events or observations which do not conform to an expected pattern. Anomaly detection is applicable in a variety of domains, e.g., fraud detection, fault detection, system health monitoring but this article focuses on application of anomaly detection in the field of network intrusion detection.The main goal of the article is to prove that an entropy-based approach is suitable to detect modern botnet-like malware based on anomalous patterns in network. This aim is achieved by realization of the following points: (i preparation of a concept of original entropy-based network anomaly detection method, (ii implementation of the method, (iii preparation of original dataset, (iv evaluation of the method.

Anomalies in the detection of change: When changes in sample size are mistaken for changes in proportions.

Science.gov (United States)

Fiedler, Klaus; Kareev, Yaakov; Avrahami, Judith; Beier, Susanne; Kutzner, Florian; Hütter, Mandy

2016-01-01

Detecting changes, in performance, sales, markets, risks, social relations, or public opinions, constitutes an important adaptive function. In a sequential paradigm devised to investigate detection of change, every trial provides a sample of binary outcomes (e.g., correct vs. incorrect student responses). Participants have to decide whether the proportion of a focal feature (e.g., correct responses) in the population from which the sample is drawn has decreased, remained constant, or increased. Strong and persistent anomalies in change detection arise when changes in proportional quantities vary orthogonally to changes in absolute sample size. Proportional increases are readily detected and nonchanges are erroneously perceived as increases when absolute sample size increases. Conversely, decreasing sample size facilitates the correct detection of proportional decreases and the erroneous perception of nonchanges as decreases. These anomalies are however confined to experienced samples of elementary raw events from which proportions have to be inferred inductively. They disappear when sample proportions are described as percentages in a normalized probability format. To explain these challenging findings, it is essential to understand the inductive-learning constraints imposed on decisions from experience.

Acoustic change detection algorithm using an FM radio

Science.gov (United States)

Goldman, Geoffrey H.; Wolfe, Owen

2012-06-01

The U.S. Army is interested in developing low-cost, low-power, non-line-of-sight sensors for monitoring human activity. One modality that is often overlooked is active acoustics using sources of opportunity such as speech or music. Active acoustics can be used to detect human activity by generating acoustic images of an area at different times, then testing for changes among the imagery. A change detection algorithm was developed to detect physical changes in a building, such as a door changing positions or a large box being moved using acoustics sources of opportunity. The algorithm is based on cross correlating the acoustic signal measured from two microphones. The performance of the algorithm was shown using data generated with a hand-held FM radio as a sound source and two microphones. The algorithm could detect a door being opened in a hallway.

New method of silicon photovoltaic panel fault detection using impedance spectroscopy

DEFF Research Database (Denmark)

Symonowicz, Joanna Karolina; Riedel, Nicholas; Thorsteinsson, Sune

2017-01-01

The aim of our project is to develop a new method for photovoltaic (PV) panel fault detection based on analysing its impedance spectra (IS). Although this technique was successful in assessing the state of degradation of fuel cells and batteries [1, 2], it has never been applied to PV cells...... on a wide scale. In this paper, we show that, unlike current-voltage (I-V) tests, the IS method is capable of early detection of changes in PV panel parameters due to microcracks and potential-induced degradation (PID). Although our measurements are only successful under dark conditions, the results...... are similar for both laboratory environment and for outdoor tests in various weather conditions. A fully developed IS technique, accounting for all kinds of most common PV panel degradation types, would surpass the existing PV fault detection methods then it comes to cost and accuracy [3,4]....

Samsung Salmonella Detection Kit. AOAC Performance Tested Method(SM) 021203.

Science.gov (United States)

Li, Jun; Cheung, Win Den; Opdyke, Jason; Harvey, John; Chong, Songchun; Moon, Cheol Gon

2012-01-01

Salmonella, one of the most common causes of foodborne illness, is a significant public health concern worldwide. There is a need in the food industry for methods that are simple, rapid, and sensitive for the detection of foodborne pathogens. In this study, the Samsung Salmonella Detection Kit, a real-time PCR assay for the detection of Salmonella, was evaluated according to the current AOAC guidelines. The validation consisted of lot-to-lot consistency, stability, robustness, and inclusivity/exclusivity studies, as well as a method comparison of 10 different food matrixes. In the validation, the Samsung Salmonella Detection Kit was used in conjunction with the Applied Biosystems StepOnePlus PCR system and the Samsung Food Testing Software for the detection of Salmonella species. The performance of the assays was compared to the U.S. Department of Agriculture/Food Safety and Inspection Service-Microbiology Laboratory Guidebook (USDA/FSIS-MLG) 4.05: Isolation and Identification of Salmonella from Meat, Poultry, Pasteurized Egg, and Catfish and the and U.S. Food and Drug Administration/Bacteriological Analytical Manual (FDA/BAM) Chapter 5 Salmonella reference methods. The validation was conducted using an unpaired study design for detection of Salmonella spp. in raw ground beef, raw pork, raw ground pork, raw chicken wings, raw salmon, alfalfa sprouts, pasteurized orange juice, peanut butter, pasteurized whole milk, and shell eggs. The Samsung Salmonella Detection Kit demonstrated lot-to-lot consistency among three independent lots as well as ruggedness with minor modifications to changes in enrichment incubation time, enrichment incubation temperature, and DNA sample volume for PCR reaction. Stability was observed for 13 months at -20 degrees C and 3 months at 5 degrees C. For the inclusivity/exclusivity study, the Samsung Salmonella Detection Kit correctly identified 147 Salmonella species isolates out of 147 isolates tested from each of three different enrichment

FAST AND ROBUST SEGMENTATION AND CLASSIFICATION FOR CHANGE DETECTION IN URBAN POINT CLOUDS

Directory of Open Access Journals (Sweden)

X. Roynard

2016-06-01

Full Text Available Change detection is an important issue in city monitoring to analyse street furniture, road works, car parking, etc. For example, parking surveys are needed but are currently a laborious task involving sending operators in the streets to identify the changes in car locations. In this paper, we propose a method that performs a fast and robust segmentation and classification of urban point clouds, that can be used for change detection. We apply this method to detect the cars, as a particular object class, in order to perform parking surveys automatically. A recently proposed method already addresses the need for fast segmentation and classification of urban point clouds, using elevation images. The interest to work on images is that processing is much faster, proven and robust. However there may be a loss of information in complex 3D cases: for example when objects are one above the other, typically a car under a tree or a pedestrian under a balcony. In this paper we propose a method that retain the three-dimensional information while preserving fast computation times and improving segmentation and classification accuracy. It is based on fast region-growing using an octree, for the segmentation, and specific descriptors with Random-Forest for the classification. Experiments have been performed on large urban point clouds acquired by Mobile Laser Scanning. They show that the method is as fast as the state of the art, and that it gives more robust results in the complex 3D cases.

Attempts to elaborate detection methods for some irradiated food and dry ingredients

International Nuclear Information System (INIS)

Barabassy, S.; Sharif, M.; Farkas, J.; Felfoeldi, J.; Koncz, A.; Formanek, Z.; Kaffka, K.

1996-01-01

In many countries ionising radiation is increasingly used for microbial decontamination of dry food ingredients, such as spices and herbs, because this treatment causes minimal chemical alteration and few, if any, detectable changes in the flavour of spices. However, many health authorities and consumer organisations demand unequivocal tests for identification of irradiated foods. Due to the diversity and delicate chemical composition of spices, there is very little chance of developing routine chemical methods to detect a specific radiolytic product in dry spices and herbs, physical methods appear to have greater potential. Significant reduction of the gel-forming capability after irradiation could be observed in several spices. Degradation caused by ionising radiation in the volatile oils, lipids, carotenoids and starch is indicated in the near infrared (NIR) wavelength region by changes in the reflectance spectrum. The low doses of ionising radiation (for inhibition of the sprouting of tubers and bulbs) as a consequence of some histological characteristics, induce changes in the electrical impedance and derived quantities. (author)

Using Page’s cumulative sum test on MODIS time series to detect land-cover changes

CSIR Research Space (South Africa)

Grobler, TL

2012-01-01

Full Text Available by natural vegetation using 500-m Moderate Resolution Imaging Spectroradiometer time-series satellite data. The method is a sequential per-pixel change alarm algorithm that can take into account positive detection delay, probability of detection, and false...

Damage detection and locating using tone burst and continuous excitation modulation method

Science.gov (United States)

Li, Zheng; Wang, Zhi; Xiao, Li; Qu, Wenzhong

2014-03-01

Among structural health monitoring techniques, nonlinear ultrasonic spectroscopy methods are found to be effective diagnostic approach to detecting nonlinear damage such as fatigue crack, due to their sensitivity to incipient structural changes. In this paper, a nonlinear ultrasonic modulation method was developed to detect and locate a fatigue crack on an aluminum plate. The method is different with nonlinear wave modulation method which recognizes the modulation of low-frequency vibration and high-frequency ultrasonic wave; it recognizes the modulation of tone burst and high-frequency ultrasonic wave. In the experiment, a Hanning window modulated sinusoidal tone burst and a continuous sinusoidal excitation were simultaneously imposed on the PZT array which was bonded on the surface of an aluminum plate. The modulations of tone burst and continuous sinusoidal excitation was observed in different actuator-sensor paths, indicating the presence and location of fatigue crack. The results of experiments show that the proposed method is capable of detecting and locating the fatigue crack successfully.

Fast Change Point Detection for Electricity Market Analysis

Energy Technology Data Exchange (ETDEWEB)

Berkeley, UC; Gu, William; Choi, Jaesik; Gu, Ming; Simon, Horst; Wu, Kesheng

2013-08-25

Electricity is a vital part of our daily life; therefore it is important to avoid irregularities such as the California Electricity Crisis of 2000 and 2001. In this work, we seek to predict anomalies using advanced machine learning algorithms. These algorithms are effective, but computationally expensive, especially if we plan to apply them on hourly electricity market data covering a number of years. To address this challenge, we significantly accelerate the computation of the Gaussian Process (GP) for time series data. In the context of a Change Point Detection (CPD) algorithm, we reduce its computational complexity from O($n^{5}$) to O($n^{2}$). Our efficient algorithm makes it possible to compute the Change Points using the hourly price data from the California Electricity Crisis. By comparing the detected Change Points with known events, we show that the Change Point Detection algorithm is indeed effective in detecting signals preceding major events.

Development of evaluation method of long-term confinement performance for canister. Part 1. Fundamental study of analyses method for helium leak detection

International Nuclear Information System (INIS)

Takeda, Hirofumi; Toriu, Daisuke; Ushijima, Satoru

2014-01-01

The storage management of spent nuclear fuel for ageing degradation is becoming a global issue, so we researched the present status and measures of the management in each country. In particular, for the concrete cask storage, a leak detecting method that detects the leak from the change in canister surface temperature has been proposed. We performed thermal hydraulics analysis to clarify the phenomenon and to work toward practical use of the detecting method. For analyzing the leak phenomenon with high accuracy, it is necessary to stably solve the low-Mach number flow problem considering compressibility of gas. Therefore, we originally modified the conventional compressible flow solution method and proposed a new method which is applicable to thermo-hydraulics phenomenon and satisfies the mass conservation law with high accuracy. For the cavity natural convection analysis, the mass conservation in a calculating area was satisfied with high accuracy. As for the analysis of leak from the cavity, a helium leak phenomenon could be calculated stably by using the proposed method. The pressure in the cavity and the change of the mass could be also analyzed validly. As for the temperature distribution in the cavity, it was confirmed that the temperature changes before and after the leak. (author)

GMDD: a database of GMO detection methods.

Science.gov (United States)

Dong, Wei; Yang, Litao; Shen, Kailin; Kim, Banghyun; Kleter, Gijs A; Marvin, Hans J P; Guo, Rong; Liang, Wanqi; Zhang, Dabing

2008-06-04

Since more than one hundred events of genetically modified organisms (GMOs) have been developed and approved for commercialization in global area, the GMO analysis methods are essential for the enforcement of GMO labelling regulations. Protein and nucleic acid-based detection techniques have been developed and utilized for GMOs identification and quantification. However, the information for harmonization and standardization of GMO analysis methods at global level is needed. GMO Detection method Database (GMDD) has collected almost all the previous developed and reported GMOs detection methods, which have been grouped by different strategies (screen-, gene-, construct-, and event-specific), and also provide a user-friendly search service of the detection methods by GMO event name, exogenous gene, or protein information, etc. In this database, users can obtain the sequences of exogenous integration, which will facilitate PCR primers and probes design. Also the information on endogenous genes, certified reference materials, reference molecules, and the validation status of developed methods is included in this database. Furthermore, registered users can also submit new detection methods and sequences to this database, and the newly submitted information will be released soon after being checked. GMDD contains comprehensive information of GMO detection methods. The database will make the GMOs analysis much easier.

INCREMENTAL PRINCIPAL COMPONENT ANALYSIS BASED OUTLIER DETECTION METHODS FOR SPATIOTEMPORAL DATA STREAMS

Directory of Open Access Journals (Sweden)

A. Bhushan

2015-07-01

Full Text Available In this paper, we address outliers in spatiotemporal data streams obtained from sensors placed across geographically distributed locations. Outliers may appear in such sensor data due to various reasons such as instrumental error and environmental change. Real-time detection of these outliers is essential to prevent propagation of errors in subsequent analyses and results. Incremental Principal Component Analysis (IPCA is one possible approach for detecting outliers in such type of spatiotemporal data streams. IPCA has been widely used in many real-time applications such as credit card fraud detection, pattern recognition, and image analysis. However, the suitability of applying IPCA for outlier detection in spatiotemporal data streams is unknown and needs to be investigated. To fill this research gap, this paper contributes by presenting two new IPCA-based outlier detection methods and performing a comparative analysis with the existing IPCA-based outlier detection methods to assess their suitability for spatiotemporal sensor data streams.

Transfer Entropy Estimation and Directional Coupling Change Detection in Biomedical Time Series

Directory of Open Access Journals (Sweden)

Lee Joon

2012-04-01

Full Text Available Abstract Background The detection of change in magnitude of directional coupling between two non-linear time series is a common subject of interest in the biomedical domain, including studies involving the respiratory chemoreflex system. Although transfer entropy is a useful tool in this avenue, no study to date has investigated how different transfer entropy estimation methods perform in typical biomedical applications featuring small sample size and presence of outliers. Methods With respect to detection of increased coupling strength, we compared three transfer entropy estimation techniques using both simulated time series and respiratory recordings from lambs. The following estimation methods were analyzed: fixed-binning with ranking, kernel density estimation (KDE, and the Darbellay-Vajda (D-V adaptive partitioning algorithm extended to three dimensions. In the simulated experiment, sample size was varied from 50 to 200, while coupling strength was increased. In order to introduce outliers, the heavy-tailed Laplace distribution was utilized. In the lamb experiment, the objective was to detect increased respiratory-related chemosensitivity to O2 and CO2 induced by a drug, domperidone. Specifically, the separate influence of end-tidal PO2 and PCO2 on minute ventilation (V˙E before and after administration of domperidone was analyzed. Results In the simulation, KDE detected increased coupling strength at the lowest SNR among the three methods. In the lamb experiment, D-V partitioning resulted in the statistically strongest increase in transfer entropy post-domperidone for PO2→V˙E. In addition, D-V partitioning was the only method that could detect an increase in transfer entropy for PCO2→V˙E, in agreement with experimental findings. Conclusions Transfer entropy is capable of detecting directional coupling changes in non-linear biomedical time series analysis featuring a small number of observations and presence of outliers. The results

Unsupervised Speaker Change Detection for Broadcast News Segmentation

DEFF Research Database (Denmark)

Jørgensen, Kasper Winther; Mølgaard, Lasse Lohilahti; Hansen, Lars Kai

2006-01-01

This paper presents a speaker change detection system for news broadcast segmentation based on a vector quantization (VQ) approach. The system does not make any assumption about the number of speakers or speaker identity. The system uses mel frequency cepstral coefficients and change detection...

Detection of food irradiation with luminescence methods

International Nuclear Information System (INIS)

Anderle, H.

1997-06-01

Food irradiation is applied as method for the preservation of foods, the prevention of food spoilage and the inhibition of food-borne pathogens. Doses exceeding 10 kGy (10 kJ/kg) are not recommended by the WHO. The different legislation requires methods for the detection and the closimetry of irradiated foods. Among the physical methods based on the radiation-induced changes in inorganic, nonhygroscopic crystalline solids are thermoluminescence (TL), photostimulated luminescence (PSL) and lyoluminescence (LL) measurement. The luminescence methods were tested on natural minerals. Pure quartz, feldspars, calcite, aragonite and dolomite of known origin were irradiated, read out and analyzed to determine the influence of luminescence-activators and deactivators. Carbonate minerals show an orange-red TL easily detectable by blue-sensitive photomultiplier tubes. TIL-inactive carbonate samples may be identified by a lyoluminescence method using the reaction of trapped irradiation-generated charge carriers with the solvent during crystal-lattice breakup. The fine-ground mineral is dissolved in an alkaline complexing agent/chemiluminescence sensitizer/chemiluminescence catalyst (EDTA/luminol/hemin) reagent mixture. The TL and PSL of quartz is too weak to contribute a significant part for the corresponding signals in polymineral dust. Alkali and soda feldspar show intense TL and PSL. The temperature maxima in the TL glow curves allow a clear distinction. PSL does not give this additional information, it suffers from bleaching by ambient light and requires light-protection. Grain disinfestated with low irradiation doses (500 Gy) may not identified by both TL and PSL measurement. The natural TL of feldspar particles may be overlap with the irradiation-induced TL of other minerals. As a routine method, irradiated spices are identified with TL measurement. The dust particles have to be enriched by heavy-liquid flotation and centrifugation. The PSL method allows a clear

A new fault detection method for computer networks

International Nuclear Information System (INIS)

Lu, Lu; Xu, Zhengguo; Wang, Wenhai; Sun, Youxian

2013-01-01

Over the past few years, fault detection for computer networks has attracted extensive attentions for its importance in network management. Most existing fault detection methods are based on active probing techniques which can detect the occurrence of faults fast and precisely. But these methods suffer from the limitation of traffic overhead, especially in large scale networks. To relieve traffic overhead induced by active probing based methods, a new fault detection method, whose key is to divide the detection process into multiple stages, is proposed in this paper. During each stage, only a small region of the network is detected by using a small set of probes. Meanwhile, it also ensures that the entire network can be covered after multiple detection stages. This method can guarantee that the traffic used by probes during each detection stage is small sufficiently so that the network can operate without severe disturbance from probes. Several simulation results verify the effectiveness of the proposed method

[Detection of protein-protein interactions by FRET and BRET methods].

Science.gov (United States)

Matoulková, E; Vojtěšek, B

2014-01-01

Nowadays, in vivo protein-protein interaction studies have become preferable detecting meth-ods that enable to show or specify (already known) protein interactions and discover their inhibitors. They also facilitate detection of protein conformational changes and discovery or specification of signaling pathways in living cells. One group of in vivo methods enabling these findings is based on fluorescent resonance energy transfer (FRET) and its bio-luminescent modification (BRET). They are based on visualization of protein-protein interactions via light or enzymatic excitation of fluorescent or bio-luminescent proteins. These methods allow not only protein localization within the cell or its organelles (or small animals) but they also allow us to quantify fluorescent signals and to discover weak or strong interaction partners. In this review, we explain the principles of FRET and BRET, their applications in the characterization of protein-protein interactions and we describe several findings using these two methods that clarify molecular and cellular mechanisms and signals related to cancer biology.

Cost-Effectiveness Analysis of Three Leprosy Case Detection Methods in Northern Nigeria

Science.gov (United States)

Ezenduka, Charles; Post, Erik; John, Steven; Suraj, Abdulkarim; Namadi, Abdulahi; Onwujekwe, Obinna

2012-01-01

Background Despite several leprosy control measures in Nigeria, child proportion and disability grade 2 cases remain high while new cases have not significantly reduced, suggesting continuous spread of the disease. Hence, there is the need to review detection methods to enhance identification of early cases for effective control and prevention of permanent disability. This study evaluated the cost-effectiveness of three leprosy case detection methods in Northern Nigeria to identify the most cost-effective approach for detection of leprosy. Methods A cross-sectional study was carried out to evaluate the additional benefits of using several case detection methods in addition to routine practice in two north-eastern states of Nigeria. Primary and secondary data were collected from routine practice records and the Nigerian Tuberculosis and Leprosy Control Programme of 2009. The methods evaluated were Rapid Village Survey (RVS), Household Contact Examination (HCE) and Traditional Healers incentive method (TH). Effectiveness was measured as number of new leprosy cases detected and cost-effectiveness was expressed as cost per case detected. Costs were measured from both providers' and patients' perspectives. Additional costs and effects of each method were estimated by comparing each method against routine practise and expressed as incremental cost-effectiveness ratio (ICER). All costs were converted to the U.S. dollar at the 2010 exchange rate. Univariate sensitivity analysis was used to evaluate uncertainties around the ICER. Results The ICER for HCE was $142 per additional case detected at all contact levels and it was the most cost-effective method. At ICER of $194 per additional case detected, THs method detected more cases at a lower cost than the RVS, which was not cost-effective at $313 per additional case detected. Sensitivity analysis showed that varying the proportion of shared costs and subsistent wage for valuing unpaid time did not significantly change the

Detection of bursts in neuronal spike trains by the mean inter-spike interval method

Institute of Scientific and Technical Information of China (English)

Lin Chen; Yong Deng; Weihua Luo; Zhen Wang; Shaoqun Zeng

2009-01-01

Bursts are electrical spikes firing with a high frequency, which are the most important property in synaptic plasticity and information processing in the central nervous system. However, bursts are difficult to identify because bursting activities or patterns vary with phys-iological conditions or external stimuli. In this paper, a simple method automatically to detect bursts in spike trains is described. This method auto-adaptively sets a parameter (mean inter-spike interval) according to intrinsic properties of the detected burst spike trains, without any arbitrary choices or any operator judgrnent. When the mean value of several successive inter-spike intervals is not larger than the parameter, a burst is identified. By this method, bursts can be automatically extracted from different bursting patterns of cultured neurons on multi-electrode arrays, as accurately as by visual inspection. Furthermore, significant changes of burst variables caused by electrical stimulus have been found in spontaneous activity of neuronal network. These suggest that the mean inter-spike interval method is robust for detecting changes in burst patterns and characteristics induced by environmental alterations.

Linear and kernel methods for multivariate change detection

DEFF Research Database (Denmark)

Canty, Morton J.; Nielsen, Allan Aasbjerg

2012-01-01

), as well as maximum autocorrelation factor (MAF) and minimum noise fraction (MNF) analyses of IR-MAD images, both linear and kernel-based (nonlinear), may further enhance change signals relative to no-change background. IDL (Interactive Data Language) implementations of IR-MAD, automatic radiometric...... normalization, and kernel PCA/MAF/MNF transformations are presented that function as transparent and fully integrated extensions of the ENVI remote sensing image analysis environment. The train/test approach to kernel PCA is evaluated against a Hebbian learning procedure. Matlab code is also available...... that allows fast data exploration and experimentation with smaller datasets. New, multiresolution versions of IR-MAD that accelerate convergence and that further reduce no-change background noise are introduced. Computationally expensive matrix diagonalization and kernel image projections are programmed...

Invasive species change detection using artificial neural networks and CASI hyperspectral imagery

Science.gov (United States)

For monitoring and controlling the extent and intensity of an invasive species, a direct multi-date image classification method was applied in invasive species (saltcedar) change detection in the study area of Lovelock, Nevada. With multi-date Compact Airborne Spectrographic Imager (CASI) hyperspec...

Comparison of floods non-stationarity detection methods: an Austrian case study

Science.gov (United States)

Salinas, Jose Luis; Viglione, Alberto; Blöschl, Günter

2016-04-01

Non-stationarities in flood regimes have a huge impact in any mid and long term flood management strategy. In particular the estimation of design floods is very sensitive to any kind of flood non-stationarity, as they should be linked to a return period, concept that can be ill defined in a non-stationary context. Therefore it is crucial when analyzing existent flood time series to detect and, where possible, attribute flood non-stationarities to changing hydroclimatic and land-use processes. This works presents the preliminary results of applying different non-stationarity detection methods on annual peak discharges time series over more than 400 gauging stations in Austria. The kind of non-stationarities analyzed include trends (linear and non-linear), breakpoints, clustering beyond stochastic randomness, and detection of flood rich/flood poor periods. Austria presents a large variety of landscapes, elevations and climates that allow us to interpret the spatial patterns obtained with the non-stationarity detection methods in terms of the dominant flood generation mechanisms.

Real-time change detection for countering improvised explosive devices

NARCIS (Netherlands)

Wouw, van de D.W.J.M.; Rens, van K.; Lint, van R.H.; Jaspers, Egbert; With, de P.H.N.; Loce, R.P.; Saber, E.

2014-01-01

We explore an automatic real-time change detection system to assist military personnel during transport and surveillance, by detection changes in the environment with respect to a previous operation. Such changes may indicate the presence of Improvised Explosive Devices (IEDs), which can then be

AN APPROACH TO ALLEVIATE THE FALSE ALARM IN BUILDING CHANGE DETECTION FROM URBAN VHR IMAGE

Directory of Open Access Journals (Sweden)

J. Chen

2016-06-01

Full Text Available Building change detection from very-high-resolution (VHR urban remote sensing image frequently encounter the challenge of serious false alarm caused by different illumination or viewing angles in bi-temporal images. An approach to alleviate the false alarm in urban building change detection is proposed in this paper. Firstly, as shadows casted by urban buildings are of distinct spectral and shape feature, it adopts a supervised object-based classification technique to extract them in this paper. Secondly, on the opposite direction of sunlight illumination, a straight line is drawn along the principal orientation of building in every extracted shadow region. Starting from the straight line and moving toward the sunlight direction, a rectangular area is constructed to cover partial shadow and rooftop of each building. Thirdly, an algebra and geometry invariant based method is used to abstract the spatial topological relationship of the potential unchanged buildings from all central points of the rectangular area. Finally, based on an oriented texture curvature descriptor, an index is established to determine the actual false alarm in building change detection result. The experiment results validate that the proposed method can be used as an effective framework to alleviate the false alarm in building change detection from urban VHR image.

Detection and Classification of Changes in Buildings from Airborne Laser Scanning Data

Directory of Open Access Journals (Sweden)

Sudan Xu

2015-12-01

Full Text Available The difficulty associated with the Lidar data change detection method is lack of data, which is mainly caused by occlusion or pulse absorption by the surface material, e.g., water. To address this challenge, we present a new strategy for detecting buildings that are “changed”, “unchanged”, or “unknown”, and quantifying the changes. The designation “unknown” is applied to locations where, due to lack of data in at least one of the epochs, it is not possible to reliably detect changes in the structure. The process starts with classified data sets in which buildings are extracted. Next, a point-to-plane surface difference map is generated by merging and comparing the two data sets. Context rules are applied to the difference map to distinguish between “changed”, “unchanged”, and “unknown”. Rules are defined to solve problems caused by the lack of data. Further, points labelled as “changed” are re-classified into changes to roofs, walls, dormers, cars, constructions above the roof line, and undefined objects. Next, all the classified changes are organized as changed building objects, and the geometric indices are calculated from their 3D minimum bounding boxes. Performance analysis showed that 80%–90% of real changes are found, of which approximately 50% are considered relevant.

Detection of non-coding RNAs on the basis of predicted secondary structure formation free energy change

Directory of Open Access Journals (Sweden)

Uzilov Andrew V

2006-03-01

Full Text Available Abstract Background Non-coding RNAs (ncRNAs have a multitude of roles in the cell, many of which remain to be discovered. However, it is difficult to detect novel ncRNAs in biochemical screens. To advance biological knowledge, computational methods that can accurately detect ncRNAs in sequenced genomes are therefore desirable. The increasing number of genomic sequences provides a rich dataset for computational comparative sequence analysis and detection of novel ncRNAs. Results Here, Dynalign, a program for predicting secondary structures common to two RNA sequences on the basis of minimizing folding free energy change, is utilized as a computational ncRNA detection tool. The Dynalign-computed optimal total free energy change, which scores the structural alignment and the free energy change of folding into a common structure for two RNA sequences, is shown to be an effective measure for distinguishing ncRNA from randomized sequences. To make the classification as a ncRNA, the total free energy change of an input sequence pair can either be compared with the total free energy changes of a set of control sequence pairs, or be used in combination with sequence length and nucleotide frequencies as input to a classification support vector machine. The latter method is much faster, but slightly less sensitive at a given specificity. Additionally, the classification support vector machine method is shown to be sensitive and specific on genomic ncRNA screens of two different Escherichia coli and Salmonella typhi genome alignments, in which many ncRNAs are known. The Dynalign computational experiments are also compared with two other ncRNA detection programs, RNAz and QRNA. Conclusion The Dynalign-based support vector machine method is more sensitive for known ncRNAs in the test genomic screens than RNAz and QRNA. Additionally, both Dynalign-based methods are more sensitive than RNAz and QRNA at low sequence pair identities. Dynalign can be used as a

[Early detection of cervical cancer in Chile: time for change].

Science.gov (United States)

Léniz Martelli, Javiera; Van De Wyngard, Vanessa; Lagos, Marcela; Barriga, María Isabel; Puschel Illanes, Klaus; Ferreccio Readi, Catterina

2014-08-01

Mortality rates for cervical cancer (CC) in Chile are higher than those of developed countries and it has an unequal socioeconomic distribution. The recognition of human papilloma virus (HPV) as the causal agent of cervical cancer in the early 80's changed the prevention paradigms. Current goals are to prevent HPV infection by vaccination before the onset of sexual activity and to detect HPV infection in women older than 30 years. This article reviews CC prevention and early detection methods, discusses relevant evidence to support a change in Chile and presents an innovation proposal. A strategy of primary screening based on HPV detection followed by triage of HPV-positive women by colposcopy in primary care or by cytological or molecular reflex testing is proposed. Due to the existence in Chile of a well-organized nationwide CC prevention program, the replacement of a low-sensitivity screening test such as the Papanicolau test with a highly sensitive one such as HPV detection, could quickly improve the effectiveness of the program. The program also has a network of personnel qualified to conduct naked-eye inspections of the cervix, who could easily be trained to perform triage colposcopy. The incorporation of new prevention strategies could reduce the deaths of Chilean women and correct inequities.

Detecting changes in real-time data: a user's guide to optimal detection.

Science.gov (United States)

Johnson, P; Moriarty, J; Peskir, G

2017-08-13

The real-time detection of changes in a noisily observed signal is an important problem in applied science and engineering. The study of parametric optimal detection theory began in the 1930s, motivated by applications in production and defence. Today this theory, which aims to minimize a given measure of detection delay under accuracy constraints, finds applications in domains including radar, sonar, seismic activity, global positioning, psychological testing, quality control, communications and power systems engineering. This paper reviews developments in optimal detection theory and sequential analysis, including sequential hypothesis testing and change-point detection, in both Bayesian and classical (non-Bayesian) settings. For clarity of exposition, we work in discrete time and provide a brief discussion of the continuous time setting, including recent developments using stochastic calculus. Different measures of detection delay are presented, together with the corresponding optimal solutions. We emphasize the important role of the signal-to-noise ratio and discuss both the underlying assumptions and some typical applications for each formulation.This article is part of the themed issue 'Energy management: flexibility, risk and optimization'. © 2017 The Author(s).

Post-Processing Approach for Refining Raw Land Cover Change Detection of Very High-Resolution Remote Sensing Images

Directory of Open Access Journals (Sweden)

Zhiyong Lv

2018-03-01

Full Text Available In recent decades, land cover change detection (LCCD using very high-spatial resolution (VHR remote sensing images has been a major research topic. However, VHR remote sensing images usually lead to a large amount of noises in spectra, thereby reducing the reliability of the detected results. To solve this problem, this study proposes an object-based expectation maximization (OBEM post-processing approach for enhancing raw LCCD results. OBEM defines a refinement of the labeling in a detected map to enhance its raw detection accuracies. Current mainstream change detection (preprocessing techniques concentrate on proposing a change magnitude measurement or considering image spatial features to obtain a change detection map. The proposed OBEM approach is a new solution to enhance change detection accuracy by refining the raw result. Post-processing approaches can achieve competitive accuracies to the preprocessing methods, but in a direct and succinct manner. The proposed OBEM post-processing method synthetically considers multi-scale segmentation and expectation maximum algorithms to refine the raw change detection result. Then, the influence of the scale of segmentation on the LCCD accuracy of the proposed OBEM is investigated. Four pairs of remote sensing images, one of two pairs (aerial image with 0.5 m/pixel resolution which depict two landslide sites on Landtau Island, Hong Kong, China, are used in the experiments to evaluate the effectiveness of the proposed approach. In addition, the proposed approach is applied, and validated by two case studies, LCCD in Tianjin City China (SPOT-5 satellite image with 2.5 m/pixel resolution and Mexico forest fire case (Landsat TM images with 30 m/pixel resolution, respectively. Quantitative evaluations show that the proposed OBEM post-processing approach can achieve better performance and higher accuracies than several commonly used preprocessing methods. To the best of the authors’ knowledge, this type

Adaptively detecting changes in Autonomic Grid Computing

KAUST Repository

Zhang, Xiangliang; Germain, Cé cile; Sebag, Michè le

2010-01-01

Detecting the changes is the common issue in many application fields due to the non-stationary distribution of the applicative data, e.g., sensor network signals, web logs and gridrunning logs. Toward Autonomic Grid Computing, adaptively detecting

Epigenetic change detection and pattern recognition via Bayesian hierarchical hidden Markov models.

Science.gov (United States)

Wang, Xinlei; Zang, Miao; Xiao, Guanghua

2013-06-15

Epigenetics is the study of changes to the genome that can switch genes on or off and determine which proteins are transcribed without altering the DNA sequence. Recently, epigenetic changes have been linked to the development and progression of disease such as psychiatric disorders. High-throughput epigenetic experiments have enabled researchers to measure genome-wide epigenetic profiles and yield data consisting of intensity ratios of immunoprecipitation versus reference samples. The intensity ratios can provide a view of genomic regions where protein binding occur under one experimental condition and further allow us to detect epigenetic alterations through comparison between two different conditions. However, such experiments can be expensive, with only a few replicates available. Moreover, epigenetic data are often spatially correlated with high noise levels. In this paper, we develop a Bayesian hierarchical model, combined with hidden Markov processes with four states for modeling spatial dependence, to detect genomic sites with epigenetic changes from two-sample experiments with paired internal control. One attractive feature of the proposed method is that the four states of the hidden Markov process have well-defined biological meanings and allow us to directly call the change patterns based on the corresponding posterior probabilities. In contrast, none of existing methods can offer this advantage. In addition, the proposed method offers great power in statistical inference by spatial smoothing (via hidden Markov modeling) and information pooling (via hierarchical modeling). Both simulation studies and real data analysis in a cocaine addiction study illustrate the reliability and success of this method. Copyright © 2012 John Wiley & Sons, Ltd.

No evidence for an item limit in change detection.

Directory of Open Access Journals (Sweden)

Shaiyan Keshvari

Full Text Available Change detection is a classic paradigm that has been used for decades to argue that working memory can hold no more than a fixed number of items ("item-limit models". Recent findings force us to consider the alternative view that working memory is limited by the precision in stimulus encoding, with mean precision decreasing with increasing set size ("continuous-resource models". Most previous studies that used the change detection paradigm have ignored effects of limited encoding precision by using highly discriminable stimuli and only large changes. We conducted two change detection experiments (orientation and color in which change magnitudes were drawn from a wide range, including small changes. In a rigorous comparison of five models, we found no evidence of an item limit. Instead, human change detection performance was best explained by a continuous-resource model in which encoding precision is variable across items and trials even at a given set size. This model accounts for comparison errors in a principled, probabilistic manner. Our findings sharply challenge the theoretical basis for most neural studies of working memory capacity.

Validation of methods for the detection and quantification of engineered nanoparticles in food

DEFF Research Database (Denmark)

Linsinger, T.P.J.; Chaudhry, Q.; Dehalu, V.

2013-01-01

the methods apply equally well to particles of different suppliers. In trueness testing, information whether the particle size distribution has changed during analysis is required. Results are largely expected to follow normal distributions due to the expected high number of particles. An approach...... approach for the validation of methods for detection and quantification of nanoparticles in food samples. It proposes validation of identity, selectivity, precision, working range, limit of detection and robustness, bearing in mind that each “result” must include information about the chemical identity...

SALIENCY-GUIDED CHANGE DETECTION OF REMOTELY SENSED IMAGES USING RANDOM FOREST

Directory of Open Access Journals (Sweden)

W. Feng

2018-04-01

Full Text Available Studies based on object-based image analysis (OBIA representing the paradigm shift in change detection (CD have achieved remarkable progress in the last decade. Their aim has been developing more intelligent interpretation analysis methods in the future. The prediction effect and performance stability of random forest (RF, as a new kind of machine learning algorithm, are better than many single predictors and integrated forecasting method. In this paper, we present a novel CD approach for high-resolution remote sensing images, which incorporates visual saliency and RF. First, highly homogeneous and compact image super-pixels are generated using super-pixel segmentation, and the optimal segmentation result is obtained through image superimposition and principal component analysis (PCA. Second, saliency detection is used to guide the search of interest regions in the initial difference image obtained via the improved robust change vector analysis (RCVA algorithm. The salient regions within the difference image that correspond to the binarized saliency map are extracted, and the regions are subject to the fuzzy c-means (FCM clustering to obtain the pixel-level pre-classification result, which can be used as a prerequisite for superpixel-based analysis. Third, on the basis of the optimal segmentation and pixel-level pre-classification results, different super-pixel change possibilities are calculated. Furthermore, the changed and unchanged super-pixels that serve as the training samples are automatically selected. The spectral features and Gabor features of each super-pixel are extracted. Finally, superpixel-based CD is implemented by applying RF based on these samples. Experimental results on Ziyuan 3 (ZY3 multi-spectral images show that the proposed method outperforms the compared methods in the accuracy of CD, and also confirm the feasibility and effectiveness of the proposed approach.

Saliency-Guided Change Detection of Remotely Sensed Images Using Random Forest

Science.gov (United States)

Feng, W.; Sui, H.; Chen, X.

2018-04-01

Studies based on object-based image analysis (OBIA) representing the paradigm shift in change detection (CD) have achieved remarkable progress in the last decade. Their aim has been developing more intelligent interpretation analysis methods in the future. The prediction effect and performance stability of random forest (RF), as a new kind of machine learning algorithm, are better than many single predictors and integrated forecasting method. In this paper, we present a novel CD approach for high-resolution remote sensing images, which incorporates visual saliency and RF. First, highly homogeneous and compact image super-pixels are generated using super-pixel segmentation, and the optimal segmentation result is obtained through image superimposition and principal component analysis (PCA). Second, saliency detection is used to guide the search of interest regions in the initial difference image obtained via the improved robust change vector analysis (RCVA) algorithm. The salient regions within the difference image that correspond to the binarized saliency map are extracted, and the regions are subject to the fuzzy c-means (FCM) clustering to obtain the pixel-level pre-classification result, which can be used as a prerequisite for superpixel-based analysis. Third, on the basis of the optimal segmentation and pixel-level pre-classification results, different super-pixel change possibilities are calculated. Furthermore, the changed and unchanged super-pixels that serve as the training samples are automatically selected. The spectral features and Gabor features of each super-pixel are extracted. Finally, superpixel-based CD is implemented by applying RF based on these samples. Experimental results on Ziyuan 3 (ZY3) multi-spectral images show that the proposed method outperforms the compared methods in the accuracy of CD, and also confirm the feasibility and effectiveness of the proposed approach.

Change detection by the IR-MAD and kernel MAF methods in Landsat TM data covering a Swedish forest region

DEFF Research Database (Denmark)

Nielsen, Allan Aasbjerg; Olsson, Håkan

2010-01-01

Change over time between two 512 by 512 (25 m by 25 m pixels) multispectral Landsat Thematic Mapper images dated 6 June 1986 and 27 June 1988 respectively covering a forested region in northern Sweden, is here detected by means of the iteratively reweighted multivariate alteration detection (IR-M...

Investigation of environmental change on the Tega Lake based on lake sediment analysis. Pt. 2. Dating of sediment by the lead-210/cesium-137 method and environmental change detected by the diatom assemblage analysis

International Nuclear Information System (INIS)

Hamada, Takaomi

1998-01-01

Sediment collected in the Tega Lake was dated by lead-210/cesium-137 method and environmental change in the Tega Lake was investigated by analysis of diatom remain assemblages in the sediment. Dating of the lead-210/cesium-137 method proved that the surface 30 cm-thickness of sediment in the Tega Lake was deposited during the recent 50 years. Diatom remain assemblage change in the Hon-Tega Lake sediment started in the early half of 1960's and the changes is characterized decrease of Fragilaria construens, that does not prefer to inhabit eutrophic water, and increase of Cyclotella meneghiniana that prefers to inhabit eutrophic water. This diatom assemblage change indicates that the Tega Lake was eutrophicated, and probably suggests water pollution in the Tega Lake. It is detected that influence of residential development around the Tega Lake and reclaiming by drainage on the Tega Lake. (author)

Review of candidate methods for detecting incipient defects due to aging of installed cables in nuclear power plants

International Nuclear Information System (INIS)

Martzloff, F.D.

1988-01-01

Several types of test methods have been proposed for detecting incipient defects due to aging in cable insulation systems, none offering certainty of detecting all possible types of defects. Some methods apply direct detection of a defect in the cable; other methods detect changes in electrical or non-electrical parameters from which inference can be drawn on the integrity of the cable. The paper summarizes the first year of a program conducted at the National Bureau of Standards to assess the potential of success for in situ detection of incipient defects by the most promising of these methods

Leak detection method

International Nuclear Information System (INIS)

1978-01-01

This invention provides a method for removing nuclear fuel elements from a fabrication building while at the same time testing the fuel elements for leaks without releasing contaminants from the fabrication building or from the fuel elements. The vacuum source used, leak detecting mechanism and fuel element fabrication building are specified to withstand environmental hazards. (UK)

Note on Studying Change Point of LRD Traffic Based on Li's Detection of DDoS Flood Attacking

Directory of Open Access Journals (Sweden)

Zhengmin Xia

2010-01-01

Full Text Available Distributed denial-of-service (DDoS flood attacks remain great threats to the Internet. To ensure network usability and reliability, accurate detection of these attacks is critical. Based on Li's work on DDoS flood attack detection, we propose a DDoS detection method by monitoring the Hurst variation of long-range dependant traffic. Specifically, we use an autoregressive system to estimate the Hurst parameter of normal traffic. If the actual Hurst parameter varies significantly from the estimation, we assume that DDoS attack happens. Meanwhile, we propose two methods to determine the change point of Hurst parameter that indicates the occurrence of DDoS attacks. The detection rate associated with one method and false alarm rate for the other method are also derived. The test results on DARPA intrusion detection evaluation data show that the proposed approaches can achieve better detection performance than some well-known self-similarity-based detection methods.

Detection of greenhouse-gas-induced climatic change

International Nuclear Information System (INIS)

Wigley, T.M.L.; Jones, P.D.

1992-01-01

The aims of the US Department of Energy's Carbon Dioxide Research Program are to improve assessments of greenhouse-gas-induced climatic change and to define and reduce uncertainties through selected research. This project will address: The regional and seasonal details of the expected climatic changes; how rapidly will these changes occur; how and when will the climatic effects of CO 2 and other greenhouse gases be first detected; and the relationships between greenhouse-gas-induced climatic change and changes caused by other external and internal factors. The present project addresses all of these questions. Many of the diverse facets of greenhouse-gas-related climate research can be grouped under three interlinked subject areas: modeling, first detection and supporting data. This project will include the analysis of climate forcing factors, the development and refinement of transient response climate models, and the use of instrumental data in validating General Circulation Models (GCMs)

Development of a spatial analysis method using ground-based repeat photography to detect changes in the alpine treeline ecotone, Glacier National Park, Montana, U.S.A.

Science.gov (United States)

Roush, W.; Munroe, Jeffrey S.; Fagre, D.B.

2007-01-01

Repeat photography is a powerful tool for detection of landscape change over decadal timescales. Here a novel method is presented that applies spatial analysis software to digital photo-pairs, allowing vegetation change to be categorized and quantified. This method is applied to 12 sites within the alpine treeline ecotone of Glacier National Park, Montana, and is used to examine vegetation changes over timescales ranging from 71 to 93 years. Tree cover at the treeline ecotone increased in 10 out of the 12 photo-pairs (mean increase of 60%). Establishment occurred at all sites, infilling occurred at 11 sites. To demonstrate the utility of this method, patterns of tree establishment at treeline are described and the possible causes of changes within the treeline ecotone are discussed. Local factors undoubtedly affect the magnitude and type of the observed changes, however the ubiquity of the increase in tree cover implies a common forcing mechanism. Mean minimum summer temperatures have increased by 1.5??C over the past century and, coupled with variations in the amount of early spring snow water equivalent, likely account for much of the increase in tree cover at the treeline ecotone. Lastly, shortcomings of this method are presented along with possible solutions and areas for future research. ?? 2007 Regents of the University of Colorado.

Change Detection in Naturalistic Pictures among Children with Autism

Science.gov (United States)

Burack, Jacob A.; Joseph, Shari; Russo, Natalie; Shore, David I.; Porporino, Mafalda; Enns, James T.

2009-01-01

Persons with autism often show strong reactions to changes in the environment, suggesting that they may detect changes more efficiently than typically developing (TD) persons. However, Fletcher-Watson et al. (Br J Psychol 97:537-554, 2006) reported no differences between adults with autism and TD adults with a change-detection task. In this study,…

Coherent Change Detection with COSMO SkyMed Data-experimental Results

OpenAIRE

A. Mishra; D. Chaudhuri; C. Bhattacharya; Y.S. Rao

2013-01-01

Change detection is a technique in which we try to find changes between two acquisitions. These acquisitions can be from different platforms and sensors. Acquisition from satellite using synthetic aperture radar (SAR) is of immense interest to military applications. Satellite has the ability to peep into the enemy territory while SAR has the capability of day and night operations, being an active sensor. Coherent change detection (CCD) can be used to detect minute changes between two images. ...

Detection methods for irradiated mites and insects

International Nuclear Information System (INIS)

Ignatowicz, S.

1999-01-01

Results of the study on the following tests for separation of irradiated pests from untreated ones are reported: (a) test for identification of irradiated mites (Acaridae) based on lack of fecundity of treated females; (b) test for identification of irradiated beetles based on their locomotor activity; (c) test for identification of irradiated pests based on electron spin resonance (ESR) signal derived from treated insects; (d) test for identification of irradiated pests based on changes in the midgut induced by gamma radiation; and (e) test for identification of irradiated pests based on the alterations in total proteins of treated adults. Of these detection methods, only the test based on the pathological changes induced by irradiation in the insect midgut may identify consistently either irradiated larvae or adults. This test is simple and convenient when a rapid processing technique for dehydrating and embedding the midgut is used. (author)

Improving the measurement of longitudinal change in renal function: automated detection of changes in laboratory creatinine assay

Directory of Open Access Journals (Sweden)

Norman Poh

2015-04-01

Full Text Available IntroductionRenal function is reported using the estimates of glomerular filtration rate (eGFR. However, eGFR values are recorded without reference to the particular serum creatinine (SCr assays used to derive them, and newer assays were introduced at different time points across the laboratories in the United Kingdom. These changes may cause systematic bias in eGFR reported in routinely collected data, even though laboratory-reported eGFR values have a correction factor applied.DesignAn algorithm to detect changes in SCr that in turn affect eGFR calculation method was developed. It compares the mapping of SCr values on to eGFR values across a time series of paired eGFR and SCr measurements.SettingRoutinely collected primary care data from 20,000 people with the richest renal function data from the quality improvement in chronic kidney disease trial.ResultsThe algorithm identified a change in eGFR calculation method in 114 (90% of the 127 included practices. This change was identified in 4736 (23.7% patient time series analysed. This change in calibration method was found to cause a significant step change in the reported eGFR values, producing a systematic bias. The eGFR values could not be recalibrated by applying the Modification of Diet in Renal Disease equation to the laboratory reported SCr values.ConclusionsThis algorithm can identify laboratory changes in eGFR calculation methods and changes in SCr assay. Failure to account for these changes may misconstrue renal function changes over time. Researchers using routine eGFR data should account for these effects.  

Wide area change detection with satellite imagery for locating underground nuclear testing

International Nuclear Information System (INIS)

Canty, M.J.; Jasani, B.; Schlittenhardt, J.

2001-01-01

With the advent of high resolution optical imagery from commercial earth observation satellites, the use of remote sensing data for verification of nuclear non-proliferation agreements is becoming increasingly attractive. Non-governmental organizations are routinely publishing high-quality imagery of sensitive nuclear installations round the world, and international verification authorities, such as the International Atomic Energy Agency (IAEA) or the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO), will also want to make use, directly or indirectly, of this additional open source of information. Exact location of the sites of underground nuclear explosions is a task eminently suited to satellite imagery. Here both moderate resolutions for detecting signals in very large testing ranges as well as high resolution images for exact interpretation play important roles. We describe in our paper a particularly sensitive change detection procedure for bitemporal, multispectral satellite imagery which can be used to locate the spall zone of underground nuclear explosions with commercial satellite imagery. The method is based on the multivariate alteration detection (MAD) technique of Nielsen et al. Linear combinations of the spectral channels in two images of the same scene are chosen so as to minimize their positive correlation. This leads to a series of difference images - the so-called MAD components - which are mutually orthogonal (uncorrelated) and ordered according to decreasing variance in their pixel intensities. Since interesting changes in man-made structures may contribute minimally to the overall variance (as the latter may be dominated for instance by seasonal vegetation differences) it is often the case that such changes turn up in a higher order MAD component. This is because they will be uncorrelated with seasonal vegetation changes, stochastic image noise or other major contributions to the overall change signal. This in fact is one of the

An ontology-based annotation of cardiac implantable electronic devices to detect therapy changes in a national registry.

Science.gov (United States)

Rosier, Arnaud; Mabo, Philippe; Chauvin, Michel; Burgun, Anita

2015-05-01

The patient population benefitting from cardiac implantable electronic devices (CIEDs) is increasing. This study introduces a device annotation method that supports the consistent description of the functional attributes of cardiac devices and evaluates how this method can detect device changes from a CIED registry. We designed the Cardiac Device Ontology, an ontology of CIEDs and device functions. We annotated 146 cardiac devices with this ontology and used it to detect therapy changes with respect to atrioventricular pacing, cardiac resynchronization therapy, and defibrillation capability in a French national registry of patients with implants (STIDEFIX). We then analyzed a set of 6905 device replacements from the STIDEFIX registry. Ontology-based identification of therapy changes (upgraded, downgraded, or similar) was accurate (6905 cases) and performed better than straightforward analysis of the registry codes (F-measure 1.00 versus 0.75 to 0.97). This study demonstrates the feasibility and effectiveness of ontology-based functional annotation of devices in the cardiac domain. Such annotation allowed a better description and in-depth analysis of STIDEFIX. This method was useful for the automatic detection of therapy changes and may be reused for analyzing data from other device registries.

Detection methods of irradiated foodstuffs

Energy Technology Data Exchange (ETDEWEB)

Ponta, C C; Cutrubinis, M; Georgescu, R [IRASM Center, Horia Hulubei National Institute for Physics and Nuclear Engineering, PO Box MG-6, RO-077125 Magurele-Bucharest (Romania); Mihai, R [Life and Environmental Physics Department, Horia Hulubei National Institute for Physics and Nuclear Engineering, PO Box MG-6, RO-077125 Magurele-Bucharest (Romania); Secu, M [National Institute of Materials Physics, Bucharest (Romania)

2005-07-01

food is marketed as irradiated or if irradiated goods are sold without the appropriate labeling, then detection tests should be able to prove the authenticity of the product. For the moment in Romania there is not any food control laboratory able to detect irradiated foodstuffs. The Technological Irradiation Department coordinates and co finances a research project aimed to establish the first Laboratory of Irradiated Foodstuffs Detection. The detection methods studied in this project are the ESR methods (for cellulose EN 1787/2000, bone EN 1786/1996 and crystalline sugar EN 13708/2003), the TL method (EN 1788/2001), the PSL method (EN 13751/2002) and the DNA Comet Assay method (EN 13784/2001). The above detection methods will be applied on various foodstuffs such: garlic, onion, potatoes, rice, beans, wheat, maize, pistachio, sunflower seeds, raisins, figs, strawberries, chicken, beef, fish, pepper, paprika, thyme, laurel and mushrooms. As an example of the application of a detection method there are presented the ESR spectra of irradiated and nonirradiated paprika acquired according to ESR detection method for irradiated foodstuffs containing cellulose. First of all it can be noticed that the intensity of the signal of cellulose is much higher for the irradiated sample than that for the nonirradiated one and second that appear two radiation specific signals symmetrical to the cellulose signal. These two radiation specific signals prove the irradiation treatment of paprika. (author)

Detection of protein concentrations using a pH-step titration method

NARCIS (Netherlands)

Kruise, J.; Kruise, J.; Eijkel, Jan C.T.; Bergveld, Piet

1997-01-01

A stimulus-response method based on the application of a pH step is proposed for the detection of protein immobilized in a membrane on top of an ion-sensitive field-effect transistor (ISFET). The ISFET response to a step-wise change in pH, applied at the interface between the membrane and the

Spices, irradiation and detection methods

International Nuclear Information System (INIS)

Sjoeberg, A.M.; Manninen, M.

1991-01-01

This paper is about microbiological aspects of spices and microbiological methods to detect irradiated food. The proposed method is a combination of the Direct Epifluorescence Filter Technique (DEFT) and the Aerobic Plate Count (APC). The evidence for irradiation of spices is based on the demonstration of a higher DEFT count than the APC. The principle was first tested in our earlier investigation in the detection of irradiation of whole spices. The combined DEFT+APC procedure was found to give a fairly reliable indication of whether or not a whole spice sample had been irradiated. The results are given (8 figs, 22 refs)

Non-linear laws of echoic memory and auditory change detection in humans

OpenAIRE

Inui, Koji; Urakawa, Tomokazu; Yamashiro, Koya; Otsuru, Naofumi; Nishihara, Makoto; Takeshima, Yasuyuki; Keceli, Sumru; Kakigi, Ryusuke

2010-01-01

Abstract Background The detection of any abrupt change in the environment is important to survival. Since memory of preceding sensory conditions is necessary for detecting changes, such a change-detection system relates closely to the memory system. Here we used an auditory change-related N1 subcomponent (change-N1) of event-related brain potentials to investigate cortical mechanisms underlying change detection and echoic memory. Results Change-N1 was elicited by a simple paradigm with two to...

On the pilot's behavior of detecting a system parameter change

Science.gov (United States)

Morizumi, N.; Kimura, H.

1986-01-01

The reaction of a human pilot, engaged in compensatory control, to a sudden change in the controlled element's characteristics is described. Taking the case where the change manifests itself as a variance change of the monitored signal, it is shown that the detection time, defined to be the time elapsed until the pilot detects the change, is related to the monitored signal and its derivative. Then, the detection behavior is modeled by an optimal controller, an optimal estimator, and a variance-ratio test mechanism that is performed for the monitored signal and its derivative. Results of a digital simulation show that the pilot's detection behavior can be well represented by the model proposed here.

CARAT: A novel method for allelic detection of DNA copy number changes using high density oligonucleotide arrays

Directory of Open Access Journals (Sweden)

Ishikawa Shumpei

2006-02-01

Full Text Available Abstract Background DNA copy number alterations are one of the main characteristics of the cancer cell karyotype and can contribute to the complex phenotype of these cells. These alterations can lead to gains in cellular oncogenes as well as losses in tumor suppressor genes and can span small intervals as well as involve entire chromosomes. The ability to accurately detect these changes is central to understanding how they impact the biology of the cell. Results We describe a novel algorithm called CARAT (Copy Number Analysis with Regression And Tree that uses probe intensity information to infer copy number in an allele-specific manner from high density DNA oligonuceotide arrays designed to genotype over 100, 000 SNPs. Total and allele-specific copy number estimations using CARAT are independently evaluated for a subset of SNPs using quantitative PCR and allelic TaqMan reactions with several human breast cancer cell lines. The sensitivity and specificity of the algorithm are characterized using DNA samples containing differing numbers of X chromosomes as well as a test set of normal individuals. Results from the algorithm show a high degree of agreement with results from independent verification methods. Conclusion Overall, CARAT automatically detects regions with copy number variations and assigns a significance score to each alteration as well as generating allele-specific output. When coupled with SNP genotype calls from the same array, CARAT provides additional detail into the structure of genome wide alterations that can contribute to allelic imbalance.

Real-time change detection in data streams with FPGAs

International Nuclear Information System (INIS)

Vega, J.; Dormido-Canto, S.; Cruz, T.; Ruiz, M.; Barrera, E.; Castro, R.; Murari, A.; Ochando, M.

2014-01-01

Highlights: • Automatic recognition of changes in data streams of multidimensional signals. • Detection algorithm based on testing exchangeability on-line. • Real-time and off-line applicability. • Real-time implementation in FPGAs. - Abstract: The automatic recognition of changes in data streams is useful in both real-time and off-line data analyses. This article shows several effective change-detecting algorithms (based on martingales) and describes their real-time applicability in the data acquisition systems through the use of Field Programmable Gate Arrays (FPGA). The automatic event recognition system is absolutely general and it does not depend on either the particular event to detect or the specific data representation (waveforms, images or multidimensional signals). The developed approach provides good results for change detection in both the temporal evolution of profiles and the two-dimensional spatial distribution of volume emission intensity. The average computation time in the FPGA is 210 μs per profile

Unsupervised Multi-Scale Change Detection from SAR Imagery for Monitoring Natural and Anthropogenic Disasters

Science.gov (United States)

Ajadi, Olaniyi A.

Radar remote sensing can play a critical role in operational monitoring of natural and anthropogenic disasters. Despite its all-weather capabilities, and its high performance in mapping, and monitoring of change, the application of radar remote sensing in operational monitoring activities has been limited. This has largely been due to: (1) the historically high costs associated with obtaining radar data; (2) slow data processing, and delivery procedures; and (3) the limited temporal sampling that was provided by spaceborne radar-based satellites. Recent advances in the capabilities of spaceborne Synthetic Aperture Radar (SAR) sensors have developed an environment that now allows for SAR to make significant contributions to disaster monitoring. New SAR processing strategies that can take full advantage of these new sensor capabilities are currently being developed. Hence, with this PhD dissertation, I aim to: (i) investigate unsupervised change detection techniques that can reliably extract signatures from time series of SAR images, and provide the necessary flexibility for application to a variety of natural, and anthropogenic hazard situations; (ii) investigate effective methods to reduce the effects of speckle and other noise on change detection performance; (iii) automate change detection algorithms using probabilistic Bayesian inferencing; and (iv) ensure that the developed technology is applicable to current, and future SAR sensors to maximize temporal sampling of a hazardous event. This is achieved by developing new algorithms that rely on image amplitude information only, the sole image parameter that is available for every single SAR acquisition.. The motivation and implementation of the change detection concept are described in detail in Chapter 3. In the same chapter, I demonstrated the technique's performance using synthetic data as well as a real-data application to map wildfire progression. I applied Radiometric Terrain Correction (RTC) to the data to

Improved GLR method to instrument failure detection

International Nuclear Information System (INIS)

Jeong, Hak Yeoung; Chang, Soon Heung

1985-01-01

The generalized likehood radio(GLR) method performs statistical tests on the innovations sequence of a Kalman-Buchy filter state estimator for system failure detection and its identification. However, the major drawback of the convensional GLR is to hypothesize particular failure type in each case. In this paper, a method to solve this drawback is proposed. The improved GLR method is applied to a PWR pressurizer and gives successful results in detection and identification of any failure. Furthmore, some benefit on the processing time per each cycle of failure detection and its identification can be accompanied. (Author)

Thermoelectric SQUID method for the detection of segregations

Science.gov (United States)

Hinken, Johann H.; Tavrin, Yury

2000-05-01

Aero engine turbine discs are most critical parts. Material inhomogeneities can cause disc fractures during the flight with fatal air disasters. Nondestructive testing (NDT) of the discs in various machining steps is necessary and performed as well as possible. Conventional NDT methods, however, like eddy current testing and ultrasonic testing have unacceptable limits. For example, subsurface segregations often cannot be detected directly but only indirectly in such cases when cracks already have developed from them. This may be too late. A new NDT method, which we call the Thermoelectric SQUID Method, has been developed. It allows for the detection of metallic inclusions within non-ferromagnetic metallic base material. This paper describes the results of a feasibility study on aero engine turbine discs made from Inconel® 718. These contained segregations that had been detected before by anodic etching. With the Thermoelectric SQUID Method, these segregations were detected again, and further segregations below the surfaces have been found, which had not been detected before. For this new NDT method the disc material is quasi-transparent. The Thermoelectric SQUID Method is also useful to detect distributed and localized inhomogeneities in pure metals like niobium sheets for particle accelerators.

Detecting land cover change using an extended Kalman filter on MODIS NDVI time-series data

CSIR Research Space (South Africa)

Kleynhans, W

2011-05-01

Full Text Available A method for detecting land cover change using NDVI time-series data derived from 500-m MODIS satellite data is proposed. The algorithm acts as a per-pixel change alarm and takes the NDVI time series of a 3 × 3 grid of MODIS pixels as the input...

Reliability Study Regarding the Use of Histogram Similarity Methods for Damage Detection

Directory of Open Access Journals (Sweden)

Nicoleta Gillich

2013-01-01

Full Text Available The paper analyses the reliability of three dissimilarity estimators to compare histograms, as support for a frequency-based damage detection method, able to identify structural changes in beam-like structures. First a brief presentation of the own developed damage detection method is made, with focus on damage localization. It consists actually in comparing a histogram derived from measurement results, with a large series of histograms, namely the damage location indexes for all locations along the beam, obtained by calculus. We tested some dissimilarity estimators like the Minkowski-form Distances, the Kullback-Leibler Divergence and the Histogram Intersection and found the Minkowski Distance as the method providing best results. It was tested for numerous locations, using real measurement results and with results artificially debased by noise, proving its reliability.

Stroller running: Energetic and kinematic changes across pushing methods.

Science.gov (United States)

Alcantara, Ryan S; Wall-Scheffler, Cara M

2017-01-01

Running with a stroller provides an opportunity for parents to exercise near their child and counteract health declines experienced during early parenthood. Understanding biomechanical and physiological changes that occur when stroller running is needed to evaluate its health impact, yet the effects of stroller running have not been clearly presented. Here, three commonly used stroller pushing methods were investigated to detect potential changes in energetic cost and lower-limb kinematics. Sixteen individuals (M/F: 10/6) ran at self-selected speeds for 800m under three stroller conditions (2-Hands, 1-Hand, and Push/Chase) and an independent running control. A significant decrease in speed (p = 0.001) and stride length (ppushing method had a significant effect on speed (p = 0.001) and stride length (ppushing technique influences stroller running speed and kinematics. These findings suggest specific fitness effects may be achieved through the implementation of different pushing methods.

Comparative analysis of methods for detecting interacting loci.

Science.gov (United States)

Chen, Li; Yu, Guoqiang; Langefeld, Carl D; Miller, David J; Guy, Richard T; Raghuram, Jayaram; Yuan, Xiguo; Herrington, David M; Wang, Yue

2011-07-05

Interactions among genetic loci are believed to play an important role in disease risk. While many methods have been proposed for detecting such interactions, their relative performance remains largely unclear, mainly because different data sources, detection performance criteria, and experimental protocols were used in the papers introducing these methods and in subsequent studies. Moreover, there have been very few studies strictly focused on comparison of existing methods. Given the importance of detecting gene-gene and gene-environment interactions, a rigorous, comprehensive comparison of performance and limitations of available interaction detection methods is warranted. We report a comparison of eight representative methods, of which seven were specifically designed to detect interactions among single nucleotide polymorphisms (SNPs), with the last a popular main-effect testing method used as a baseline for performance evaluation. The selected methods, multifactor dimensionality reduction (MDR), full interaction model (FIM), information gain (IG), Bayesian epistasis association mapping (BEAM), SNP harvester (SH), maximum entropy conditional probability modeling (MECPM), logistic regression with an interaction term (LRIT), and logistic regression (LR) were compared on a large number of simulated data sets, each, consistent with complex disease models, embedding multiple sets of interacting SNPs, under different interaction models. The assessment criteria included several relevant detection power measures, family-wise type I error rate, and computational complexity. There are several important results from this study. First, while some SNPs in interactions with strong effects are successfully detected, most of the methods miss many interacting SNPs at an acceptable rate of false positives. In this study, the best-performing method was MECPM. Second, the statistical significance assessment criteria, used by some of the methods to control the type I error rate

Comparative analysis of methods for detecting interacting loci

Directory of Open Access Journals (Sweden)

Yuan Xiguo

2011-07-01

Full Text Available Abstract Background Interactions among genetic loci are believed to play an important role in disease risk. While many methods have been proposed for detecting such interactions, their relative performance remains largely unclear, mainly because different data sources, detection performance criteria, and experimental protocols were used in the papers introducing these methods and in subsequent studies. Moreover, there have been very few studies strictly focused on comparison of existing methods. Given the importance of detecting gene-gene and gene-environment interactions, a rigorous, comprehensive comparison of performance and limitations of available interaction detection methods is warranted. Results We report a comparison of eight representative methods, of which seven were specifically designed to detect interactions among single nucleotide polymorphisms (SNPs, with the last a popular main-effect testing method used as a baseline for performance evaluation. The selected methods, multifactor dimensionality reduction (MDR, full interaction model (FIM, information gain (IG, Bayesian epistasis association mapping (BEAM, SNP harvester (SH, maximum entropy conditional probability modeling (MECPM, logistic regression with an interaction term (LRIT, and logistic regression (LR were compared on a large number of simulated data sets, each, consistent with complex disease models, embedding multiple sets of interacting SNPs, under different interaction models. The assessment criteria included several relevant detection power measures, family-wise type I error rate, and computational complexity. There are several important results from this study. First, while some SNPs in interactions with strong effects are successfully detected, most of the methods miss many interacting SNPs at an acceptable rate of false positives. In this study, the best-performing method was MECPM. Second, the statistical significance assessment criteria, used by some of the

CHANGE DETECTION VIA SELECTIVE GUIDED CONTRASTING FILTERS

Directory of Open Access Journals (Sweden)

Y. V. Vizilter

2017-05-01

Full Text Available Change detection scheme based on guided contrasting was previously proposed. Guided contrasting filter takes two images (test and sample as input and forms the output as filtered version of test image. Such filter preserves the similar details and smooths the non-similar details of test image with respect to sample image. Due to this the difference between test image and its filtered version (difference map could be a basis for robust change detection. Guided contrasting is performed in two steps: at the first step some smoothing operator (SO is applied for elimination of test image details; at the second step all matched details are restored with local contrast proportional to the value of some local similarity coefficient (LSC. The guided contrasting filter was proposed based on local average smoothing as SO and local linear correlation as LSC. In this paper we propose and implement new set of selective guided contrasting filters based on different combinations of various SO and thresholded LSC. Linear average and Gaussian smoothing, nonlinear median filtering, morphological opening and closing are considered as SO. Local linear correlation coefficient, morphological correlation coefficient (MCC, mutual information, mean square MCC and geometrical correlation coefficients are applied as LSC. Thresholding of LSC allows operating with non-normalized LSC and enhancing the selective properties of guided contrasting filters: details are either totally recovered or not recovered at all after the smoothing. These different guided contrasting filters are tested as a part of previously proposed change detection pipeline, which contains following stages: guided contrasting filtering on image pyramid, calculation of difference map, binarization, extraction of change proposals and testing change proposals using local MCC. Experiments on real and simulated image bases demonstrate the applicability of all proposed selective guided contrasting filters. All

Detection of Early Ischemic Changes in Noncontrast CT Head Improved with "Stroke Windows".

Science.gov (United States)

Mainali, Shraddha; Wahba, Mervat; Elijovich, Lucas

2014-01-01

Introduction. Noncontrast head CT (NCCT) is the standard radiologic test for patients presenting with acute stroke. Early ischemic changes (EIC) are often overlooked on initial NCCT. We determine the sensitivity and specificity of improved EIC detection by a standardized method of image evaluation (Stroke Windows). Methods. We performed a retrospective chart review to identify patients with acute ischemic stroke who had NCCT at presentation. EIC was defined by the presence of hyperdense MCA/basilar artery sign; sulcal effacement; basal ganglia/subcortical hypodensity; and loss of cortical gray-white differentiation. NCCT was reviewed with standard window settings and with specialized Stroke Windows. Results. Fifty patients (42% females, 58% males) with a mean NIHSS of 13.4 were identified. EIC was detected in 9 patients with standard windows, while EIC was detected using Stroke Windows in 35 patients (18% versus 70%; P Windows (14% and 36%; P Windows (6% and 46%; P Windows significantly improved detection of EIC.

Supporting dynamic change detection: using the right tool for the task.

Science.gov (United States)

Vallières, Benoît R; Hodgetts, Helen M; Vachon, François; Tremblay, Sébastien

2016-01-01

Detecting task-relevant changes in a visual scene is necessary for successfully monitoring and managing dynamic command and control situations. Change blindness-the failure to notice visual changes-is an important source of human error. Change History EXplicit (CHEX) is a tool developed to aid change detection and maintain situation awareness; and in the current study we test the generality of its ability to facilitate the detection of changes when this subtask is embedded within a broader dynamic decision-making task. A multitasking air-warfare simulation required participants to perform radar-based subtasks, for which change detection was a necessary aspect of the higher-order goal of protecting one's own ship. In this task, however, CHEX rendered the operator even more vulnerable to attentional failures in change detection and increased perceived workload. Such support was only effective when participants performed a change detection task without concurrent subtasks. Results are interpreted in terms of the NSEEV model of attention behavior (Steelman, McCarley, & Wickens, Hum. Factors 53:142-153, 2011; J. Exp. Psychol. Appl. 19:403-419, 2013), and suggest that decision aids for use in multitasking contexts must be designed to fit within the available workload capacity of the user so that they may truly augment cognition.

Nonexplicit change detection in complex dynamic settings: what eye movements reveal.

Science.gov (United States)

Vachon, François; Vallières, Benoît R; Jones, Dylan M; Tremblay, Sébastien

2012-12-01

We employed a computer-controlled command-and-control (C2) simulation and recorded eye movements to examine the extent and nature of the inability to detect critical changes in dynamic displays when change detection is implicit (i.e., requires no explicit report) to the operator's task. Change blindness-the failure to notice significant changes to a visual scene-may have dire consequences on performance in C2 and surveillance operations. Participants performed a radar-based risk-assessment task involving multiple subtasks. Although participants were not required to explicitly report critical changes to the operational display, change detection was critical in informing decision making. Participants' eye movements were used as an index of visual attention across the display. Nonfixated (i.e., unattended) changes were more likely to be missed than were fixated (i.e., attended) changes, supporting the idea that focused attention is necessary for conscious change detection. The finding of significant pupil dilation for changes undetected but fixated suggests that attended changes can nonetheless be missed because of a failure of attentional processes. Change blindness in complex dynamic displays takes the form of failures in establishing task-appropriate patterns of attentional allocation. These findings have implications in the design of change-detection support tools for dynamic displays and work procedure in C2 and surveillance.

Sensitive change detection for remote sensing monitoring of nuclear treaties

DEFF Research Database (Denmark)

Canty, Morton J.; Nielsen, Allan Aasbjerg; Schlittenhardt, Jörg

2005-01-01

change is a commonplace application in remote sensing, the detection of anthropogenic changes associated with nuclear activities, whether declared or clandestine, presents a difficult challenge. It is necessary to discriminate subtle, often weak signals of interest on a background of irrelevant...... in multispectral, bitemporal image data: New approaches to change detection studies, Remote Sens. Environ. 64(1), 1998, pp. 1--19. Nielsen, A. A., Iteratively re-weighted multivariate alteration detection in multi- and hyperspectral data, to be published....

Detecting changes in the nutritional value and elemental composition of transgenic sorghum grain

CSIR Research Space (South Africa)

Ndimba, R

2015-09-01

Full Text Available Instruments and Methods in Physics Research B 363 (2015) 183–187 Detecting changes in the nutritional value and elemental composition of transgenic sorghum grain R. Ndimba a,c,, A.W. Grootboom b, L. Mehlo b, N.L. Mkhonza b, J. Kossmann c, A...

Multi- and hyperspectral remote sensing change detection with generalized difference images by the IR-MAD method

DEFF Research Database (Denmark)

Nielsen, Allan Aasbjerg; Canty, Morton J.

2005-01-01

-based method for determining thresholds for differentiating between change and no-change in the difference images, and for estimating the variance of the no-change observations. This variance is used to establish a single change/no-change image based on the general multivariate difference image. The resulting....../no-change image can be used to establish both change regions and to extract observations based on which a fully automated orthogonal regression analysis based normalization of the multivariate data between the two points in time can be developed. Also, regularization issues typically important in connection...

Building Change Detection from Harvey using Unmanned Aerial System (UAS)

Science.gov (United States)

Chang, A.; Yeom, J.; Jung, J.; Choi, I.

2017-12-01

Unmanned Aerial System (UAS) is getting to be the most important technique in recent days since the fine spatial and high temporal resolution data previously unobtainable from traditional remote sensing platforms. Advanced UAS data can provide a great opportunity for disaster monitoring. Especially, building change detection is the one of the most important topics for damage assessment and recovery from disasters. This study is proposing a method to monitor building change with UAS data for Holiday Beach in Texas, where was directly hit by Harvey on 25 August 2017. This study adopted 3D change detection to monitor building damage and recovery levels with building height as well as natural color information. We used a rotorcraft UAS to collect RGB data twice on 9 September and 18 October 2017 after the hurricane. The UAS data was processed using Agisoft Photoscan Pro Software to generate super high resolution dataset including orthomosaic, DSM (Digital Surface Model), and 3D point cloud. We compared the processed dataset with an airborne image considerable as before-hurricane data, which was acquired on January 2016. Building damage and recovery levels were determined by height and color change. The result will show that UAS data is useful to assess building damage and recovery for affected area by the natural disaster such as Harvey.

Hardware accelerator design for change detection in smart camera

Science.gov (United States)

Singh, Sanjay; Dunga, Srinivasa Murali; Saini, Ravi; Mandal, A. S.; Shekhar, Chandra; Chaudhury, Santanu; Vohra, Anil

2011-10-01

Smart Cameras are important components in Human Computer Interaction. In any remote surveillance scenario, smart cameras have to take intelligent decisions to select frames of significant changes to minimize communication and processing overhead. Among many of the algorithms for change detection, one based on clustering based scheme was proposed for smart camera systems. However, such an algorithm could achieve low frame rate far from real-time requirements on a general purpose processors (like PowerPC) available on FPGAs. This paper proposes the hardware accelerator capable of detecting real time changes in a scene, which uses clustering based change detection scheme. The system is designed and simulated using VHDL and implemented on Xilinx XUP Virtex-IIPro FPGA board. Resulted frame rate is 30 frames per second for QVGA resolution in gray scale.

Brain correlates of automatic visual change detection.

Science.gov (United States)

Cléry, H; Andersson, F; Fonlupt, P; Gomot, M

2013-07-15

A number of studies support the presence of visual automatic detection of change, but little is known about the brain generators involved in such processing and about the modulation of brain activity according to the salience of the stimulus. The study presented here was designed to locate the brain activity elicited by unattended visual deviant and novel stimuli using fMRI. Seventeen adult participants were presented with a passive visual oddball sequence while performing a concurrent visual task. Variations in BOLD signal were observed in the modality-specific sensory cortex, but also in non-specific areas involved in preattentional processing of changing events. A degree-of-deviance effect was observed, since novel stimuli elicited more activity in the sensory occipital regions and at the medial frontal site than small changes. These findings could be compared to those obtained in the auditory modality and might suggest a "general" change detection process operating in several sensory modalities. Copyright © 2013 Elsevier Inc. All rights reserved.

Visual long-term memory and change blindness: Different effects of pre- and post-change information on one-shot change detection using meaningless geometric objects.

Science.gov (United States)

Nishiyama, Megumi; Kawaguchi, Jun

2014-11-01

To clarify the relationship between visual long-term memory (VLTM) and online visual processing, we investigated whether and how VLTM involuntarily affects the performance of a one-shot change detection task using images consisting of six meaningless geometric objects. In the study phase, participants observed pre-change (Experiment 1), post-change (Experiment 2), or both pre- and post-change (Experiment 3) images appearing in the subsequent change detection phase. In the change detection phase, one object always changed between pre- and post-change images and participants reported which object was changed. Results showed that VLTM of pre-change images enhanced the performance of change detection, while that of post-change images decreased accuracy. Prior exposure to both pre- and post-change images did not influence performance. These results indicate that pre-change information plays an important role in change detection, and that information in VLTM related to the current task does not always have a positive effect on performance. Copyright © 2014 Elsevier Inc. All rights reserved.

Volumetric Forest Change Detection Through Vhr Satellite Imagery

Science.gov (United States)

Akca, Devrim; Stylianidis, Efstratios; Smagas, Konstantinos; Hofer, Martin; Poli, Daniela; Gruen, Armin; Sanchez Martin, Victor; Altan, Orhan; Walli, Andreas; Jimeno, Elisa; Garcia, Alejandro

2016-06-01

Quick and economical ways of detecting of planimetric and volumetric changes of forest areas are in high demand. A research platform, called FORSAT (A satellite processing platform for high resolution forest assessment), was developed for the extraction of 3D geometric information from VHR (very-high resolution) imagery from satellite optical sensors and automatic change detection. This 3D forest information solution was developed during a Eurostars project. FORSAT includes two main units. The first one is dedicated to the geometric and radiometric processing of satellite optical imagery and 2D/3D information extraction. This includes: image radiometric pre-processing, image and ground point measurement, improvement of geometric sensor orientation, quasiepipolar image generation for stereo measurements, digital surface model (DSM) extraction by using a precise and robust image matching approach specially designed for VHR satellite imagery, generation of orthoimages, and 3D measurements in single images using mono-plotting and in stereo images as well as triplets. FORSAT supports most of the VHR optically imagery commonly used for civil applications: IKONOS, OrbView - 3, SPOT - 5 HRS, SPOT - 5 HRG, QuickBird, GeoEye-1, WorldView-1/2, Pléiades 1A/1B, SPOT 6/7, and sensors of similar type to be expected in the future. The second unit of FORSAT is dedicated to 3D surface comparison for change detection. It allows users to import digital elevation models (DEMs), align them using an advanced 3D surface matching approach and calculate the 3D differences and volume changes between epochs. To this end our 3D surface matching method LS3D is being used. FORSAT is a single source and flexible forest information solution with a very competitive price/quality ratio, allowing expert and non-expert remote sensing users to monitor forests in three and four dimensions from VHR optical imagery for many forest information needs. The capacity and benefits of FORSAT have been tested in

Automated baseline change detection - Phases 1 and 2. Final report

International Nuclear Information System (INIS)

Byler, E.

1997-01-01

The primary objective of this project is to apply robotic and optical sensor technology to the operational inspection of mixed toxic and radioactive waste stored in barrels, using Automated Baseline Change Detection (ABCD), based on image subtraction. Absolute change detection is based on detecting any visible physical changes, regardless of cause, between a current inspection image of a barrel and an archived baseline image of the same barrel. Thus, in addition to rust, the ABCD system can also detect corrosion, leaks, dents, and bulges. The ABCD approach and method rely on precise camera positioning and repositioning relative to the barrel and on feature recognition in images. The ABCD image processing software was installed on a robotic vehicle developed under a related DOE/FETC contract DE-AC21-92MC29112 Intelligent Mobile Sensor System (IMSS) and integrated with the electronics and software. This vehicle was designed especially to navigate in DOE Waste Storage Facilities. Initial system testing was performed at Fernald in June 1996. After some further development and more extensive integration the prototype integrated system was installed and tested at the Radioactive Waste Management Facility (RWMC) at INEEL beginning in April 1997 through the present (November 1997). The integrated system, composed of ABCD imaging software and IMSS mobility base, is called MISS EVE (Mobile Intelligent Sensor System--Environmental Validation Expert). Evaluation of the integrated system in RWMC Building 628, containing approximately 10,000 drums, demonstrated an easy to use system with the ability to properly navigate through the facility, image all the defined drums, and process the results into a report delivered to the operator on a GUI interface and on hard copy. Further work is needed to make the brassboard system more operationally robust

Development and Establishment of Detection Method of Irradiated Foods

International Nuclear Information System (INIS)

Byun, Myung Woo; Lee, Ju Woon; Kim, Dong Ho; Jo, Cheo Run; Kim, Jang Ho; Kim, Kyong Su

2004-12-01

The present project was related to the development and establishment of the detection techniques for the safety management of gamma-irradiated food and particularly conducted for the establishment of standard detection method for gamma-irradiated dried spices and raw materials, dried meat and fish powder for processed foods, bean paste powder, red pepper paste powder, soy sauce powder, and starch for flavoring ingredients described in 3, 6, 7 section of Korean Food Standard. Since the approvement of gamma-irradiated food items will be enlarged due to the international tendency for gamma-irradiated food, it was concluded that the establishment of detailed detection methods for each food group is not efficient for the enactment and enforcement of related regulations. For this reason, in order to establish the standard detection method, a detection system for gamma-irradiated food suitable for domestic operation was studied using comparative analysis of domestic and foreign research data classified by items and methods and European Standard as a reference. According to the comparative analyses of domestic and foreign research data and regulations of detection for gamma-irradiated food, it was concluded to be desirable that the optimal detection method should be decided after principal detection tests such as physical, chemical, and biological detection methods are established as standard methods and that the specific descriptions such as pre-treatment of raw materials, test methods, and the evaluation of results should be separately prescribed

Development and Establishment of Detection Method of Irradiated Foods

Energy Technology Data Exchange (ETDEWEB)

Byun, Myung Woo; Lee, Ju Woon; Kim, Dong Ho; Jo, Cheo Run; Kim, Jang Ho; Kim, Kyong Su

2004-12-15

The present project was related to the development and establishment of the detection techniques for the safety management of gamma-irradiated food and particularly conducted for the establishment of standard detection method for gamma-irradiated dried spices and raw materials, dried meat and fish powder for processed foods, bean paste powder, red pepper paste powder, soy sauce powder, and starch for flavoring ingredients described in 3, 6, 7 section of Korean Food Standard. Since the approvement of gamma-irradiated food items will be enlarged due to the international tendency for gamma-irradiated food, it was concluded that the establishment of detailed detection methods for each food group is not efficient for the enactment and enforcement of related regulations. For this reason, in order to establish the standard detection method, a detection system for gamma-irradiated food suitable for domestic operation was studied using comparative analysis of domestic and foreign research data classified by items and methods and European Standard as a reference. According to the comparative analyses of domestic and foreign research data and regulations of detection for gamma-irradiated food, it was concluded to be desirable that the optimal detection method should be decided after principal detection tests such as physical, chemical, and biological detection methods are established as standard methods and that the specific descriptions such as pre-treatment of raw materials, test methods, and the evaluation of results should be separately prescribed.

TMTI Task 1.6 Genetic Engineering Methods and Detection

Energy Technology Data Exchange (ETDEWEB)

Slezak, T; Lenhoff, R; Allen, J; Borucki, M; Vitalis, E; Gardner, S

2009-12-04

A large number of GE techniques can be adapted from other microorganisms to biothreat bacteria and viruses. Detection of GE in a microorganism increases in difficulty as the size of the genetic change decreases. In addition to the size of the engineered change, the consensus genomic sequence of the microorganism can impact the difficulty of detecting an engineered change in genomes that are highly variable from strain to strain. This problem will require comprehensive databases of whole genome sequences for more genetically variable biothreat bacteria and viruses. Preliminary work with microarrays for detecting synthetic elements or virulence genes and analytic bioinformatic approaches for whole genome sequence comparison to detect genetic engineering show promise for attacking this difficult problem but a large amount of future work remains.

A Robust Method for Detecting Parking Areas in Both Indoor and Outdoor Environments

Directory of Open Access Journals (Sweden)

Wenhao Zong

2018-06-01

Full Text Available Although an automatic parking system has been installed in many vehicles recently, it is still hard for the system to confirm by itself whether a vacant parking area truly exists or not. In this paper, we introduced a robust vision-based vacancy parking area detecting method for both indoor and outdoor environments. The main contribution of this paper is given as follows. First, an automatic image stitching method is proposed. Secondly, the problem of environment illuminating change and line color difference is considered and solved. Thirdly, the proposed algorithm is insensitive to the shadow and scene diversity, which means the detecting result satisfies most of the environment. Finally, a vehicle model is considered for tracking and reconfirming the detecting results to eliminate most of the false positives.

Total least squares for anomalous change detection

Science.gov (United States)

Theiler, James; Matsekh, Anna M.

2010-04-01

A family of subtraction-based anomalous change detection algorithms is derived from a total least squares (TLSQ) framework. This provides an alternative to the well-known chronochrome algorithm, which is derived from ordinary least squares. In both cases, the most anomalous changes are identified with the pixels that exhibit the largest residuals with respect to the regression of the two images against each other. The family of TLSQbased anomalous change detectors is shown to be equivalent to the subspace RX formulation for straight anomaly detection, but applied to the stacked space. However, this family is not invariant to linear coordinate transforms. On the other hand, whitened TLSQ is coordinate invariant, and special cases of it are equivalent to canonical correlation analysis and optimized covariance equalization. What whitened TLSQ offers is a generalization of these algorithms with the potential for better performance.

A new method of small target detection based on neural network

Science.gov (United States)

Hu, Jing; Hu, Yongli; Lu, Xinxin

2018-02-01

The detection and tracking of moving dim target in infrared image have been an research hotspot for many years. The target in each frame of images only occupies several pixels without any shape and structure information. Moreover, infrared small target is often submerged in complicated background with low signal-to-clutter ratio, making the detection very difficult. Different backgrounds exhibit different statistical properties, making it becomes extremely complex to detect the target. If the threshold segmentation is not reasonable, there may be more noise points in the final detection, which is unfavorable for the detection of the trajectory of the target. Single-frame target detection may not be able to obtain the desired target and cause high false alarm rate. We believe the combination of suspicious target detection spatially in each frame and temporal association for target tracking will increase reliability of tracking dim target. The detection of dim target is mainly divided into two parts, In the first part, we adopt bilateral filtering method in background suppression, after the threshold segmentation, the suspicious target in each frame are extracted, then we use LSTM(long short term memory) neural network to predict coordinates of target of the next frame. It is a brand-new method base on the movement characteristic of the target in sequence images which could respond to the changes in the relationship between past and future values of the values. Simulation results demonstrate proposed algorithm can effectively predict the trajectory of the moving small target and work efficiently and robustly with low false alarm.

Image Processing Methods Usable for Object Detection on the Chessboard

Directory of Open Access Journals (Sweden)

Beran Ladislav

2016-01-01

Full Text Available Image segmentation and object detection is challenging problem in many research. Although many algorithms for image segmentation have been invented, there is no simple algorithm for image segmentation and object detection. Our research is based on combination of several methods for object detection. The first method suitable for image segmentation and object detection is colour detection. This method is very simply, but there is problem with different colours. For this method it is necessary to have precisely determined colour of segmented object before all calculations. In many cases it is necessary to determine this colour manually. Alternative simply method is method based on background removal. This method is based on difference between reference image and detected image. In this paper several methods suitable for object detection are described. Thisresearch is focused on coloured object detection on chessboard. The results from this research with fusion of neural networks for user-computer game checkers will be applied.

Updating the 2001 National Land Cover Database Impervious Surface Products to 2006 using Landsat imagery change detection methods

Science.gov (United States)

Xian, George; Homer, Collin G.

2010-01-01

A prototype method was developed to update the U.S. Geological Survey (USGS) National Land Cover Database (NLCD) 2001 to a nominal date of 2006. NLCD 2001 is widely used as a baseline for national land cover and impervious cover conditions. To enable the updating of this database in an optimal manner, methods are designed to be accomplished by individual Landsat scene. Using conservative change thresholds based on land cover classes, areas of change and no-change were segregated from change vectors calculated from normalized Landsat scenes from 2001 and 2006. By sampling from NLCD 2001 impervious surface in unchanged areas, impervious surface predictions were estimated for changed areas within an urban extent defined by a companion land cover classification. Methods were developed and tested for national application across six study sites containing a variety of urban impervious surface. Results show the vast majority of impervious surface change associated with urban development was captured, with overall RMSE from 6.86 to 13.12% for these areas. Changes of urban development density were also evaluated by characterizing the categories of change by percentile for impervious surface. This prototype method provides a relatively low cost, flexible approach to generate updated impervious surface using NLCD 2001 as the baseline.

Detection methods for irradiated food

International Nuclear Information System (INIS)

Stevenson, M.H.

1993-01-01

The plenary lecture gives a brief historical review of the development of methods for the detection of food irradiation and defines the demands on such methods. The methods described in detail are as follows: 1) Physical methods: As examples of luminescence methods, thermoluminescence and chermoluminescence are mentioned; ESR spectroscopy is discussed in detail by means of individual examples (crustaceans, frutis and vegetables, spieces and herbs, nuts). 2) Chemical methods: Examples given for these are methods that make use of alterations in lipids through radiation (formation of long-chain hydrocarbons, formation of 2-alkyl butanones), respectively radiation-induced alterations in the DNA. 3) Microbiological methods. An extensive bibliography is appended. (VHE) [de

TOWARDS CHANGE DETECTION IN URBAN AREA BY SAR INTERFEROMETRY AND RADARGRAMMETRY

Directory of Open Access Journals (Sweden)

C. Dubois

2013-04-01

Full Text Available Change detection in urban area is an active topic in remote sensing. However, well-dealt subject in optical remote sensing, this research topic is still at an early stage and needs deeper investigations and improvement in what concerns SAR and InSAR remote sensing. Due to their weather and daylight-independency, SAR sensors allow an all-time observation of the earth. This is determining in cases where rapid change detection is required after a natural – or technological – disaster. Due to the high resolution that can be achieved, the new generation of space-borne radar sensors opens up new perspectives for analysing buildings in urban areas. Moreover, due to their short revisiting cycle, they give rise to monitoring and change detection applications. In this paper, we present a concept for change detection in urban area at building level, relying only on SAR- and InSAR data. In this approach, interferometric and radargrammetric SAR data are merged in order to detect changes. Here, we present the overall workflow, the test area, the required data as well as first findings on the best-suited stereo-configurations for change detection.

Detecting changes during pregnancy with Raman spectroscopy

Science.gov (United States)

Vargis, Elizabeth; Robertson, Kesha; Al-Hendy, Ayman; Reese, Jeff; Mahadevan-Jansen, Anita

2010-02-01

Preterm labor is the second leading cause of neonatal mortality and leads to a myriad of complications like delayed development and cerebral palsy. Currently, there is no way to accurately predict preterm labor, making its prevention and treatment virtually impossible. While there are some at-risk patients, over half of all preterm births do not fall into any high-risk category. This study seeks to predict and prevent preterm labor by using Raman spectroscopy to detect changes in the cervix during pregnancy. Since Raman spectroscopy has been used to detect cancers in vivo in organs like the cervix and skin, it follows that spectra will change over the course of pregnancy. Previous studies have shown that fluorescence decreased during pregnancy and increased during post-partum exams to pre-pregnancy levels. We believe significant changes will occur in the Raman spectra obtained during the course of pregnancy. In this study, Raman spectra from the cervix of pregnant mice and women will be acquired. Specific changes that occur due to cervical softening or changes in hormonal levels will be observed to understand the likelihood that a female mouse or a woman will enter labor.

A New Approach to Performing Bundle Adjustment for Time Series UAV Images 3D Building Change Detection

Directory of Open Access Journals (Sweden)

Wenzhuo Li

2017-06-01

Full Text Available Successful change detection in multi-temporal images relies on high spatial co-registration accuracy. However, co-registration accuracy alone cannot meet the needs of change detection when using several ground control points to separately geo-reference multi-temporal images from unmanned aerial vehicles (UAVs. This letter reports on a new approach to perform bundle adjustment—named united bundle adjustment (UBA—to solve this co-registration problem for change detection in multi-temporal UAV images. In UBA, multi-temporal UAV images are matched with each other to construct a unified tie point net. One single bundle adjustment process is performed on the unified tie point net, placing every image into the same coordinate system and thus automatically accomplishing spatial co-registration. We then perform change detection using both orthophotos and three-dimensional height information derived from dense image matching techniques. Experimental results show that UBA co-registration accuracy is higher than the accuracy of commonly-used approaches for multi-temporal UAV images. Our proposed preprocessing method extends the capacities of consumer-level UAVs so they can eventually meet the growing need for automatic building change detection and dynamic monitoring using only RGB band images.

In-vitro studies of change in edge detection with changes in bone density

International Nuclear Information System (INIS)

Pocock, N.; Noakes, K.; Griffiths, M.

1999-01-01

Full text: Dual energy X-ray absorptiometry (DXA) requires edge detection software to identify the skeletal regions for quantitation of bone mineral density (BMD) and bone mineral content (BMC). As bone mass decreases, the detection of bone edges becomes more difficult and this potentially could cause errors in DXA estimations of areal BMD or BMC. To address this issue, we have used an in-vitro model to study the effects of 'bone loss' on calculated bone area, BMD and BMC. Multiple vertebral phantoms, of equal cross-sectional area but incrementally decreased areal BMD, were constructed using calcium sulphate hemihydrate. The weight of each phantom vertebra, measured accurately using an electronic balance, was used as an index of its true 'bone mass equivalent' (BME). The phantoms were scanned and analysed in the lumbar spine mode using a Lunar DPX-L (L) and Hologic QDR-1000 (H). The changes in BME were compared to changes in measured area, BMC and areal BMD. The results demonstrate that, in an in-vitro model, as bone mass decreases, measured bone area and consequently BMC will decrease as the edge detection algorithms have greater difficulty in detecting the true edges. In conclusion, in an in-vitro model, the DXA edge detection algorithms will underestimate bone area as bone mass decreases. This has potential implications for monitoring changes in bone mass in vivo

Methods for assessment of climate variability and climate changes in different time-space scales

International Nuclear Information System (INIS)

Lobanov, V.; Lobanova, H.

2004-01-01

Main problem of hydrology and design support for water projects connects with modern climate change and its impact on hydrological characteristics as observed as well as designed. There are three main stages of this problem: - how to extract a climate variability and climate change from complex hydrological records; - how to assess the contribution of climate change and its significance for the point and area; - how to use the detected climate change for computation of design hydrological characteristics. Design hydrological characteristic is the main generalized information, which is used for water management and design support. First step of a research is a choice of hydrological characteristic, which can be as a traditional one (annual runoff for assessment of water resources, maxima, minima runoff, etc) as well as a new one, which characterizes an intra-annual function or intra-annual runoff distribution. For this aim a linear model has been developed which has two coefficients connected with an amplitude and level (initial conditions) of seasonal function and one parameter, which characterizes an intensity of synoptic and macro-synoptic fluctuations inside a year. Effective statistical methods have been developed for a separation of climate variability and climate change and extraction of homogeneous components of three time scales from observed long-term time series: intra annual, decadal and centural. The first two are connected with climate variability and the last (centural) with climate change. Efficiency of new methods of decomposition and smoothing has been estimated by stochastic modeling and well as on the synthetic examples. For an assessment of contribution and statistical significance of modern climate change components statistical criteria and methods have been used. Next step has been connected with a generalization of the results of detected climate changes over the area and spatial modeling. For determination of homogeneous region with the same

Detecting Brain State Changes via Fiber-Centered Functional Connectivity Analysis

Science.gov (United States)

Li, Xiang; Lim, Chulwoo; Li, Kaiming; Guo, Lei; Liu, Tianming

2013-01-01

Diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI) have been widely used to study structural and functional brain connectivity in recent years. A common assumption used in many previous functional brain connectivity studies is the temporal stationarity. However, accumulating literature evidence has suggested that functional brain connectivity is under temporal dynamic changes in different time scales. In this paper, a novel and intuitive approach is proposed to model and detect dynamic changes of functional brain states based on multimodal fMRI/DTI data. The basic idea is that functional connectivity patterns of all fiber-connected cortical voxels are concatenated into a descriptive functional feature vector to represent the brain’s state, and the temporal change points of brain states are decided by detecting the abrupt changes of the functional vector patterns via the sliding window approach. Our extensive experimental results have shown that meaningful brain state change points can be detected in task-based fMRI/DTI, resting state fMRI/DTI, and natural stimulus fMRI/DTI data sets. Particularly, the detected change points of functional brain states in task-based fMRI corresponded well to the external stimulus paradigm administered to the participating subjects, thus partially validating the proposed brain state change detection approach. The work in this paper provides novel perspective on the dynamic behaviors of functional brain connectivity and offers a starting point for future elucidation of the complex patterns of functional brain interactions and dynamics. PMID:22941508

Color-Changing Sensors for Detecting the Presence of Hypergolic Fuels

Science.gov (United States)

Roberson, Luke; Captain, Janine; Santiago-Maldonado, Edgardo; Starr, Stanley; DeVor, Robert

2013-01-01

Hypergolic fuel sensors were designed to incorporate novel chemochromic pigments into substrates for use in various methods of leak detection. There are several embodiments to this invention that would provide specific visual indication of hypergols used during and after transfer. The ability to incorporate these pigments into various polymer matrices provides a unique opportunity to manufacture nearly any type of sensor shape that is required. The vibrant color change from yellow to black instantaneously shows the worker the presence of hypergols in the area.

Change detection in urban and rural driving scenes: Effects of target type and safety relevance on change blindness.

Science.gov (United States)

Beanland, Vanessa; Filtness, Ashleigh J; Jeans, Rhiannon

2017-03-01

The ability to detect changes is crucial for safe driving. Previous research has demonstrated that drivers often experience change blindness, which refers to failed or delayed change detection. The current study explored how susceptibility to change blindness varies as a function of the driving environment, type of object changed, and safety relevance of the change. Twenty-six fully-licenced drivers completed a driving-related change detection task. Changes occurred to seven target objects (road signs, cars, motorcycles, traffic lights, pedestrians, animals, or roadside trees) across two environments (urban or rural). The contextual safety relevance of the change was systematically manipulated within each object category, ranging from high safety relevance (i.e., requiring a response by the driver) to low safety relevance (i.e., requiring no response). When viewing rural scenes, compared with urban scenes, participants were significantly faster and more accurate at detecting changes, and were less susceptible to "looked-but-failed-to-see" errors. Interestingly, safety relevance of the change differentially affected performance in urban and rural environments. In urban scenes, participants were more efficient at detecting changes with higher safety relevance, whereas in rural scenes the effect of safety relevance has marginal to no effect on change detection. Finally, even after accounting for safety relevance, change blindness varied significantly between target types. Overall the results suggest that drivers are less susceptible to change blindness for objects that are likely to change or move (e.g., traffic lights vs. road signs), and for moving objects that pose greater danger (e.g., wild animals vs. pedestrians). Copyright © 2017 Elsevier Ltd. All rights reserved.

Enhanced change detection index for disaster response, recovery assessment and monitoring of buildings and critical facilities-A case study for Muzzaffarabad, Pakistan

Science.gov (United States)

de Alwis Pitts, Dilkushi A.; So, Emily

2017-12-01

The availability of Very High Resolution (VHR) optical sensors and a growing image archive that is frequently updated, allows the use of change detection in post-disaster recovery and monitoring for robust and rapid results. The proposed semi-automated GIS object-based method uses readily available pre-disaster GIS data and adds existing knowledge into the processing to enhance change detection. It also allows targeting specific types of changes pertaining to similar man-made objects such as buildings and critical facilities. The change detection method is based on pre/post normalized index, gradient of intensity, texture and edge similarity filters within the object and a set of training data. More emphasis is put on the building edges to capture the structural damage in quantifying change after disaster. Once the change is quantified, based on training data, the method can be used automatically to detect change in order to observe recovery over time in potentially large areas. Analysis over time can also contribute to obtaining a full picture of the recovery and development after disaster, thereby giving managers a better understanding of productive management and recovery practices. The recovery and monitoring can be analyzed using the index in zones extending from to epicentre of disaster or administrative boundaries over time.

A new relative radiometric consistency processing method for change detection based on wavelet transform and a low-pass filter

Institute of Scientific and Technical Information of China (English)

无

2010-01-01

The research purpose of this paper is to show the limitations of the existing radiometric normalization approaches and their disadvantages in change detection of artificial objects by comparing the existing approaches,on the basis of which a preprocessing approach to radiometric consistency,based on wavelet transform and a spatial low-pass filter,has been devised.This approach first separates the high frequency information and low frequency information by wavelet transform.Then,the processing of relative radiometric consistency based on a low-pass filter is conducted on the low frequency parts.After processing,an inverse wavelet transform is conducted to obtain the results image.The experimental results show that this approach can substantially reduce the influence on change detection of linear or nonlinear radiometric differences in multi-temporal images.

Novel Method For Low-Rate Ddos Attack Detection

Science.gov (United States)

Chistokhodova, A. A.; Sidorov, I. D.

2018-05-01

The relevance of the work is associated with an increasing number of advanced types of DDoS attacks, in particular, low-rate HTTP-flood. Last year, the power and complexity of such attacks increased significantly. The article is devoted to the analysis of DDoS attacks detecting methods and their modifications with the purpose of increasing the accuracy of DDoS attack detection. The article details low-rate attacks features in comparison with conventional DDoS attacks. During the analysis, significant shortcomings of the available method for detecting low-rate DDoS attacks were found. Thus, the result of the study is an informal description of a new method for detecting low-rate denial-of-service attacks. The architecture of the stand for approbation of the method is developed. At the current stage of the study, it is possible to improve the efficiency of an already existing method by using a classifier with memory, as well as additional information.

Automatic detection of health changes using statistical process control techniques on measured transfer times of elderly.

Science.gov (United States)

Baldewijns, Greet; Luca, Stijn; Nagels, William; Vanrumste, Bart; Croonenborghs, Tom

2015-01-01

It has been shown that gait speed and transfer times are good measures of functional ability in elderly. However, data currently acquired by systems that measure either gait speed or transfer times in the homes of elderly people require manual reviewing by healthcare workers. This reviewing process is time-consuming. To alleviate this burden, this paper proposes the use of statistical process control methods to automatically detect both positive and negative changes in transfer times. Three SPC techniques: tabular CUSUM, standardized CUSUM and EWMA, known for their ability to detect small shifts in the data, are evaluated on simulated transfer times. This analysis shows that EWMA is the best-suited method with a detection accuracy of 82% and an average detection time of 9.64 days.

Self-adaptive change detection in streaming data with non-stationary distribution

KAUST Repository

Zhang, Xiangliang

2010-01-01

Non-stationary distribution, in which the data distribution evolves over time, is a common issue in many application fields, e.g., intrusion detection and grid computing. Detecting the changes in massive streaming data with a non-stationary distribution helps to alarm the anomalies, to clean the noises, and to report the new patterns. In this paper, we employ a novel approach for detecting changes in streaming data with the purpose of improving the quality of modeling the data streams. Through observing the outliers, this approach of change detection uses a weighted standard deviation to monitor the evolution of the distribution of data streams. A cumulative statistical test, Page-Hinkley, is employed to collect the evidence of changes in distribution. The parameter used for reporting the changes is self-adaptively adjusted according to the distribution of data streams, rather than set by a fixed empirical value. The self-adaptability of the novel approach enhances the effectiveness of modeling data streams by timely catching the changes of distributions. We validated the approach on an online clustering framework with a benchmark KDDcup 1999 intrusion detection data set as well as with a real-world grid data set. The validation results demonstrate its better performance on achieving higher accuracy and lower percentage of outliers comparing to the other change detection approaches. © 2010 Springer-Verlag.

A Novel Unscheduled Islanding Detection Method for Microgrid

Directory of Open Access Journals (Sweden)

Li Hui

2018-01-01

Full Text Available Microgrid with its intelligent and flexible control characteristics conform to the trend of sustainable development of electricity, and when the microgrid in the unplanned island state, the successful detection of the island is a prerequisite, energy storage inverter as the key equipment in the microgrid system, island protection is one of the necessary functions. In this paper, an improved islanding detection method based on active frequency drift and q-axis reactive power perturbation is proposed. The method has the advantages of faster detection speed and minor influence on power quality, which makes the energy storage inverter with better output power quality when it works on grid-connected state, and can be detected the islanding state quickly from grid-connected mode to islanded mode. Finally, the validity and superiority of the improved island detection method are verified by simulation experiments.

Method of detecting leakage in nuclear reactor containment vessel

International Nuclear Information System (INIS)

Koba, Akitoshi; Goto, Seiichiro.

1974-01-01

Object: To permit accurate and prompt detection of leakage of a radioactive substance. Structure: The rate of change of such factors as radiation dose, temperature and pressure in the containment vessel, and each detected rate of change is compared with a reference value. The running cycle of the condensed drain exhausting pump in a drain collecting tank within a predetermined period is detected, and it is also compared with a reference value. These comparisons determine the absence or presence of leakage. (Kamimura, M.)

Detecting Android Malwares with High-Efficient Hybrid Analyzing Methods

Directory of Open Access Journals (Sweden)

Yu Liu

2018-01-01

Full Text Available In order to tackle the security issues caused by malwares of Android OS, we proposed a high-efficient hybrid-detecting scheme for Android malwares. Our scheme employed different analyzing methods (static and dynamic methods to construct a flexible detecting scheme. In this paper, we proposed some detecting techniques such as Com+ feature based on traditional Permission and API call features to improve the performance of static detection. The collapsing issue of traditional function call graph-based malware detection was also avoided, as we adopted feature selection and clustering method to unify function call graph features of various dimensions into same dimension. In order to verify the performance of our scheme, we built an open-access malware dataset in our experiments. The experimental results showed that the suggested scheme achieved high malware-detecting accuracy, and the scheme could be used to establish Android malware-detecting cloud services, which can automatically adopt high-efficiency analyzing methods according to the properties of the Android applications.

Removing Parallax-Induced False Changes in Change Detection

Science.gov (United States)

2014-03-27

Teller , “Equation of state calculations by fast computing machines ,” The Journal of Chemical Physics, vol. 21, no. 6, pp. 1087–1092, 1953. [89] J. D’Errico...especially in the case of HS data, due to its hundreds of spectral channels. Therefore, there is a strong need for methodologies that enable automated ...researchers for many years due in large part to the number of applications across diverse disciplines. Automated image change detection (CD) is the

Proactive interference does not meaningfully distort visual working memory capacity estimates in the canonical change detection task

Directory of Open Access Journals (Sweden)

Po-Han eLin

2012-02-01

Full Text Available The change detection task has become a standard method for estimating the storage capacity of visual working memory. Most researchers assume that this task isolates the properties of an active short-term storage system that can be dissociated from long-term memory systems. However, long-term memory storage may influence performance on this task. In particular, memory traces from previous trials may create proactive interference that sometimes leads to errors, thereby reducing estimated capacity. Consequently, the capacity of visual working memory may be higher than is usually thought, and correlations between capacity and other measures of cognition may reflect individual differences in proactive interference rather than individual differences in the capacity of the short-term storage system. Indeed, previous research has shown that change detection performance can be influenced by proactive interference under some conditions. The purpose of the present study was to determine whether the canonical version of the change detection task—in which the to-be-remembered information consists of simple, briefly presented features—is influenced by proactive interference. Two experiments were conducted using methods that ordinarily produce substantial evidence of proactive interference, but no proactive interference was observed. Thus, the canonical version of the change detection task can be used to assess visual working memory capacity with no meaningful influence of proactive interference.

Proactive interference does not meaningfully distort visual working memory capacity estimates in the canonical change detection task.

Science.gov (United States)

Lin, Po-Han; Luck, Steven J

2012-01-01

The change detection task has become a standard method for estimating the storage capacity of visual working memory. Most researchers assume that this task isolates the properties of an active short-term storage system that can be dissociated from long-term memory systems. However, long-term memory storage may influence performance on this task. In particular, memory traces from previous trials may create proactive interference that sometimes leads to errors, thereby reducing estimated capacity. Consequently, the capacity of visual working memory may be higher than is usually thought, and correlations between capacity and other measures of cognition may reflect individual differences in proactive interference rather than individual differences in the capacity of the short-term storage system. Indeed, previous research has shown that change detection performance can be influenced by proactive interference under some conditions. The purpose of the present study was to determine whether the canonical version of the change detection task - in which the to-be-remembered information consists of simple, briefly presented features - is influenced by proactive interference. Two experiments were conducted using methods that ordinarily produce substantial evidence of proactive interference, but no proactive interference was observed. Thus, the canonical version of the change detection task can be used to assess visual working memory capacity with no meaningful influence of proactive interference.

Individual tree crown modeling and change detection from airborne lidar data

NARCIS (Netherlands)

Xiao, W.; Xu, Sudan; Oude Elberink, S.J.; Vosselman, G.

2016-01-01

Light detection and ranging (lidar) provides a promising way of detecting changes of trees in three-dimensional (3-D) because laser beams can penetrate through the foliage and therefore provide full coverage of trees. The aim is to detect changes in trees in urban areas using multitemporal airborne

Cancer Detection and Diagnosis Methods - Annual Plan

Science.gov (United States)

Early cancer detection is a proven life-saving strategy. Learn about the research opportunities NCI supports, including liquid biopsies and other less-invasive methods, for detecting early cancers and precancerous growths.

Rapid-viability PCR method for detection of live, virulent Bacillus anthracis in environmental samples.

Science.gov (United States)

Létant, Sonia E; Murphy, Gloria A; Alfaro, Teneile M; Avila, Julie R; Kane, Staci R; Raber, Ellen; Bunt, Thomas M; Shah, Sanjiv R

2011-09-01

In the event of a biothreat agent release, hundreds of samples would need to be rapidly processed to characterize the extent of contamination and determine the efficacy of remediation activities. Current biological agent identification and viability determination methods are both labor- and time-intensive such that turnaround time for confirmed results is typically several days. In order to alleviate this issue, automated, high-throughput sample processing methods were developed in which real-time PCR analysis is conducted on samples before and after incubation. The method, referred to as rapid-viability (RV)-PCR, uses the change in cycle threshold after incubation to detect the presence of live organisms. In this article, we report a novel RV-PCR method for detection of live, virulent Bacillus anthracis, in which the incubation time was reduced from 14 h to 9 h, bringing the total turnaround time for results below 15 h. The method incorporates a magnetic bead-based DNA extraction and purification step prior to PCR analysis, as well as specific real-time PCR assays for the B. anthracis chromosome and pXO1 and pXO2 plasmids. A single laboratory verification of the optimized method applied to the detection of virulent B. anthracis in environmental samples was conducted and showed a detection level of 10 to 99 CFU/sample with both manual and automated RV-PCR methods in the presence of various challenges. Experiments exploring the relationship between the incubation time and the limit of detection suggest that the method could be further shortened by an additional 2 to 3 h for relatively clean samples.

An integration time adaptive control method for atmospheric composition detection of occultation

Science.gov (United States)

Ding, Lin; Hou, Shuai; Yu, Fei; Liu, Cheng; Li, Chao; Zhe, Lin

2018-01-01

When sun is used as the light source for atmospheric composition detection, it is necessary to image sun for accurate identification and stable tracking. In the course of 180 second of the occultation, the magnitude of sun light intensity through the atmosphere changes greatly. It is nearly 1100 times illumination change between the maximum atmospheric and the minimum atmospheric. And the process of light change is so severe that 2.9 times per second of light change can be reached. Therefore, it is difficult to control the integration time of sun image camera. In this paper, a novel adaptive integration time control method for occultation is presented. In this method, with the distribution of gray value in the image as the reference variable, and the concepts of speed integral PID control, the integration time adaptive control problem of high frequency imaging. The large dynamic range integration time automatic control in the occultation can be achieved.

LAND COVER CHANGE DETECTION BASED ON GENETICALLY FEATURE AELECTION AND IMAGE ALGEBRA USING HYPERION HYPERSPECTRAL IMAGERY

Directory of Open Access Journals (Sweden)

S. T. Seydi

2015-12-01

Full Text Available The Earth has always been under the influence of population growth and human activities. This process causes the changes in land use. Thus, for optimal management of the use of resources, it is necessary to be aware of these changes. Satellite remote sensing has several advantages for monitoring land use/cover resources, especially for large geographic areas. Change detection and attribution of cultivation area over time present additional challenges for correctly analyzing remote sensing imagery. In this regards, for better identifying change in multi temporal images we use hyperspectral images. Hyperspectral images due to high spectral resolution created special placed in many of field. Nevertheless, selecting suitable and adequate features/bands from this data is crucial for any analysis and especially for the change detection algorithms. This research aims to automatically feature selection for detect land use changes are introduced. In this study, the optimal band images using hyperspectral sensor using Hyperion hyperspectral images by using genetic algorithms and Ratio bands, we select the optimal band. In addition, the results reveal the superiority of the implemented method to extract change map with overall accuracy by a margin of nearly 79% using multi temporal hyperspectral imagery.

LANDSAT-8 OPERATIONAL LAND IMAGER CHANGE DETECTION ANALYSIS

Directory of Open Access Journals (Sweden)

W. Pervez

2017-05-01

Full Text Available This paper investigated the potential utility of Landsat-8 Operational Land Imager (OLI for change detection analysis and mapping application because of its superior technical design to previous Landsat series. The OLI SVM classified data was successfully classified with regard to all six test classes (i.e., bare land, built-up land, mixed trees, bushes, dam water and channel water. OLI support vector machine (SVM classified data for the four seasons (i.e., spring, autumn, winter, and summer was used to change detection results of six cases: (1 winter to spring which resulted reduction in dam water mapping and increases of bushes; (2 winter to summer which resulted reduction in dam water mapping and increase of vegetation; (3 winter to autumn which resulted increase in dam water mapping; (4 spring to summer which resulted reduction of vegetation and shallow water; (5 spring to autumn which resulted decrease of vegetation; and (6 summer to autumn which resulted increase of bushes and vegetation . OLI SVM classified data resulted higher overall accuracy and kappa coefficient and thus found suitable for change detection analysis.

Detection method of internal leakage from valve using acoustic method

International Nuclear Information System (INIS)

Kumagai, Horomichi

1990-01-01

The purpose of this study is to estimate the availability of acoustic method for detecting the internal leakage of valves at power plants. Experiments have been carried out on the characteristics of acoustic noise caused by the leak simulated flow. From the experimental results, the mechanism of the acoustic noisegenerated from flow, the relation between acoustic intensity and leak flow velocity, and the characteristics of the acoustic frequency spectrum were clarified. The acoustic method was applied to valves at site, and the background noises were measured in abnormal plant conditions. When the background level is higher than the acoustic signal, the difference between the background noise frequency spectrum and the acoustic signal spectrum provide a very useful leak detection method. (author)

Method of detecting failed fuels

International Nuclear Information System (INIS)

Ishizaki, Hideaki; Suzumura, Takeshi.

1982-01-01

Purpose: To enable the settlement of the temperature of an adequate filling high temperature pure water by detecting the outlet temperature of a high temperature pure water filling tube to a fuel assembly to control the heating of the pure water and detecting the failed fuel due to the sampling of the pure water. Method: A temperature sensor is provided at a water tube connected to a sipping cap for filling high temperature pure water to detect the temperature of the high temperature pure water at the outlet of the tube, and the temperature is confirmed by a temperature indicator. A heater is controlled on the basis of this confirmation, an adequate high temperature pure water is filled in the fuel assembly, and the pure water is replaced with coolant. Then, it is sampled to settle the adequate temperature of the high temperature coolant used for detecting the failure of the fuel assembly. As a result, the sipping effect does not decrease, and the failed fuel can be precisely detected. (Yoshihara, H.)

Trend Change Detection in NDVI Time Series: Effects of Inter-Annual Variability and Methodology

Science.gov (United States)

Forkel, Matthias; Carvalhais, Nuno; Verbesselt, Jan; Mahecha, Miguel D.; Neigh, Christopher S.R.; Reichstein, Markus

2013-01-01

Changing trends in ecosystem productivity can be quantified using satellite observations of Normalized Difference Vegetation Index (NDVI). However, the estimation of trends from NDVI time series differs substantially depending on analyzed satellite dataset, the corresponding spatiotemporal resolution, and the applied statistical method. Here we compare the performance of a wide range of trend estimation methods and demonstrate that performance decreases with increasing inter-annual variability in the NDVI time series. Trend slope estimates based on annual aggregated time series or based on a seasonal-trend model show better performances than methods that remove the seasonal cycle of the time series. A breakpoint detection analysis reveals that an overestimation of breakpoints in NDVI trends can result in wrong or even opposite trend estimates. Based on our results, we give practical recommendations for the application of trend methods on long-term NDVI time series. Particularly, we apply and compare different methods on NDVI time series in Alaska, where both greening and browning trends have been previously observed. Here, the multi-method uncertainty of NDVI trends is quantified through the application of the different trend estimation methods. Our results indicate that greening NDVI trends in Alaska are more spatially and temporally prevalent than browning trends. We also show that detected breakpoints in NDVI trends tend to coincide with large fires. Overall, our analyses demonstrate that seasonal trend methods need to be improved against inter-annual variability to quantify changing trends in ecosystem productivity with higher accuracy.