scholarly journals A Novel Active Contours Model for Environmental Change Detection from Multitemporal Synthetic Aperture Radar Images

2020 ◽  
Vol 12 (11) ◽  
pp. 1746
Author(s):  
Salman Ahmadi ◽  
Saeid Homayouni
Keyword(s):  
Synthetic Aperture Radar ◽  
Change Detection ◽  
Active Contours ◽  
Training Data ◽  
Synthetic Aperture ◽  
Data Sets ◽  
Difference Image ◽  
Proposed Model ◽  
The Difference ◽  
Aperture Radar

In this paper, we propose a novel approach based on the active contours model for change detection from synthetic aperture radar (SAR) images. In order to increase the accuracy of the proposed approach, a new operator was introduced to generate a difference image from the before and after change images. Then, a new model of active contours was developed for accurately detecting changed regions from the difference image. The proposed model extracts the changed areas as a target feature from the difference image based on training data from changed and unchanged regions. In this research, we used the Otsu histogram thresholding method to produce the training data automatically. In addition, the training data were updated in the process of minimizing the energy function of the model. To evaluate the accuracy of the model, we applied the proposed method to three benchmark SAR data sets. The proposed model obtains 84.65%, 87.07%, and 96.26% of the Kappa coefficient for Yellow River Estuary, Bern, and Ottawa sample data sets, respectively. These results demonstrated the effectiveness of the proposed approach compared to other methods. Another advantage of the proposed model is its high speed in comparison to the conventional methods.

scholarly journals Change Detection in Synthetic Aperture Radar Images Based on Fuzzy Active Contour Models and Genetic Algorithms

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
Jiao Shi ◽  
Jiaji Wu ◽  
Anand Paul ◽  
Licheng Jiao ◽  
Maoguo Gong
Keyword(s):  
Synthetic Aperture Radar ◽  
Change Detection ◽  
Active Contour ◽  
Active Contour Model ◽  
Synthetic Aperture ◽  
Difference Image ◽  
Radar Images ◽  
Contour Model ◽  
The Difference ◽  
Aperture Radar

This paper presents an unsupervised change detection approach for synthetic aperture radar images based on a fuzzy active contour model and a genetic algorithm. The aim is to partition the difference image which is generated from multitemporal satellite images into changed and unchanged regions. Fuzzy technique is an appropriate approach to analyze the difference image where regions are not always statistically homogeneous. Since interval type-2 fuzzy sets are well-suited for modeling various uncertainties in comparison to traditional fuzzy sets, they are combined with active contour methodology for properly modeling uncertainties in the difference image. The interval type-2 fuzzy active contour model is designed to provide preliminary analysis of the difference image by generating intermediate change detection masks. Each intermediate change detection mask has a cost value. A genetic algorithm is employed to find the final change detection mask with the minimum cost value by evolving the realization of intermediate change detection masks. Experimental results on real synthetic aperture radar images demonstrate that change detection results obtained by the improved fuzzy active contour model exhibits less error than previous approaches.

scholarly journals Data Fusion and Fuzzy Clustering on Ratio Images for Change Detection in Synthetic Aperture Radar Images

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Wenping Ma ◽  
Xiaoting Li ◽  
Yue Wu ◽  
Licheng Jiao ◽  
Dan Xing
Keyword(s):  
Synthetic Aperture Radar ◽  
Change Detection ◽  
Yellow River ◽  
River Estuary ◽  
Synthetic Aperture ◽  
Radar Images ◽  
Step Procedure ◽  
The Difference ◽  
Log Ratio ◽  
Aperture Radar

The unsupervised approach to change detection via synthetic aperture radar (SAR) images becomes more and more popular. The three-step procedure is the most widely used procedure, but it does not work well with the Yellow River Estuary dataset obtained by two synthetic aperture radars. The difference of the two radars in imaging techniques causes severe noise, which seriously affects the difference images generated by a single change detector in step two, producing the difference image. To deal with problem, we propose a change detector to fuse the log-ratio (LR) and the mean-ratio (MR) images by a context independent variable behavior (CIVB) operator and can utilize the complement information in two ratio images. In order to validate the effectiveness of the proposed change detector, the change detector will be compared with three other change detectors, namely, the log-ratio (LR), mean-ratio (MR), and the wavelet-fusion (WR) operator, to deal with three datasets with different characteristics. The four operators are applied not only in a widely used three-step procedure but also in a new approach. The experiments show that the false alarms and overall errors of change detection are greatly reduced, and the kappa and KCC are improved a lot. And its superiority can also be observed visually.

scholarly journals Brief Communication: Contrast-stretching- and histogram-smoothness-based synthetic aperture radar image enhancement for flood map generation

2015 ◽  
Vol 15 (2) ◽  
pp. 273-276 ◽  
Author(s):  
F. Nazir ◽  
M. M. Riaz ◽  
A. Ghafoor ◽  
F. Arif
Keyword(s):  
Synthetic Aperture Radar ◽  
Synthetic Aperture Radar Image ◽  
Radar Image ◽  
Synthetic Aperture ◽  
Data Sets ◽  
Difference Image ◽  
Map Generation ◽  
Histogram Smoothing ◽  
Contrast Stretching ◽  
Aperture Radar

Abstract. Synthetic-aperture-radar-image-based flood map generation is usually a challenging task (due to degraded contrast). A three-step approach (based on adaptive histogram clipping, histogram remapping and smoothing) is proposed for generation of a more visualized flood map image. The pre- and post-flood images are adaptively histogram equalized. The hidden details in difference image are enhanced using contrast-based enhancement and histogram smoothing. A fast-ready flood map is then generated using equalized pre-, post- and difference images. Results (evaluated using different data sets) show significance of the proposed technique.

AN INVARIANT SHAPE REPRESENTATION: INTERIOR ANGLE CHAIN

2007 ◽  
Vol 21 (03) ◽  
pp. 543-559 ◽  
Author(s):  
BIN WANG ◽  
YAN QIU CHEN
Keyword(s):  
Synthetic Aperture Radar ◽  
Shape Representation ◽  
Interior Angle ◽  
Synthetic Aperture ◽  
Radar Images ◽  
Planar Shape ◽  
The Difference ◽  
Representation Scheme ◽  
Invariant Shape ◽  
Aperture Radar

This paper proposes a novel shape representation scheme — Interior Angle Chain (IAC) — which is invariant to translation, rotation and scaling. The proposed method first approximates the contour of a planar shape with an equilateral polygon and then makes a representation using the polygon's interior angle chain. The difference between two shapes is measured by the distance between their IAC's. An algorithm to obtain equilateral polygon approximation and its associated IAC is proposed in this paper. The proposed shape representation scheme has been tested on two benchmarks and applied to lake recognition in SAR (Synthetic Aperture Radar) images. The results show that IAC is an effective shape representation scheme.

scholarly journals Application of Data Driven Optimization for Change Detection in Synthetic Aperture Radar Images

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 11426-11436
Author(s):  
Yangyang Li ◽  
Guangyuan Liu ◽  
Tiantian Li ◽  
Licheng Jiao ◽  
Gao Lu ◽  
...  
Keyword(s):  
Synthetic Aperture Radar ◽  
Change Detection ◽  
Data Driven ◽  
Synthetic Aperture ◽  
Radar Images ◽  
Aperture Radar
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