Remote Sensing Applications and Decision Support

Change detection from synthetic aperture radar images based on neighborhood-based ratio and extreme learning machine

[+] Author Affiliations
Feng Gao, Junyu Dong, Cui Xie

Ocean University of China, Department of Computer Science and Technology, 238 Songling Road, Qingdao 266100, China

Bo Li, Qizhi Xu

Beihang University, School of Computer Science and Engineering, Beijing Key Laboratory of Digital Media, 37 Xueyuan Road, Beijing 100191, China

J. Appl. Remote Sens. 10(4), 046019 (Dec 01, 2016). doi:10.1117/1.JRS.10.046019
History: Received August 3, 2016; Accepted November 11, 2016
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Abstract.  Change detection is of high practical value to hazard assessment, crop growth monitoring, and urban sprawl detection. A synthetic aperture radar (SAR) image is the ideal information source for performing change detection since it is independent of atmospheric and sunlight conditions. Existing SAR image change detection methods usually generate a difference image (DI) first and use clustering methods to classify the pixels of DI into changed class and unchanged class. Some useful information may get lost in the DI generation process. This paper proposed an SAR image change detection method based on neighborhood-based ratio (NR) and extreme learning machine (ELM). NR operator is utilized for obtaining some interested pixels that have high probability of being changed or unchanged. Then, image patches centered at these pixels are generated, and ELM is employed to train a model by using these patches. Finally, pixels in both original SAR images are classified by the pretrained ELM model. The preclassification result and the ELM classification result are combined to form the final change map. The experimental results obtained on three real SAR image datasets and one simulated dataset show that the proposed method is robust to speckle noise and is effective to detect change information among multitemporal SAR images.

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© 2016 Society of Photo-Optical Instrumentation Engineers

Citation

Feng Gao ; Junyu Dong ; Bo Li ; Qizhi Xu and Cui Xie
"Change detection from synthetic aperture radar images based on neighborhood-based ratio and extreme learning machine", J. Appl. Remote Sens. 10(4), 046019 (Dec 01, 2016). ; http://dx.doi.org/10.1117/1.JRS.10.046019


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