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

Feng Gao; Junyu Dong; Bo Li; Qizhi Xu; Cui Xie

doi:10.1117/1.JRS.10.046019

1 December 2016 Change detection from synthetic aperture radar images based on neighborhood-based ratio and extreme learning machine

Feng Gao, Junyu Dong, Bo Li, Qizhi Xu, Cui Xie

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 10, Issue 4, 046019 (December 2016). https://doi.org/10.1117/1.JRS.10.046019

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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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," Journal of Applied Remote Sensing 10(4), 046019 (1 December 2016). https://doi.org/10.1117/1.JRS.10.046019

Published: 1 December 2016

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