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

Xiao Wang; Feng Gao; Junyu Dong; Qiang Qi

doi:10.1117/12.2305510

10 April 2018 Change detection for synthetic aperture radar images based on pattern and intensity distinctiveness analysis

Xiao Wang, Feng Gao, Junyu Dong, Qiang Qi

Author Affiliations +

Proceedings Volume 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017); 1061548 (2018) https://doi.org/10.1117/12.2305510
Event: Ninth International Conference on Graphic and Image Processing, 2017, Qingdao, China

Abstract

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.

Citation Download Citation

Xiao Wang, Feng Gao, Junyu Dong, and Qiang Qi "Change detection for synthetic aperture radar images based on pattern and intensity distinctiveness analysis", Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 1061548 (10 April 2018); https://doi.org/10.1117/12.2305510

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