Change Detection of High Resolution SAR Images by the Fusion of Coherent/Incoherent Information
Yang Xiang-li① Xu De-wei① Huang Ping-ping② Yang Wen①
①(School of Electronic Information, Wuhan University, Wuhan 430072, China) ②(Radar Research Institute, Inner Mongolia University of Technology, Hohhot 010051, China)
Aiming at detecting the change regions of high resolution Synthetic Aperture Radar (SAR) images, we propose to use the Dempster-Shafer (D-S) evidence theory to fuse coherent/incoherent features from sensors that form an integral part of the system. First, we use the Simple Linear Iterative Clustering (SLIC) segmentation algorithm to implement multi-scale joint segmentation for multi-temporal SAR images. Second, we extract multiple intensity and coherence difference features on each segment level by SLIC using mean operator to complete the fusion of multi-scale features to get the multi-feature difference mapped by a ratio operator. Finally, we fuse the multi-feature difference maps to get the final change detection result using the D-S evidence theory. The experimental results in our study prove the effectiveness of our proposed computational algorithm.
杨祥立, 徐德伟, 黄平平, 杨 文. 融合相干/非相干信息的高分辨率SAR图像变化检测[J]. 雷达学报, 2015, 4(5): 582-590.
Yang Xiang-li, Xu De-wei, Huang Ping-ping, Yang Wen. Change Detection of High Resolution SAR Images by the Fusion of Coherent/Incoherent Information. JOURNAL OF RADARS, 2015, 4(5): 582-590.
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2015, Vol. 4 
