融合相干/非相干信息的高分辨率SAR图像变化检测

该文运用Dempster-Shafer(D-S)证据理论融合高分辨率SAR影像的相干/非相干差异特征进行变化检测。首先使用简单线性迭代聚类(SLIC)分割算法完成多时相SAR影像联合多尺度分割。然后在各个分割尺度上提取适宜的强度差异特征及相干差异特征,通过Mean算子融合多尺度差异特征并得到多特征差异图。最后运用D-S证据理论完成多特征差异图融合得到变化检测结果。实验表明该方法可得到较为稳健的变化检测结果。 

Change Detection of High Resolution SAR Images by the Fusion of Coherent/Incoherent Information

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.