Spatial Fuzzy Clustering and Deep Auto-encoder for Unsupervised Change Detection in Synthetic Aperture Radar Images

Change detection in synthetic aperture radar (SAR) images is to detect the changes happening during a period of time in the same area, which has important application research value. In this paper, we propose a novel method based on spatial fuzzy clustering (SFCM) and deep auto-encoder for the change-detection of SAR. In this method, the difference image (DI) is generated by the log-ratio operator. Then, the spatial fuzzy clustering (SFCM) algorithm is used to analyze the DI. The spatial fuzzy clustering (SFCM) algorithm adds spatial information to the fuzzy cluster, which effectively reduces the influence of speckle noise. Finally, we choose appropriate samples to train the deep auto-encoder. Real data and theoretical analysis show the effectiveness and robustness of the proposed method.