High-Resolution SAR Change Detection Based on ROI and SPP Net

With the resolution increasing, the structure information becomes more and more abundant in Synthetic Aperture Radar (SAR) images. The speckle noise generated by the coherent imaging mechanism, has a great influence on the detection accuracy and detection difficulty accordingly in high-resolution SAR change detection. In this paper, a multivariate change detection framework based on non-subsampled contourlet transform (NSCT), deep belief networks (DBN), fuzzy c-means (FCM) clustering, and global-local spatial pyramid pooling (SPP) net is proposed. NSCT decomposes the image into multiple scales and DBN is used for extracting feature of the decomposed coefficient matrix. FCM converges the similarity matrix of the initial features by DBN into two classes as a pseudo-label for global-local SPP net training data. The global-local SPP net consists of a large-scale region of interest (ROI) SPP net and a small-scale change detection SPP net. The combination of ROI and the SPP net, as well as the fusion between different scales, weakens the interference of the unchanged information and effectively eliminates a large number of redundant information. The experimental results show that our proposed method can effectively remove speckle noise and improve the robustness of high-resolution SAR change detection.