Foreground feature manifold ranking method for SAR image change detection

There are two problems with the difference image analysis for the current SAR image change detection methods. Some of the changed areas in the connected area are easily misclassified as unchanged areas, and the central prior assumption cannot be well applied to detecting the changed regions located at the boundary of the SAR image. In order to avoid the above limitations, a method of manifold ranking based on superpixel segmentation and foreground features for change detection (MRSFCD) was designed. Firstly, the difference image was constructed by weighted fusion of single pixel and neighborhood logarithmic ratio operator, which can maintain consistency within the change areas and suppress noise interference. The difference image was then segmented by the superpixel model. After that, the improved undirected graph connection method of superpixels was proposed. The main idea is that superpixels on the boundary are not considered when connecting, and superpixel segmentation results and grayscale information are applied for three adjacencies. Finally, we do a dot product between the significance image by manifold ranking based on foreground features and the single-pixel logarithmic difference image, and the final binary change image is obtained by threshold segmentation. In this paper, three datasets of dual-phase images are tested. The results indicate that compared with other change detection algorithms, the proposed method can improve the accuracy of change detection effectively. © 2022 SinoMaps Press. All rights reserved.