Moving target detection based on Circular Video SAR

Video Synthetic Aperture Radar (Video-SAR) achieves radar video observation by continuously monitoring a certain area to form continuous image data of the target, and it plays an important role in civil and military fields. The SAR image of the moving target is defocused and deviates from its true position, but in its real position, a shadow area similar to the target size is generated. In this paper, the shadow area is used to detect the moving target. Firstly, the image sequence is de-noised by wavelet analysis. Then the maximum threshold segmentation of Tsallis gray entropy is performed. The SIFT and FAST algorithms are used to detect and match the corners of each frame. At the same time, the RANSAC algorithm is used to optimize the pairing results to complete the image registration. Finally, the moving target is extracted by the three-frame difference method, and the result is marked to the corresponding position of the original image. Based on the Video SAR segment published by Sandia Labs in the United States, the detection of the largest moving vehicle in the video is realized, and the effectiveness and anti-interference of the proposed algorithm is verified.