A ViSAR Shadow-Detection Algorithm Based on LRSD Combined Trajectory Region Extraction

Abstract

Shadow detection is a new method for video synthetic aperture radar moving target indication (ViSAR-GMTI). The shadow formed by the target occlusion will reflect its real position, preventing the defocusing or offset of the moving target from making it difficult to identify the target during imaging. To achieve high-precision shadow detection, this paper proposes a video SAR moving target shadow-detection algorithm based on low-rank sparse decomposition combined with trajectory area extraction. Based on the low-rank sparse decomposition (LRSD) model, the algorithm creates a new decomposition framework combined with total variation (TV) regularization and coherence suppression items to improve the decomposition effect, and a global constraint is constructed to suppress interference using feature operators. In addition, it cooperates with the double threshold trajectory segmentation and error trajectory elimination method to further improve the detection performance. Finally, an experiment was carried out based on the video SAR data released by Sandia National Laboratory (SNL); the results prove the effectiveness of the proposed method, and the detection performance of the method is proved by comparative experiments.