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

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