High-Precision Moving Target Shadow Detection Algorithm for ViSAR Based on Information Geometry

Video synthetic aperture radar (ViSAR) attracts much attention in the field of moving target detection due to its high frame rate and high-resolution imaging capability. In ViSAR, the moving target's shadow does not offset and defocus, which is widely used as a feature for moving target detection. To address the shortcomings of high false and missing alarms in existing detection algorithms, this study proposes a high-precision moving target shadow detection algorithm for ViSAR based on information geometry. Firstly, the shadow enhancement and background suppression algorithm is used to enhance the saliency of moving targets' shadows in ViSAR images to reduce the false and missing alarms caused by complex background. Then, based on the information geometry theory, the enhanced ViSAR image is mapped to a Gaussian statistical manifold, and the Jensen–Shannon (JS) divergence matrix with pixel context information is extracted from this manifold. And the JS divergence matrix and the gray-scale information from the enhanced ViSAR image are used to construct the joint probability distribution function (JPDF). The 2D minimum cross-entropy principle is used to calculate the threshold to extract the regions of interest (ROIs). Finally, the final detection results are obtained by applying single-frame and multiple-frame false alarms suppression. The experimental results, based on real ViSAR dataset published by Sandia National Laboratories (SNL), verify the effectiveness of our proposed algorithm. Specifically, the Recall rate is 85.97%, the Precision is 96.22%, and the F1 score achieves 0.91.