A Siamese Network-Based Similarity Measure for Target in SAR Images

Synthetic Aperture Radar (SAR) has been widely applied due to its capability for continuous monitoring under all-weather and all-time conditions. Unlike optical images, targets in SAR images are highly sensitive to observation angles whose shapes and shadows change significantly under different viewing angles. Some current methods tackle the limited angle coverage problem in SAR targets through multi-view sample generation. However, for SAR image similarity assessment, existing techniques mainly concentrate on category and scene similarity, with insufficient attention to viewing angle similarity. This paper proposes a novel method for evaluating target viewing angle similarity in SAR images. Specifically, the method introduces a Siamese network to map SAR target features from high-dimensional space to a low-dimensional manifold. A contrastive loss function is constructed using Euclidean distance to measure the similarity of low-dimensional features of viewing angles. Additionally, a sample angle pairing method is designed for model training. Furthermore, a recognition module is incorporated into the model to assess the similarity of categories. Experiments based on the MSTAR dataset demonstrate the efficiency of the proposed method in evaluating SAR target viewing angle and category similarity.