Synthetic Aperture Radar (SAR) is highly valuable for target detection due to its all-weather, day-night operational capability and certain ground penetration potential. However, traditional SAR target detection methods often directly adapt algorithms designed for optical imagery, simplistically treating SAR data as grayscale images. This approach overlooks SAR’s unique physical nature, failing to account for key factors such as backscatter variations from different polarizations, target representation changes across resolutions, and detection threshold shifts due to clutter background heterogeneity. Consequently, these limitations lead to insufficient cross-polarization adaptability, feature masking, and degraded recognition accuracy due to clutter interference. To address these challenges, this paper systematically reviews recent research advances in SAR target detection, focusing on physical constraints including polarization characteristics, scattering mechanisms, signal-domain properties, and resolution effects. Finally, it outlines promising research directions to guide future developments in physics-aware SAR target detection.

Vision-Language Models (VLMs) demonstrate significant potential for remote sensing interpretation through multimodal fusion and semantic representation of imagery. However, their adaptation to Synthetic Aperture Radar (SAR) remains challenging due to fundamental differences in imaging mechanisms and physical properties compared to optical remote sensing. SAREval, the first comprehensive benchmark specifically designed for SAR image understanding, incorporates SAR-specific characteristics, including scattering mechanisms and polarization features, through a hierarchical framework spanning perception, reasoning, and robustness capabilities. It encompasses 20 tasks from image classification to physical-attribute inference with over 10,000 high-quality image–text pairs. Extensive experiments conducted on 11 mainstream VLMs reveal substantial limitations in SAR image interpretation. Models achieve merely 25.35% accuracy in fine-grained ship classification tasks and demonstrate significant difficulties in establishing mappings between visual features and physical parameters. Furthermore, certain models exhibit unexpected performance improvements under certain noise conditions that challenge conventional robustness understanding. SAREval establishes an essential foundation for developing and evaluating VLMs in SAR image interpretation, providing standardized assessment protocols and quality-controlled annotations for cross-modal remote sensing research.