Azimuth Low-Resolution SAR Ship Detection via Directional Encoding and Boundary-Aware Loss

Abstract

Wide-area maritime surveillance typically requires a tradeoff between processing efficiency and imaging resolution. The 2-D asymmetric resolution mode (2-D-ARM) produces azimuth low-resolution imagery by compressing the effective azimuth Doppler bandwidth, thereby enabling efficient coarse search. However, this mode also introduces strong anisotropy: ship targets occupy only a few pixels along the azimuth direction and appear as range-elongated ultrathin stripe-like responses with scarce visual details, and high-IoU localization becomes unusually sensitive to pixel-level boundary deviations. For this problem, we construct ALR-SHIP with 367 offshore scenes and four paired azimuth-resolution subsets, each containing 2978 patches and 4032 ship instances. Furthermore, we propose directional statistics-guided input encoding (DSGIE), which incorporates azimuth-gradient and structural anisotropy cues while preserving main magnitude-channel statistics to enhance ultrathin target learnability. We also design an anisotropic boundary-aware loss (ABAL), which adaptively constrains azimuth-boundary regression to enhance high-IoU localization. Experiments show consistent high-IoU gains across azimuth-resolution settings.