Scattering–Semantic Collaborative Learning via Asymmetric Dual-Branch DINO Network for Inshore SAR Ship Detection

Inshore synthetic aperture radar (SAR) ship detection remains challenging because strong coastal clutter, speckle noise, and degraded target responses frequently result in false alarms and missed detections of small vessels. Moreover, local scattering-related responses and high-level semantic information exhibit different characteristics across network stages, making it difficult for a unified feature-learning framework to fully exploit their complementarity. To address these issues, we propose a Scattering–Semantic Collaborative Network based on an asymmetric dual-branch DINO architecture, termed S-DINO. Specifically, a scattering-guided local feature reconstruction module establishes correlations between dispersed high-response regions and selected dominant response centers to improve the representation of fragmented target structures and enhance small-ship representation. Furthermore, a Semantic–Scattering Dual-Driven Query Injection strategy combines normalized feature-space response magnitude with semantic confidence to guide candidate reference-box initialization and reduce the selection bias caused by strong coastal activations. Experiments on the SSDD and HRSID inshore subsets demonstrate that, compared with the baseline DINO, S-DINO improves mAP@50 by 5.6 and 11.5 percentage points and F1-score by 11.4 and 8.6 percentage points, respectively. These results indicate the effectiveness of collaboratively exploiting scattering-related response cues and semantic information for ship detection in complex inshore environments.