RSCIWANet: Regional Spatial-Channel Information Weighted Attention Network for Video SAR and Large-Scale SAR Image Targets Detection

Synthetic aperture radar (SAR) encounters distinct challenges in airborne surveillance (dynamic scene variations, target edge blurring) and spaceborne observation (large-scale analysis, high-resolution processing). Both traditional methods and contemporary deep learning-based solutions exhibit limitations: inadequate dynamic target adaptability, weak small-target detection, and redundant recognition in large-scale scenarios, stemming from challenges like target ambiguity, occlusion, and interclass similarity. To address these challenges, we propose the regional spatial-channel information weighted attention network. The innovations encompass the following. 1) Regional spatial channel attention integrates regional weighting in spatial attention (SA) to amplify key positional features while suppressing speckle noise and edge weak samples. Channel self-attention enhances cross-regional interactions to capture target-environment scattering correlations. 2) Boundary-aware loss employs edge overlapping penalties to improve localization of fuzzy shadow edges, with adaptive weighting to amplify small-target gradient contributions during backpropagation. 3) Context-preserving sliding window detection strategy for large-scale images, which can carry out comprehensive and robust detection. Experimental results demonstrate state-of-the-art performance, with the mAP⁵⁰ of 99.35% on Sandia National Laboratories video SAR dataset, 97.50% on MSAR-1.0 dataset, and superior large-scale detection capability on MSAR-1.0 and LS-SSDDD datasets.