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 inter-class similarity. To address these challenges, we propose the Regional Spatial-Channel Information Weighted Attention Network (RSCIWANet). The innovations encompass: 1) Regional Spatial Channel Attention (RSCIWA) integrates regional weighting in spatial attention 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 (BA_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 mAP 50 of 99.35% on Sandia National Laboratories (SNL) video SAR dataset, 97.50% on MSAR-1.0 dataset, and superior large-scale detection capability on MSAR-1.0 and LS-SSDDD datasets.