SDRCNet: A Lightweight Structure-Guided Dual-Relation Consensus Network for Optical Remote Sensing Images

Multi-label classification of very-high-resolution remote sensing scenes is difficult not only because multiple land-cover categories coexist in one image, but also because their discriminative evidence is spatially uneven: boundaries, elongated structures, and fragmented regions are often weakened by appearance-dominated features; small categories can be suppressed by global scene responses; and large-area categories require broader spatial context. These spatial ambiguities are further complicated by label dependencies, where co-occurring categories may support each other while visually similar categories may compete under weak or incomplete local evidence. To address these coupled challenges, we propose SDRCNet, a lightweight structure-guided dual-relation consensus network for multi-label remote sensing scene classification. First, SDRCNet introduces structure-guided feature learning to strengthen boundary, directional, and regional structural cues while maintaining an efficient network design. Second, it learns label-aware query representations and aggregates multi-granularity evidence from global scene context, local detail responses, and regional patterns, enabling different categories to obtain evidence from suitable spatial scales. Third, an evidence-aware relation reasoning mechanism models both category correlations and category competitions, allowing the network to exploit supportive label context while suppressing conflicting predictions in complex scenes. Experiments on China-MAS-50k demonstrate that SDRCNet achieves 79.3% mAP and 83.7% OF1. Under a comparable lightweight efficiency regime, it improves over RepViT-M1.1 by 1.9 and 0.7 percentage points in mAP and OF1, respectively, while using fewer GFLOPs and parameters. Compared with the strongest official deep baseline, it improves both mAP and OF1 by 5.3 points while using only 1.13 GFLOPs and 3.82M parameters. On MultiScene-Clean, SDRCNet further improves mAP from 64.8% to 70.5% and OF1 from 71.3% to 75.9%, showing consistent effectiveness across different multi-label remote sensing benchmarks.