SS-MCNet: A Spatial–Spectral Multidimensional Collaborative Network for Fine-Grained Wetland Classification With UAV Hyperspectral Imagery

Wetlands are critical ecosystems where fine-grained land cover classification is crucial for effective management. Although UAV-based hyperspectral imaging provides both high spectral and spatial resolution, we observed significant confusion among spectrally and spatially similar classes in our self-collected Yellow River wetland dataset. To address this, we propose the Spectral-Spatial Multidimensional Collaborative Network (SS-MCNet), which comprises a Spectral Probe Module (SPM) for fine spectral representation, a Spatial Texture Module (STM) for detailed texture extraction, and a Spectral-Spatial Interaction Module (SSIM) to model cross-domain dependencies. A Collaborative Attention Fusion mechanism is introduced for adaptive balancing of spectral and spatial information, and a consistency-diversity loss function is designed to maintain consistency and complementarity among the three modules. Experimental results show that SS-MCNet achieves state-of-the-art performance with 98.08% overall accuracy (OA), 97.42% average accuracy (AA), and a Kappa coefficient of 97.75%, and performs well in an additional hyperspectral–panchromatic fusion experiment, providing a reference for multi-source fusion classification.