CSS-Mamba: Competitive Sparse-Selection Mamba Network for Hyperspectral–LiDAR Joint Classification (opens in new tab)

Hyperspectral imaging (HSI) and light detection and ranging (LiDAR) data provide highly complementary information in terms of fine-grained spectral characterization and 3-D structural description, which is crucial for accurate land-cover classification in complex scenes. However, most existing multimodal fusion methods adopt isomorphic feature extraction and weighted aggregation strategies, which fail to explicitly model the intrinsic differences between spectral sparsity in HSI and geometric...