AI RESEARCH
Implicit spatial-frequency fusion of hyperspectral and lidar data via kolmogorov-arnold networks
arXiv CS.CV
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ArXi:2605.14239v1 Announce Type: new Hyperspectral image (HSI) classification is challenging in complex scenes due to spectral ambiguity, spatial heterogeneity, and the strong coupling between material properties and geometric structures. Although LiDAR provides complementary elevation information, most HSI-LiDAR fusion methods rely on CNNs or MLPs with fixed activation functions and linear weights. These methods struggle to model structural discontinuities in LiDAR data, intricate spectral features of HSI, and their interactions.