IFGNet replaces fixed activations with KAN splines and adds LiDAR-guided implicit aggregation in spatial and frequency domains, reporting higher overall accuracy, average accuracy, and Kappa than prior fusion methods on the Houston 2013 and MUUFL benchmarks.
A novel deep learning framework by combination of subspace- based feature extraction and convolutional neural net- works for hyperspectral images classification,
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Implicit spatial-frequency fusion of hyperspectral and lidar data via kolmogorov-arnold networks
IFGNet replaces fixed activations with KAN splines and adds LiDAR-guided implicit aggregation in spatial and frequency domains, reporting higher overall accuracy, average accuracy, and Kappa than prior fusion methods on the Houston 2013 and MUUFL benchmarks.