Learning transformer-based heterogeneously salient graph representation for multimodal fusion classification of hyperspectral image and LiDAR data

Data collected by different modalities can provide a wealth of complementary information, such as hyperspectral image to offer rich spectral-spatial properties, synthetic aperture radar to provide structural information about the Earth's surface, and light detection and ranging to cover altitude information about ground elevation.