MMD-MLP: LiDAR-Guided Hyperspectral Data Classification Using Local-Global Directional-MLP With Multiresolution Multiscale Representation

Hyperspectral images (HSIs) are rich in spectral information and are widely used in the field of land-cover classification. However, existing deep learning methods ignore the combination of multiscale and multiresolution information, while not making better use of light detection and ranging (LiDAR) elevation information to assist in the enhancement of HSI data. To solve the problem above, we proposed a multiresolution, multiscale local-global directional MLP (MMD-MLP) for HSI classification with a LiDAR-guided feature enhancement module in this article. The algorithm first introduced a local-global-directed MLP structure, which effectively combines local and global features. Second, a multiresolution and multiscale feature extraction strategy is invented for the accurate acquisition of the detailed information and features of different land covers under different scale sizes. Subsequently, a LiDAR-guided feature enhancement module, which introduces the elevation information from LiDAR for improving the feature representation of HSI, adopts a cross-attention mechanism to reduce the semantic gap to improve the features of HSI. The proposed algorithm was evaluated on multiple hyperspectral-LiDAR datasets, and the results demonstrate that it achieves state-of-the-art (SOTA) performance. The code will be available at https://github.com/sanxian-svg/MMD-MLP.