IDNet: Intensity-Constrained Detail-Enhanced Network for Hyperspectral and LiDAR Collaborative Classification

The complementarity of hyperspectral image (HSI) and light detection and ranging (LiDAR) data provides significant advantages in land cover classification tasks. Currently, most HSI and LiDAR classification algorithms focus on local-global feature learning frameworks. However, high-frequency characteristics play a vital role in distinguishing different land cover classes. For instance, the high-frequency information in the spectral curve corresponds to spectral features characterized by rapid variations, as well as detailed features (edges and boundaries) in the spatial land cover information. Therefore, embedding high-frequency detail information can enhance the collaborative classification of HSI and LiDAR data. Motivated by this, this paper proposes an Intensity-Constrained Detail-Enhanced Network, named IDNet. This network embeds Laplacian high-frequency features into a Transformer network architecture and designs Transformer blocks with dynamic range awareness and similarity-based spatial-spectral feature grouping interactions to efficiently learn local-global high-frequency spatial-spectral features in the HSI and LiDAR branches. During the feature fusion stage, differential convolution is utilized to construct an enhanced detail expert that emphasizes high-frequency feature information, enabling adaptive dynamic feature fusion to improve the discriminability of the fused features. Experimental results show that compared with the current state-of-the-art algorithms such as S2ENet, HCT-Net and MHST, the proposed IDNet is improved by 0.55%-2.72%, 0.17% -1.31%, 0.41%-4.92%, and 2.58%-13.13% on the Houston 2013, Trento, MUUFL, and Augsburg dataset with OA as the evaluation index, respectively. Code of this project is at https://github.com/ZhaoYuQing01/IDNet.