Dynamic Cross-Modal Feature Interaction Network for Hyperspectral and LiDAR Data Classification

Hyperspectral image (HSI) and light detection and ranging (LiDAR) data joint classification is a challenging task. Existing multisource remote sensing data classification methods often rely on human-designed frameworks for feature extraction, which heavily depend on expert knowledge. To address these limitations, we propose a novel dynamic cross-modal feature interaction network (DCMNet), the first framework leveraging a dynamic routing mechanism for HSI and LiDAR classification. Specifically, our approach introduces three feature interaction blocks: bilinear spatial attention block (BSAB), bilinear channel attention block (BCAB), and integration convolutional block (ICB). These blocks are designed to effectively enhance spatial, spectral, and discriminative feature interactions. A multilayer routing space with routing gates is designed to determine optimal computational paths, enabling data-dependent feature fusion. Additionally, bilinear attention mechanisms are employed to enhance feature interactions in spatial and channel representations. Extensive experiments on three public HSI and LiDAR datasets demonstrate the superiority of DCMNet over the state-of-the-art methods. Our codes are available at https://github.com/oucailab/DCMNet.