Multifeature Collaborative Attention Dynamic Hypergraph Convolutional Network for Hyperspectral Image Classification

Most of the current hyperspectral image classification (HSIC) methods assume that the interactions among all ground objects in hyperspectral images (HSIs) are static pairwise relationships. However, in real scenarios, multiple ground objects have complex spatial, spectral, or statistical correlations. These correlations are not limited to simple adjacent or pairwise relationships but also include complex higher order interactions involving three or more ground object categories. A hyperedge in a hypergraph can simultaneously connect multiple vertices, effectively capturing the multi-dimensional and high-order relationships among vertices. To address the limitations of the current mainstream methods in modeling the high-order interaction relationships of ground objects, a novel multifeature collaborative attention dynamic hypergraph convolutional (MDHGC) network is proposed to model the entire HSI and capture the high-order relationships among ground objects, thereby achieving accurate classification. Specifically, we designed a static–dynamic collaborative multiview hypergraph convolutional network based on differential attention to learn superpixel-level features, which allows stable and flexible learning of high-order interactions in HSI. To learn the complementary features of pixel-level HSIC, we introduce a branch based on convolutional neural neworks that includes multiscale feature extraction and global–local feature fusion. Comprehensive experiments have been conducted across four distinct datasets to rigorously evaluate the effectiveness of MDHGC.