DGT: Deformable Graph Transformer for Hyperspectral Image Classification

Transformers can model global context to enhance the performance of hyperspectral classification. However, the explored global information is generally confined to the spatial neighborhood of target pixels. In order to fully leverage global correlation across broader areas, a deformable graph transformer (DGT) is proposed for hyperspectral classification, in which the global information within an entire image is explored to improve the classification performance. Specifically, DGT layers are designed to adaptively sample virtual nodes at varying distances from an initial graph constructed from an image, by which the global spatial information can be explored using deformable graph self-attention mechanism. Moreover, a learnable absolute position encoding (LAPE) module is constructed to enhance the spatial context awareness of DGT by integrating positional information into the graph nodes. Additionally, graph structure encoding and graph topology encoding are further designed as inductive biases for the graph, by which both local structural information and global topological information of the HSI are captured to enhance the feature extraction capability of the DGT layer. Ultimately, through the stacking of multiple DGT layers, a composite feature fusion learning (CFFL) module is employed to fully utilize the simple low-level and complex abstract high-level features extracted from different layers. Extensive experiments on four datasets demonstrate the superiority and robustness of the proposed DGT over several state-of-the-art methods in terms of various evaluation criteria.