Hyperspherical Structural-Aware Distillation Enhanced Spatial–Spectral Bidirectional Interaction Network for Hyperspectral Image Classification

The existing methods for hyperspectral image classification (HSIC) mainly focus on the extraction of spectral and spatial features while paying less attention to the interaction of each other. Besides, most of them directly use a parameterized classifier as the final layer of the network. While this design is convenient for end-to-end optimization with the backbone, it overlooks the utilization of the metric space. In this article, a novel hyperspherical structural-aware distillation enhanced spatial–spectral bidirectional interaction network (HSDBIN) is proposed for HSIC. HSDBIN uses a dual-branch design combining the 1-D CNN and transformer to separately learn the detailed spectral correlations and global spatial relationships in parallel. Then, by interacting and aggregating the independent information between two parallel branches, a bidirectional interaction block across branches is designed to explore complementary clues between spectral and spatial pipelines. Finally, to enhance the utilization of metric space and keep compact intraclass relationship, we propose a hyperspherical structural-aware distillation (HSD) to transfer the geometric relationship of hyperspherical space into the metric space of output logits. Extensive experiments and analysis on three public HSI datasets suggest the superiority of the proposed method and verify the effectiveness of the proposed modules.