Spatial-Spectral Graph Convolutional Network for Hyperspectral Target Detection

Deep learning-based hyperspectral target detection methods commonly face challenges such as insufficient target samples and inadequate use of spatial context. To address these limitations, we propose a novel hyperspectral target detection approach leveraging spatial-spectral graph convolutional networks. First, we introduce an innovative sample augmentation strategy utilizing pre-detection and target implantation, which can simulate different backgrounds around the target and expand the target sample, thereby enhancing the representation ability of the model. Next, a graph-based representation strategy is proposed to integrate spatial and spectral information. Finally, we develop four specialized graph network detectors (GCND, GATD, GCN-GAT1, and GCN-GAT2) with structural optimizations involving multi-scale feature fusion and dynamic neighborhood adjustments. Extensive experiments demonstrate the superior detection performance of our method compared to existing techniques, highlighting its practical significance.