Multi‐Scale graph wavelet convolutional network for hyperspectral image classification

Hyperspectral images (HSIs) have very high dimensionality and typically lack sufficient labeled samples, which significantly challenges their processing and analysis. These challenges contribute to the dimensionality curse, making it difficult to describe complex spatial relationships, especially those with non-Euclidean characteristics. This paper presents a multi-scale graph wavelet convolutional network (MS-GWCN) that utilizes a graph wavelet transform within a multi-scale learning framework to accurately capture spatial-spectral features. The MS-GWCN constructs graphs according to 8-neighborhood connectivity schemes, implements spectral graph wavelet transforms for multi-scale decomposition, and aggregates features through multi-scale graph convolutional layers. Our method, the MS-GWCN, demonstrates superior performance compared to existing methodologies. It achieves higher overall accuracy, average accuracy, per-class accuracy, and the Kappa coefficient, as evaluated on three datasets, including the Indian Pines, Salinas, and Pavia University datasets, thereby demonstrating enhanced robustness and generalization capability.