Dual-Scale Graph Transformer With Spectral-Spatial Convolution for Hyperspectral Image Classification

Hyperspectral image (HSI) classification has emerged as a critical task in remote sensing. However, the inherent high spectral dimensionality, limited labeled data, and complex spatial structures in HSIs present substantial challenges to traditional and even deep learning-based approaches. Traditional convolutional neural networks (CNNs) often struggle to capture the irregular, non-Euclidean spatial relationships among pixels, whereas standard Transformer architectures are limited in modeling localized spectral-spatial dependencies and are prone to overfitting in scenarios with limited training data. In this paper, we propose a novel Dual-Scale Graph Transformer with Spectral Spatial Convolution (DGTSS) framework that integrates multi scale structural modeling and deep spectral feature extraction in a unified architecture. Specifically, we construct two complementary graph representations: a pixel-level graph that captures fine grained spatial relationships and a super-pixel level graph that encodes broader semantic structures. These dual-scale graphs are processed by a Graph Transformer module that incorporates node features and enables effective modeling of long-range dependencies and context-aware feature aggregation. Furthermore, we introduce a dedicated Spectral-Spatial Convolution (SSC) module to enhance spectral feature discrimination by maintaining local spatial continuity. Extensive experiments on four benchmark hyperspectral datasets demonstrate that our method achieves state-of-the-art classification performance. Moreover, compared with existing Transformer-based models, our framework significantly reduces model complexity and computational cost, while maintaining superior performance.