Graph-Aware Hybrid Encoding for Hyperspectral Image Classification

Hyperspectral image (HSI) classification faces critical challenges in effectively modeling the intricate spectral–spatial structures and non-Euclidean relationships. Traditional methods often struggle to simultaneously capture local details, global contextual dependencies, and graph-structured correlations, leading to limited classification accuracy. To address the above issues, this letter proposes a graph-aware hybrid encoding (GAHE) framework. To fully exploit the spectral–spatial characteristics and graph structural dependencies inherent in HSI, the proposed method is structured into three key components: a multiscale selective graph-aware attention (MSGA) module, a hybrid projection encoding module, and a graph sensitive aggregation (GSA) module. The three modules work in a complementary manner to progressively refine and enhance feature representations across multiple scales and modalities. Compared with advanced classification methods, the experimental results demonstrate that the proposed GAHE method shows better classification performance.