Dual-Branch Hypergraph Convolutional Network Learning Spectral–Spatial–Semantic Features for Hyperspectral Image Classification

Hyperspectral Image (HSI) classification, which aims to assign pixel-level categories for given HSI data, has achieved remarkable success with deep learning architectures. However, such approaches typically require large amounts of annotated data, which are often scarce in practical applications. To address the challenge of limited annotated samples, this paper proposes a novel framework that leverages both semantic and structural prior knowledge to extract more expressive HSI features. This paper proposes a Dual-Branch Hypergraph Convolutional Network (DB-HGCN) for comprehensive spectral-spatial-semantic feature extraction, which employs multi-hop constrained superpixel-level hypergraphs to effectively model the complex high-order correlations inherent in HSI data. The proposed architecture consists of two complementary branches: (1) a Semantic-enhanced Hypergraph Convolutional Network (SSeHGCN) that incorporates category-specific semantic knowledge through a vision-language model to enhance spatial representations, and (2) a Spatial-Spectral enhanced Hypergraph Convolutional Network (SSpHGCN) that captures both intra-superpixel visual features and their interrelationships via a novel Spatial-Spectral Feature and Relation Fusion Module (SSRFM). Extensive experiments on four benchmark datasets demonstrate the superiority of the proposed approach, with DB-HGCN achieving state-of-the-art overall classification accuracy (OA) using only five labeled samples per class. This significant performance gain highlights the effectiveness of our method in addressing the data scarcity challenge in HSI classification.