Dual-Branch Attention Convolution Spectral–Spatial Feature Extraction Networks for Hyperspectral Image Classification

The attention-based dual-branch network demonstrates significant potential in hyperspectral image (HSI) classification. However, the existing dual-branch approaches still face two crucial issues that hinder the further performance promotion of HSI classification: 1) The static convolution in feature extraction branches is incapable of dynamically exploring the local and global dependency features in HSI. 2) Dual-branch networks tend to overlook the fusion of deep spectral-spatial semantic information. To address these challenges, a dual-branch attention convolution spectral-spatial feature extraction network (DACSS) is proposed. DACSS encompasses a dual-branch feature extraction module (DFEM) and a spatial-spectral feature aggregation module (SSAM). DFEM dynamically extracts different scales features from both spatial and spectral branches by combining attention convolution and spectral-spatial attention mechanisms. Within these components, the attention convolution module adaptively highlights the extraction of detailed regional features in HSI, while the spectral-spatial attention module effectively alleviates the limitations of long-range spatial dependencies arising from the convolution process. Based on the attention weights, SSAM performs multi-scale fusion of deep semantic information from both spatial and spectral branches. The experimental results show that the proposed method outperforms several state-of-the-art methods, achieving overall accuracies of 91.69%, 89.24%, 89.50%, and 94.67 % on the HongHu, SG, UH2018, and QUH-TDW datasets, respectively.