Dual-Branch Spatial–Spectral Transformer with Spatial Feature Enhancement for Hyperspectral Image Classification

Hyperspectral image classification is an important research topic in the field of remote sensing. Its core objective is to accurately identify different land-cover categories by jointly exploiting the spatial information and spectral characteristics of pixels. However, the complex and diverse spatial structures of ground objects in hyperspectral images make it challenging for existing methods to fully capture intricate spatial patterns. To address this issue, this paper proposes a dual-branch spatial–spectral transformer with spatial feature enhancement under a spatial–spectral joint representation learning framework. Specifically, in the spatial branch, a deformable spatial feature fusion module is introduced, which combines grouped deformable convolution with Transformer to achieve fine-grained modeling of irregular spatial structures. Subsequently, a global spatial semantic aggregation module is designed to further integrate spatial features and enhance their discriminative capability. In the spectral branch, Transformer is employed to model long-range spectral dependencies, while a convolutional feed-forward network is introduced to strengthen the representation ability of critical spectral information. Experimental results demonstrate that the proposed method achieves competitive classification performance on four benchmark datasets.