Advanced Hyperspectral Image Classification via Spectral–Spatial Redundancy Reduction and TokenLearner-Enhanced Transformer

Currently, hyperspectral image (HSI) classification methods based on deep learning (DL) are extensively researched. However, the unique imaging characteristics of HSIs introduce a significant amount of spectral-spatial information redundancy, which creates challenges for existing methods in fully processing and utilizing this information. Furthermore, the classical patch-based data augmentation introduces heterogeneous pixels, posing a risk of losing central target information. To tackle these challenges, we propose a novel HSI classification network via spectral-spatial redundancy reduction and TokenLearner-enhanced transformer, named SSRRTL. Leveraging the inherent advantages of convolutional neural networks (CNNs) and transformer architectures, we innovatively introduce three key modules: spectral redundancy reduction and multiscale information extraction (SRRMIE) module, gated spatial feature enhancement (GSFE) module, and TokenLearner module. The SRRMIE module focuses on extracting multiscale features, reducing spectral redundancy, and preserving discriminative features of minor components through separation, feature extraction (FE), and adaptive fusion strategies. To further handle spatial information redundancy, the GSFE module employs a gated mechanism to enhance the extraction of key spatial features and suppress the expression of redundant spatial features. In addition, the TokenLearner method within the transformer architecture integrates feature tokens with central target tokens to enhance semantic feature expression and stabilize central target information, thereby improving the model’s robustness. Experimental results on multiple datasets confirm the effectiveness of SSRRTL and its superiority over other state-of-the-art methods.