Retinex-Based Dual-Branch Feature Extraction Network for Hyperspectral Image Classification

Deep learning-based methods for hyperspectral image classification (HSIC) have been widely utilized in recent years. However, existing HSIC methods do not adequately account for illumination variations in HSIs, particularly in urban areas where shadows created by complex ground objects result in significant variations that cannot be ignored. Additionally, when dealing with limited labeled data, most deep learning methods are prone to overfitting, resulting in poor classification performance. To address these challenges, we propose a new Retinex-based dual-branch feature extraction network (RDFEN) for HSIC. First, by incorporating Retinex theory, we propose a hyperspectral Retinex (HyperRetinex) module to extract illumination attributes and reflectance attributes. Then, we propose a dual-branch feature extraction network, which consists of two submodules: illumination attributes feature extraction (IAFE) module and reflectance attributes feature extraction (RAFE) module. Finally, an illumination-reflectance attributes interaction attention fusion (IRAIAF) module is strategically designed to integrate distinct features. Experiments on four benchmark HSI datasets demonstrate that the proposed method outperforms other state-of-the-art HSIC methods, achieving up to 96.85% overall accuracy on the Pavia University dataset, thereby highlighting its effectiveness and robustness in HSIC. For reproducibility, the code is available at https://github.com/JT-shen/Code.