Double-branch feature fusion transformer for hyperspectral image classification

Deep learning methods, particularly Convolutional Neural Network (CNN), have been widely used in hyperspectral image (HSI) classification. CNN can achieve outstanding performance in the field of HSI classification due to its advantages of fully extracting local contextual features of HSI. However, CNN is not good at learning the long-distance dependency relation and dealing with the sequence properties of HSI. Thus, it is difficult to continuously improve the performance of CNN-based models because they cannot take full advantage of the rich and continuous spectral information of HSI. This paper proposes a new Double-Branch Feature Fusion Transformer model for HSI classification. We introduce Transformer into the process of HSI on account of HSI with sequence characteristics. The two branches of the model extract the global spectral features and global spatial features of HSI respectively, and fuse both spectral and spatial features through a feature fusion layer. Furthermore, we design two attention modules to adaptively adjust the importance of spectral bands and pixels for classification in HSI. Experiments and comparisons are carried out on four public datasets, and the results demonstrate that our model outperforms any compared CNN-Based models in terms of accuracy.

Automated summary

This paper proposes a new Double-Branch Feature Fusion Transformer model for HSI classification. The model extracts global spectral features and global spatial features of HSI through two branches, and fuses both spectral and spatial features through a feature fusion layer. Two attention modules are designed to adaptively adjust the importance of spectral bands and pixels for classification in HSI. Experimental results demonstrate that our model outperforms any compared CNN-Based models in terms of accuracy.