FeatureFlow Transformer: Enhancing Feature Fusion and Position Information Modeling for Hyperspectral Image Classification

Transformer-based networks have gained significant interest in hyperspectral image classification in recent years due to its good performance in processing long sequences and parallel computation. However, the original Transformer still suffer from limitations in capturing or conveying subtle features and modeling spectral sequences. To address these issues and make Transformer more suitable for hyperspectral image classification tasks, this paper introduces Flow Feature Fusion module in Transformer that uses 2-D CNN to connect shallow and deep features and facilitate feature flow between encoders. This augmentation enhances its ability to perceive and process subtle image features. At the same time, Rotary Position Embedding is also introduced in self-attention to model spectral sequences effectively. Building upon these improvements, we propose the FeatureFlow Transformer, a network characterized by its simple structure and superior classification performance. The experimental results are validated on Indian Pines, Pavia University, and Salinas Valley, and comparisons with state-of-the-art models show that our proposed FeatureFlow Transformer achieves notable performance in hyperspectral image classification. The code will be available at https://github.com/xiaolabushiji/FeatureFlow_Transformer_for_Hyperspectral_Image_Classification.