Attention Head Interactive Dual Attention Transformer for Hyperspectral Image Classification

In recent years, transformer has attracted the attention of many researchers in the field of remote sensing due to its ability to model global information. However, it is difficult to extract local features such as textures and edges of images, thereby limiting the performance of transformer-based hyperspectral image classification (HSIC). Currently, most existing transformer models for HSIC improve their performance by combining the powerful feature extraction ability of convolution, which also introduces a large number of trainable parameters and increases model complexity. To address this issue, this article proposes a dual attention transformer for attention head interaction (DAHIT) for HSIC. First, a spatial local bias module (SLBM) was designed in the spatial branch, which introduces local priors to extract local features effectively without introducing numerous trainable parameters. Then, an attention head interaction module (AHIM) was proposed, which can make the interaction of information obtained by different attention heads. Finally, a diagonal mask multiscale dual attention module (DAM) was constructed in the spectral branch to enhance the attention to the correlation among different spectral bands through diagonal masks and then to extract features at different scales through feature vectors. Through a series of experiments, the proposed DAHIT is evaluated on four commonly used HSI datasets. The experimental results show that compared with other advanced methods, the proposed DAHIT method exhibits excellent classification performance, demonstrating the effectiveness of the proposed method in HSIC.