A dual-channel hyperspectral classification method based on NAS and transformer

Transformer networks present excellent performance in capturing long distance dependencies between different locations in the input sequence and are highly capable on a global scale. Recently, several neural architecture search (NAS) algorithms have been proposed for hyperspectral image (HSI) classification, which further improves the accuracy of HSI classification to a new level with more attention paid to local information. However, current two-channel network methods cannot focus on both local and global information of hyperspectral images, which leads to a decrease in the classification accuracy of this type of data. In this paper, a two-channel network named CTmixer-NAS is proposed for hyperspectral image classification. Combining the advantages of NAS and Transformer networks, local and global information is captured simultaneously. In the Transformer branch, the fusion of information at different self-coding layers is proposed. In the CNN branch, a high-performance network structure is automatically designed using NAS, which improves the accuracy of hyperspectral classification. The proposed approach saves labor cost, and the most suitable network structure can be searched according to different datasets, which makes the searched network structure present better generalization performance. CTmixer-NAS achieves the best performance in five hyperspectral datasets in comparative experiments.