BSFormer: Transformer-Based Reconstruction Network for Hyperspectral Band Selection
Yufei Liu, Xiaorun Li, Zezhong Xu, Ziqiang Hua
Abstract
Band selection (BS) is an effective approach to alleviate the spectral redundancy of a hyperspectral image (HSI). The emerging deep-learning-based BS methods have become a hot topic due to their ability to model nonlinear relationships between spectral bands. However, existing deep-learning-based BS methods fail to accurately extract the representativeness of each band as a result of the limitation of interpretation networks. Moreover, existing deep-learning methods cannot fully utilize the interband correlation and the spatial information of HSIs for BS. To solve these issues, in this letter, we propose a novel Transformer reconstruction network for unsupervised BS, termed BSFormer. Specifically, the Transformer reconstruction network, which contributes to leveraging the spectral–spatial information of the HSI, consists of a Transformer-based band attention (TBA) module and a convolutional autoencoder (CAE)-based reconstruction module. On this basis, we design a novel band evaluation criterion composed of representative metric and redundancy metric, which are interpreted with the help of the multihead self-attention layer in the TBA module. The designed criterion can fully use the band representativeness and interband correlation for BS. Experimental results on three well-known hyperspectral datasets verify that the proposed BSFormer can yield better classification performance than the competitors.