LAUSR

Band selection (BS) is a foundational problem for the analysis of high-dimensional hyperspectral image (HSI) cubes. Recent developments in the visual attention mechanism allow for specifically modeling the complex relationship among different components. Inspired by this, this article proposes a novel band selection network, termed as nonlocal band attention network (NBAN), based on using a nonlocal band attention reconstruction network to adaptively calculate band weights. The framework consists of a band attention module, which aims to extract the long-range attention and reweight the original spectral bands, and a reconstruction network which is used to restore the reweighted data, resulting in a flexible architecture. The resulting BS network is able to capture the nonlinear and the long-range dependencies between spectral bands, making it more effective and robust to select the informative bands automatically. Finally, we compare the result of NBAN with six popular existing band selection methods on three hyperspectral datasets, the result showing that the long-range relationship is helpful for band selection processing. Besides, the classification performance shows that the advantage of NBAN is particularly obvious when the size of the selected band subset is small. Extensive experiments strongly evidence that the proposed NBAN method outperforms many current models on three popular HSI images consistently.