  Redundancy and noise exist in the bands of hyperspectral images (HSIs). Thus,
it is a good property to be able to select suitable parts from hundreds of
input bands for HSIs classification methods. In this letter, a band attention
module (BAM) is proposed to implement the deep learning based HSIs
classification with the capacity of band selection or weighting. The proposed
BAM can be seen as a plug-and-play complementary component of the existing
classification networks which fully considers the adverse effects caused by the
redundancy of the bands when using convolutional neural networks (CNNs) for
HSIs classification. Unlike most of deep learning methods used in HSIs, the
band attention module which is customized according to the characteristics of
hyperspectral images is embedded in the ordinary CNNs for better performance.
At the same time, unlike classical band selection or weighting methods, the
proposed method achieves the end-to-end training instead of the separated
stages. Experiments are carried out on two HSI benchmark datasets. Compared to
some classical and advanced deep learning methods, numerical simulations under
different evaluation criteria show that the proposed method have good
performance. Last but not least, some advanced CNNs are combined with the
proposed BAM for better performance.
