  Hyperspectral image (HSI) consists of hundreds of continuous narrow bands
with high spectral correlation, which would lead to the so-called Hughes
phenomenon and the high computational cost in processing. Band selection has
been proven effective in avoiding such problems by removing the redundant
bands. However, many of existing band selection methods separately estimate the
significance for every single band and cannot fully consider the nonlinear and
global interaction between spectral bands. In this paper, by assuming that a
complete HSI can be reconstructed from its few informative bands, we propose a
general band selection framework, Band Selection Network (termed as BS-Net).
The framework consists of a band attention module (BAM), which aims to
explicitly model the nonlinear inter-dependencies between spectral bands, and a
reconstruction network (RecNet), which is used to restore the original HSI cube
from the learned informative bands, resulting in a flexible architecture. The
resulting framework is end-to-end trainable, making it easier to train from
scratch and to combine with existing networks. We implement two BS-Nets
respectively using fully connected networks (BS-Net-FC) and convolutional
neural networks (BS-Net-Conv), and compare the results with many existing band
selection approaches for three real hyperspectral images, demonstrating that
the proposed BS-Nets can accurately select informative band subset with less
redundancy and achieve significantly better classification performance with an
acceptable time cost.
