LAUSR

Hyperspectral image (HSI) consists of hundreds of continuous narrow bands with high redundancy, resulting in the curse of dimensionality and an increased computation complexity in HSI classification. Many clustering-based band selection approaches have been proposed to deal with such a problem. However, a few of them consider the spectral and spatial relationship simultaneously. In this letter, we proposed a novel clustering-based band selection approach using deep subspace clustering (DSC). The proposed approach combines the subspace clustering task into a convolutional autoencoder by treating it as a self-expressive layer, enabling it to be trained end to end. The resulting network can fully extract the interaction of spectral bands based on using spatial information and nonlinear feature transformation. We compared the results of the proposed method with existing band selection methods for three widely used HSI data sets, showing that the proposed method is able to accurately select an informative band subset with remarkable classification accuracy.