Convolutional Neural Network Based on Bandwise-Independent Convolution and Hard Thresholding for Hyperspectral Band Selection

Band selection has been widely utilized in hyperspectral image (HSI) classification to reduce the dimensionality of HSIs. Recently, deep-learning-based band selection has become of great interest. However, existing deep-learning-based methods usually implement band selection and classification in isolation, or evaluate selected spectral bands by training the deep network repeatedly, which may lead to the loss of discriminative bands and increased computational cost. In this article, a novel convolutional neural network (CNN) based on bandwise-independent convolution and hard thresholding (BHCNN) is proposed, which combines band selection, feature extraction, and classification into an end-to-end trainable network. In BHCNN, a band selection layer is constructed by designing bandwise
$1\times 1$
convolutions, which perform for each spectral band of input HSIs independently. Then, hard thresholding is utilized to constrain the weights of convolution kernels with unselected spectral bands to zero. In this case, these weights are difficult to update. To optimize these weights, the straight-through estimator (STE) is devised by approximating the gradient. Furthermore, a novel coarse-to-fine loss calculated by full and selected spectral bands is defined to improve the interpretability of STE. In the subsequent layers of BHCNN, multiscale 3-D dilated convolutions are constructed to extract joint spatial–spectral features from HSIs with selected spectral bands. The experimental results on several HSI datasets demonstrate that the proposed method uses selected spectral bands to achieve more encouraging classification performance than current state-of-the-art band selection methods.