Neural Network Pruning for Hyperspectral Image Band Selection

Neural network pruning attempts to reduce parameters without hurting original performance by inducing connection matrix sparsity of network. Inspired by this idea, we proposed an effective pruning-based band selection strategy, which is a potent feature extraction tool in hyperspectral image (HSI) classification. At first, we take the whole HSI bands as input to train original network parameters. For each band, all parameters in network are integrated to measure the band importance. With the novel band signification factor constraining, then the convolutional neural network (CNN) is pruned and remains some representative weights to retrain the compact sub-network, which can finally deal with the hyperspectral band selection problem. Experimental results on the real HSI dataset demonstrate that network pruning-based method can outperform the original CNN in classification accuracy. Also, it can achieve the superiority over filter-based and other CNN-based band selection algorithms in classification accuracy. Our code is available at https://github.com/qixiong-wang/Network-pruning-for-HSI-band-selection.