Hyperspectral band selection based on ternary weight convolutional neural network

In this paper, a novel ternary weight convolution neural network (TWCNN) is proposed for band selection of hyperspectral images. TWCNN constructs deep-wise convolution layer with 1×1 filters as the first layer of the network, which is used for band selection. In the deep-wise convolution layer, weights are constrained to -1, 0, or 1. -1 and 1 represent that the corresponding band is selected, while 0 indicates it’s not. TWCNN constructs subsequent layers to extract features and classify for selected spectral bands. It combines band selection, feature extraction and classification into a unified optimization procedure, which makes it to achieve end-to-end band selection and classification. Furthermore, the constraints of the number of spectral bands is added to the cost function of TWCNN. The specific number of spectral bands can be selected. The experiment results show that the proposed model provides a competitive result to state-of-the-art methods.

Recommended citation: Feng, J., Li, D., Chen, J., Zhang, X., Tang, X., & Wu, X. (2019, July). Hyperspectral band selection based on ternary weight convolutional neural network. In IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium (pp. 3804-3807). IEEE.
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