Improved Two-Branch Capsule Network for Hyperspectral Image Classification

Abstract

The method based on the dual-channel capsule network extracts spectral information and spatial informa-tion separately in two channels, which not only retains the feature extraction method of the dual-channel convolu-tional neural network, but also improves the classification accuracy. However, when researchers train the capsule network, the dynamic routing process generates a large number of training parameters because the hyperspectral image (HSI) usually consists of hundreds of channels. To address this limitation, 1D and 2D constraint windows are proposed to reduce the number of capsules from two extraction channels. It uses the capsule vector group as the calculation unit to perform convolution operations and reduce the amount of parameters and computational com-plexity of the capsule network. Based on this parameter reduction optimization method, a new dual-branch capsule neural network (DuB-ConvCapsNet-MRF) is proposed and applied to the task of hyperspectral image classifica-tion. In addition, in order to further improve the classification accuracy, Markov random field (MRF) is introduced to smooth the spatial region and the final output is got. The results of performing ablation experiments on two repre-sentative hyperspectral image datasets and comparing the proposed method with six existing classification methods show that DuB-ConvCapsNet-MRF is superior to other methods in classification performance, and effectively re-duces the cost of training capsule network.

Keywords

• |remote sensing|hyperspectral image classification|capsule neural network|constraint window|markov random field (mrf)