Spatial Residual Blocks Combined Parallel Network for Hyperspectral Image Classification

Abstract

In hyperspectral image (HSI) classification, there are challenges of the spatial variation in spectral features and the lack of labeled samples. In this paper, a novel spatial residual blocks combined parallel network (SRPNet) is proposed for HSI classification. Firstly, the spatial residual blocks extract spatial features from rich spatial contexts information, which can be used to deal with the spatial variation of spectral signatures. Especially, the skip connection in spatial residual blocks is conducive to the backpropagation of gradients and mitigates the declining-accuracy phenomenon in the deep network. Secondly, the parallel structure is employed to extract spectral features. Spectral feature learning on parallel branches contains fewer independent connection weighs through parameter sharing. Thus, fewer parameters of the network require a lesser number of training samples. Furthermore, the feature fusion is conducted on the multi-scale features from different layers in the spectral feature learning part. Extensive experiments of three representative HSI data sets illustrate the effectiveness of the proposed network.

Keywords

• Feature fusion
• hyperspectral image classification
• parallel network
• spatial residual blocks