Hyperspectral image classification based on coupled dual-channel generative adversarial network

The aim of hyperspectral image (HSI) classification is to define categories for the different labels that are assigned to each pixel vector. Generative adversarial network (GAN) can mitigate the limited training sample dilemma to some extent, but there are still two critical issues, namely balance collapse and insufficient sample diversity. In this article, we propose a Coupled Dual-Channel Generative Adversarial Network for HSI classification. It mainly consists of a Coupled Generative Network (CGN) and a Dual-Channel Discriminative Network (DDN). CGN achieves the reconstruction of HSI samples through cascaded convolutional layers, in which the label information of the samples is used to avoid the balance collapse, while DDN extracts spatial attention weights and spectral attention weights of the input true/false samples respectively, and performs feature mining in both spatial and spectral dimensions in a dual-channel style. In order to reinforce the detailed features of input samples in both spatial and spectral dimensions, we design a new Cascaded Spatial-Spectral Attention Block (CSSAB). Finally, feature maps at different scales are fused for final sample discrimination and classification, which can mitigate the effects of insufficient sample diversity. Experimental results on two HSI data sets demonstrate that the proposed CDGAN effectively improves the classification performance compared to some state-ofthe-art GAN-based methods