A mixture generative adversarial network with category multi-classifier for hyperspectral image classification

Hyperspectral image (HSI) classification is one of the core techniques in HSI processing. In order to solve the problem of scarcity of labelled samples, a novel HSI classification framework based on mixture generative adversarial networks (MGAN) is proposed in this letter. Firstly, to overcome the drawback that MGAN cannot be directly applied for classification, a category multi-classifier is introduced into MGAN to conduct the classification task. Due to 3D convolutional neural network (3DCNN) is adopted as the category multi-classifier, the spatial information and local 3D data structure of HSI can be captured for classification, and the proposed framework is named as MGAN-3DCNN. Accordingly, a new loss function is constructed. Secondly, since the new loss function is a tripartite game which is difficult to achieve Nash equilibrium, a step-by-step training strategy is designed to solve the related minimax problem. Experiments on two HSI data sets demonstrate that the proposed MGAN-3DCNN greatly alleviates the over-fitting problem and improves the robustness of HSI classification in small-size samples.