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

Abstract

ABSTRACT 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.