A mixture generative adversarial network with category multi-classifier for hyperspectral image classification. Issue 11 (1st November 2020)

• Record Type: Journal Article
• Authors: Li, Hengchao; Wang, Weiye; Ye, Shaohui; Deng, Yangjun; Zhang, Fan; Du, Qian
• Is Part Of: Remote sensing letters. Volume 11:Issue 11(2020)
• Page Start: 983
• Page End: 992
• Publication Date: 2020-11-01
• Subjects: Remote sensing -- Periodicals; Remote sensing; Periodicals; 621.3678
• DOI: 10.1080/2150704X.2020.1804641
• Languages: English
• ISSNs: 2150-704X

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.