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.

高光谱图像（HSI）分类是HSI处理中的核心技术之一。为了解决标记样本的稀缺性问题，本文提出了一种基于混合生成对抗网络（MGAN）的新型HSI分类框架。首先，为了克服不能直接将MGAN应用于分类的缺点，将类别多分类器引入MGAN中进行分类任务。由于采用3D卷积神经网络（3DCNN）作为类别多分类器，因此可以捕获HSI的空间信息和局部3D数据结构进行分类，并将该框架命名为MGAN-3DCNN。因此，构造了新的损失函数。其次，由于新的损失函数是一个三方博弈，很难实现纳什均衡，设计了逐步的训练策略来解决相关的极大极小问题。在两个HSI数据集上的实验表明，所提出的MGAN-3DCNN大大缓解了过度拟合问题，并提高了小样本中HSI分类的鲁棒性。