关键词: 高光谱图像分类, 生成对抗网络（GANs）, 特征挖掘

Abstract: In order to solve the problem of insufficient feature utilization in hyperspectral image classification, a hyperspectral image classification method based on Generative Adversarial Networks（GANs） is proposed. According to the correlation between the spatial and spectral domains of hyperspectral image, the method of GANs is used to mine its deep features and generate highly discriminable hyperspectral image. The generated hyperspectral images are classified by Support Vector Machine（SVM）. The method is verified on two sets of hyperspectral data, the results show that this method can learn better generative model with a small number of spectral bands, and achieve much better performance in land-cover classification.

Key words: hyperspectral image classification, Generative Adversarial Networks（GANs）, feature mining