Can We Generate Good Samples for Hyperspectral Classification? — A Generative Adversarial Network Based Method

摘要

The insufficiency of training samples is really a great challenge for hyperspectral image (HSI) classification. Samples generation is a commonly used technique in deep learning based remote sensing field which can extend the training set. However, previous methods ignore the real distribution of the training samples in the feature space and thus can hardly ensure that the generated samples possess the same patterns with the real ones. In this paper, we propose a generative adversarial network based method (SpecGAN) to handle this problem. Different from traditional GAN framework where the generated samples have no categories, for the first time we take the label information into consideration for hyperspectral images. Feeding a random noise z and a class label vector y into the generator, we can get a spectral sample of the corresponding category. The experiments on the Pavia University data set demonstrate the potential of the proposed SpecGAN in spectral samples generation.