Semi-Supervised Variational Generative Adversarial Networks for Hyperspectral Image Classification

Abstract

Though Hyperspectral Image (HSI) Classification has been extensively investigated over recent decades, it is still a challenge task especially when the number of labeled samples is extremely limited. In this paper, we overcome this challenge by using synthetic samples, and proposed a semi-supervised variational Generative Adversarial Networks(GANs) for this purpose. Compared to the conditional GAN which is recently used for generating samples for HSI classification, the proposed approach has two novel aspects. First, we extend the classic variational generative adversarial network to the semi-supervised context through an ensemble prediction technique. By this way, our model can be trained using limited labeled samples (only 5 samples per class) with a large number of unlabeled samples. Second, we adopt an encoder-decoder network to explicitly learn the relationship between the latent space and the real image space. This property enables our model producing diverse samples by simply varying some latent parameters, which is desirable for enriching the training dataset. We have shown that the proposed model can achieve better and robust performance for HSI classification compared to conditional GAN, especially when the labeled data is limited.