Hyperspectral Nonlinear Unmixing via Generative Adversarial Network

Abstract

Hyperspectral nonlinear unmixing (HNU) is an extremely challenging problem as it is very difficult, if possible at all, to derive an explicit model to describe the underlying nonlinear mixing process. This paper gives the first attempt to tackle this problem by taking advantage of recent advances in deep learning, in specific, the development in generative adversarial network (GAN). The biggest contribution of GAN is that upon training, the network can generate samples with the same probabilistic distribution as that of the training samples, without explicitly knowing what the distribution actually is. Hence, we ask a similar question: can we unmix a hyperspectal image without explicitly knowing the nonlinear mixing model? In order to test this hypothesis, this paper proposes a data-driven supervised HNU method as compared to the traditional model-based approaches and uses a specific GAN framework, CycleGAN to solve the challenging nonlinear unmixing problem. We exploit the linkage between the cycle consistency loss used in CycleGAN and the spectral reconstruction loss used in traditional methods. We make the essential discovery that the usage of the cycle consistency loss enables the learning of the mixing and unmixing processes to be dependent on the training data only, without the need of an explicit mixing model. We refer to the proposed approach as CycleGAN unmixing net, or CGU net. Experimental results indicate that the proposed CGU net exhibits stable and competitive performance on different datasets as compared to traditional HNU methods that are model-based.