Hyperspectral anomaly detection based on variational background inference and generative adversarial network

Hyperspectral anomaly detection is aimed at detecting targets with significant spectral differences from their surroundings. Recently, deep generative models have been applied to anomaly detections, while the existing generative adversarial network (GAN)-based methods have difficulty in accurately modeling the background and achieving spectrum reconstruction. In this article, a hyperspectral anomaly detection network based on variational background inference and generative adversarial framework (VBIGAN-AD) is proposed. The proposed VBIGAN model can learn the background distribution characteristics of HSIs and enhance the detection performance by the use of reconstruction errors. Specifically, the VBIGAN framework consists of sample and latent GANs, which establishes the relationship between data samples and latent samples through two sub-networks to capture the data distribution. Furthermore, the variational inference method is introduced and the hyperspectral background distribution can be converged to a multivariate normal distribution. To accurately learn the background distribution characteristics and reconstruct the background spectra, the coupling loss is conducted by enforcing feature match in the two discriminators on the basis of composite loss, and the results show that the additional loss can promote the detection performance. As a result, the reconstruction errors generated by the VBIGAN-AD method is utilized to detect abnormal targets. The experiments conducted on five datasets proved the robustness and applicability of the proposed VBIGAN-AD method.