Weakly Supervised Discriminative Learning With Spectral Constrained Generative Adversarial Network for Hyperspectral Anomaly Detection

Anomaly detection (AD) using hyperspectral images (HSIs) is of great interest for deep space exploration and Earth observations. This article proposes a weakly supervised discriminative learning with a spectral constrained generative adversarial network (GAN) for hyperspectral anomaly detection (HAD), called weaklyAD. It can enhance the discrimination between anomaly and background with background homogenization and anomaly saliency in cases where anomalous samples are limited and sensitive to the background. A novel probability-based category thresholding is first proposed to label coarse samples in preparation for weakly supervised learning. Subsequently, a discriminative reconstruction model is learned by the proposed network in a weakly supervised fashion. The proposed network has an end-to-end architecture, which not only includes an encoder, a decoder, a latent layer discriminator, and a spectral discriminator competitively but also contains a novel Kullback-Leibler (KL) divergence-based orthogonal projection divergence (OPD) spectral constraint. Finally, the well-learned network is used to reconstruct HSIs captured by the same sensor. Our work paves a new weakly supervised way for HAD, which intends to match the performance of supervised methods without the prerequisite of manually labeled data. Assessments and generalization experiments over real HSIs demonstrate the unique promise of such a proposed approach.

Automated summary

This article proposes a weakly supervised discriminative learning approach using spectral constrained generative adversarial networks for hyperspectral anomaly detection. The network enhances discrimination between anomaly and background, and includes an end-to-end architecture with a novel spectral constraint. Experimental results demonstrate the unique promise of this approach.