Discriminative Semi-Supervised Generative Adversarial Network for Hyperspectral Anomaly Detection

Abstract

Hyperspectral anomaly detection has been facing great challenges in the field of deep learning due to high dimensions and limited samples. To address these challenges, a novel discriminative semi-supervised generative adversarial network (GAN) method with dual RX (Reed-Xiaoli), called semiDRX, is proposed in this paper. The main contribution of the proposed method is to learn a reconstruction of background homogenization and anomaly saliency through a semi-supervised GAN. To achieve this goal, firstly, the coarse RX detection is performed to obtain a background sample set with potential anomalous pixels being removed. Secondly, the obtained coarse background set learns more comprehensive background characteristics through the network. The original hyperspectral image (HSI) is fed into the learned network to obtain reconstructions with homogeneous backgrounds and salient anomalies. The refined detection results are generated by a second RX detector. Experiments on three HSIs over different scenes demonstrate its advancement and effectiveness.