  In this paper, we propose a generative adversarial network (GAN)-based
hyperspectral anomaly detection algorithm. In the proposed algorithm, we train
a GAN model to generate a synthetic background image which is close to the
original background image as much as possible. By subtracting the synthetic
image from the original one, we are able to remove the background from the
hyperspectral image. Anomaly detection is performed by applying Reed-Xiaoli
(RX) anomaly detector (AD) on the spectral difference image. In the
experimental part, we compare our proposed method with the classical RX,
Weighted-RX (WRX) and support vector data description (SVDD)-based anomaly
detectors and deep autoencoder anomaly detection (DAEAD) method on synthetic
and real hyperspectral images. The detection results show that our proposed
algorithm outperforms the other methods in the benchmark.
