  We propose a completely unsupervised pixel-wise anomaly detection method for
hyperspectral images. The proposed method consists of three steps called data
preparation, reconstruction, and detection. In the data preparation step, we
apply a background purification to train the deep network in an unsupervised
manner. In the reconstruction step, we propose to use three different deep
autoencoding adversarial network (AEAN) models including 1D-AEAN, 2D-AEAN, and
3D-AEAN which are developed for working on spectral, spatial, and joint
spectral-spatial domains, respectively. The goal of the AEAN models is to
generate synthesized hyperspectral images (HSIs) which are close to real ones.
A reconstruction error map (REM) is calculated between the original and the
synthesized image pixels. In the detection step, we propose to use a WRX-based
detector in which the pixel weights are obtained according to REM. We compare
our proposed method with the classical RX, WRX, support vector data
description-based (SVDD), collaborative representation-based detector (CRD),
adaptive weight deep belief network (AW-DBN) detector and deep autoencoder
anomaly detection (DAEAD) method on real hyperspectral datasets. The
experimental results show that the proposed approach outperforms other
detectors in the benchmark.
