Unsupervised Pixel-Wise Hyperspectral Anomaly Detection via Autoencoding Adversarial Networks

We propose a completely unsupervised pixel-wise anomaly detection (AD) method for hyperspectral images (HSIs). 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 1-D-AEAN, 2-D-AEAN, and 3-D-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 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 weighted RX (WRX) -based detector in which the pixel weights are obtained according to REM. We compare our proposed method with the classical Reed–Xiaoli (RX), WRX, support vector data description (SVDD)-based, collaborative representation-based detector (CRD), adaptive weight deep belief network (AW-DBN) detector, and deep autoencoder AD (DAEAD) method on real hyperspectral data sets. The experimental results show that the proposed approach outperforms other detectors in the benchmark.