Unsupervised Pixel-Wise Hyperspectral Anomaly Detection via Autoencoding Adversarial Networks

Abstract

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.