Unsupervised Pixel-wise Hyperspectral Anomaly Detection via Autoencoding Adversarial Networks

Sertac Arisoy; Nasser M. Nasrabadi; Koray Kayabol

doi:10.1109/lgrs.2021.3049711

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arXiv · 2101.08827

Unsupervised Pixel-wise Hyperspectral Anomaly Detection via Autoencoding Adversarial Networks

Sertac Arisoy·Nasser M. Nasrabadi·Koray Kayabol

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Abstract

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.

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Sertac Arisoy, Nasser M. Nasrabadi, Koray Kayabol. 2021-01-21. Unsupervised Pixel-wise Hyperspectral Anomaly Detection via Autoencoding Adversarial Networks. https://doi.org/10.1109/lgrs.2021.3049711

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