Skewed t-Distribution for Hyperspectral Anomaly Detection Based on Autoencoder

We propose multivariate skewed
${t}$
-distribution (MVSkt) for hyperspectral anomaly detection (AD). The proposed distribution model is able to increase the detection performance of autoencoder (AE)-based anomaly detectors. In the proposed method, the reconstruction error of a deep AE is modeled with a skewed
${t}$
-distribution. The deep AE network is trained based on adversarial learning strategy by feeding its input with the hyperspectral data cubes. The parameters of the
${t}$
-distribution model are estimated using variational Bayesian approach. We define an MVSkt-based detection rule for pixel-wise AD. We compare our proposed method with those based on the multivariate normal (MVN) distribution and the robust MVN variance–mean mixture distributions on real hyperspectral datasets. The experimental results show that the proposed approach outperforms other detectors in the benchmark.