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.