Skewed t-Distribution for Hyperspectral Anomaly Detection Based on Autoencoder

Kayabol K., Aytekin E. B., Arisoy S., Kuruoglu E. E.

IEEE Geoscience and Remote Sensing Letters, vol.19, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 19
  • Publication Date: 2022
  • Doi Number: 10.1109/lgrs.2021.3121876
  • Journal Name: IEEE Geoscience and Remote Sensing Letters
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Aquatic Science & Fisheries Abstracts (ASFA), Communication Abstracts, Compendex, Geobase, INSPEC, Metadex, Civil Engineering Abstracts
  • Keywords: Anomaly detection (AD), autoencoder (AE), hyperspectral image (HSI), multivariate skewed t-distribution (MVSkt), variational Bayes, BAYESIAN-INFERENCE, MIXTURES
  • Kocaeli University Affiliated: No

Abstract

© 2004-2012 IEEE.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.