Low-Rank and Sparse Decomposition with Mixture of Gaussian for Hyperspectral Anomaly Detection

Lu Li, Wei Li*, Qian Du, Ran Tao

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

156 Citations (Scopus)

Abstract

Recently, the low-rank and sparse decomposition model (LSDM) has been used for anomaly detection in hyperspectral imagery. The traditional LSDM assumes that the sparse component where anomalies and noise reside can be modeled by a single distribution which often potentially confuses weak anomalies and noise. Actually, a single distribution cannot accurately describe different noise characteristics. In this article, a combination of a mixture noise model with low-rank background may more accurately characterize complex distribution. A modified LSDM, by modeling the sparse component as a mixture of Gaussian (MoG), is employed for hyperspectral anomaly detection. In the proposed framework, the variational Bayes (VB) algorithm is applied to infer a posterior MoG model. Once the noise model is determined, anomalies can be easily separated from the noise components. Furthermore, a simple but effective detector based on the Manhattan distance is incorporated for anomaly detection under complex distribution. The experimental results demonstrate that the proposed algorithm outperforms the classic Reed-Xiaoli (RX), and the state-of-the-art detectors, such as robust principal component analysis (RPCA) with RX.

Original languageEnglish
Pages (from-to)4363-4372
Number of pages10
JournalIEEE Transactions on Cybernetics
Volume51
Issue number9
DOIs
Publication statusPublished - Sept 2021

Keywords

  • Anomaly detection
  • hyperspectral image
  • low-rank and sparse decomposition
  • mixture of Gaussian (MoG)

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