Low rank decomposition-based anomaly detection

Shih Yu Chen, Shiming Yang, Konstantinos Kalpakis, Chein I. Chang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

44 Citations (Scopus)


With high spectral resolution hyperspectral imaging is capable of uncovering many subtle signal sources which cannot be known a priori or visually inspected. Such signal sources generally appear as anomalies in the data. Due to high correlation among spectral bands and sparsity of anomalies, a hyperspectral image can be e decomposed into two subspaces: A background subspace specified by a matrix with low rank dimensionality and an anomaly subspace specified by a sparse matrix with high rank dimensionality. This paper develops an approach to finding such low-high rank decomposition to identify anomaly subspace. Its idea is to formulate a convex constrained optimization problem that minimizes the nuclear norm of the background subspace and little l1 norm of the anomaly subspace subject to a decomposition of data space into background and anomaly subspaces. By virtue of such a background-anomaly decomposition the commonly used RX detector can be implemented in the sense that anomalies can be separated in the anomaly subspace specified by a sparse matrix. Experimental results demonstrate that the background-anomaly subspace decomposition can actually improve and enhance RXD performance.

Original languageEnglish
Title of host publicationAlgorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX
Publication statusPublished - 2013
EventAlgorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX - Baltimore, MD, United States
Duration: 2013 Apr 292013 May 2

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


ConferenceAlgorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX
Country/TerritoryUnited States
CityBaltimore, MD

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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