A support vector method for anomaly detection in hyperspectral imagery

Amit Banerjee, Philippe Burlina, Chris Diehl

Research output: Contribution to journalArticlepeer-review


This paper presents a method for anomaly detection in hyperspectral images based on the support vector data description (SVDD), a kernel method for modeling the support of a distribution. Conventional anomaly-detection algorithms are based upon the popular Reed-Xiaoli detector. However, these algorithms typically suffer from large numbers of false alarms due to the assumptions that the local background is Gaussian and homogeneous. In practice, these assumptions are often violated, especially when the neighborhood of a pixel contains multiple types of terrain. To remove these assumptions, a novel anomaly detector that incorporates a nonparametric background model based on the SVDD is derived. Expanding on prior SVDD work, a geometric interpretation of the SVDD is used to propose a decision rule that utilizes a new test statistic and shares some of the properties of constant false-alarm rate detectors. Using receiver operating characteristic curves, the authors report results that demonstrate the improved performance and reduction in the false-alarm rate when using the SVDD-based detector on wide-area airborne mine detection (WAAMD) and hyperspectral digital imagery collection experiment (HYDICE) imagery.

Original languageEnglish (US)
Article number1661816
Pages (from-to)2282-2291
Number of pages10
JournalIEEE Transactions on Geoscience and Remote Sensing
Issue number8
StatePublished - Aug 1 2006


  • Hyperspectral
  • Support vector data description
  • Target detection

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Earth and Planetary Sciences(all)


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