One-Class SVMs for Hyperspectral Anomaly Detection

Amit Banerjee, Philippe Burlina, Chris Diehl

Research output: Chapter in Book/Report/Conference proceedingChapter

Original languageEnglish (US)
Title of host publicationKernel Methods for Remote Sensing Data Analysis
PublisherJohn Wiley & Sons, Ltd
Pages169-192
Number of pages24
ISBN (Print)9780470722114
DOIs
StatePublished - Nov 4 2009

Keywords

  • Computational and detection performance of SVDD anomaly detectors
  • Goodness-of-fit test statistic for hyperspectral imagery based on Barringhaus, Henze, Epps and Pully (BHEP) test
  • Normalized metric appropriate for anomaly detection in spectral imagery
  • One-class SVMs for hyperspectral anomaly detection
  • Support Vector Data Description (SVDD)
  • Support vector framework for hyperspectral anomaly detection
  • SVDD algorithms for hyperspectral anomaly detection
  • SVDD derivation
  • SVDD function optimization

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Banerjee, A., Burlina, P., & Diehl, C. (2009). One-Class SVMs for Hyperspectral Anomaly Detection. In Kernel Methods for Remote Sensing Data Analysis (pp. 169-192). John Wiley & Sons, Ltd. https://doi.org/10.1002/9780470748992.ch8

One-Class SVMs for Hyperspectral Anomaly Detection. / Banerjee, Amit; Burlina, Philippe; Diehl, Chris.

Kernel Methods for Remote Sensing Data Analysis. John Wiley & Sons, Ltd, 2009. p. 169-192.

Research output: Chapter in Book/Report/Conference proceedingChapter

Banerjee, A, Burlina, P & Diehl, C 2009, One-Class SVMs for Hyperspectral Anomaly Detection. in Kernel Methods for Remote Sensing Data Analysis. John Wiley & Sons, Ltd, pp. 169-192. https://doi.org/10.1002/9780470748992.ch8
Banerjee A, Burlina P, Diehl C. One-Class SVMs for Hyperspectral Anomaly Detection. In Kernel Methods for Remote Sensing Data Analysis. John Wiley & Sons, Ltd. 2009. p. 169-192 https://doi.org/10.1002/9780470748992.ch8
Banerjee, Amit ; Burlina, Philippe ; Diehl, Chris. / One-Class SVMs for Hyperspectral Anomaly Detection. Kernel Methods for Remote Sensing Data Analysis. John Wiley & Sons, Ltd, 2009. pp. 169-192
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