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
  • SVDD algorithms for hyperspectral anomaly detection
  • SVDD derivation
  • SVDD function optimization
  • Support Vector Data Description (SVDD)
  • Support vector framework for hyperspectral anomaly detection

ASJC Scopus subject areas

  • Engineering(all)

Cite this