One-Class SVMs for Hyperspectral Anomaly Detection

Amit Banerjee; Philippe Burlina; Chris Diehl

doi:10.1002/9780470748992.ch8

One-Class SVMs for Hyperspectral Anomaly Detection

Amit Banerjee, Philippe Burlina, Chris Diehl

School of Medicine

Research output: Chapter in Book/Report/Conference proceeding › Chapter

Original language	English (US)
Title of host publication	Kernel Methods for Remote Sensing Data Analysis
Publisher	John Wiley & Sons, Ltd
Pages	169-192
Number of pages	24
ISBN (Print)	9780470722114
DOIs	https://doi.org/10.1002/9780470748992.ch8
State	Published - 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)

Access to Document

10.1002/9780470748992.ch8

Cite this

@inbook{7c5f6d953c12473783b37f147b75fb4a,

title = "One-Class SVMs for Hyperspectral Anomaly Detection",

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",

author = "Amit Banerjee and Philippe Burlina and Chris Diehl",

year = "2009",

month = nov,

day = "4",

doi = "10.1002/9780470748992.ch8",

language = "English (US)",

isbn = "9780470722114",

pages = "169--192",

booktitle = "Kernel Methods for Remote Sensing Data Analysis",

publisher = "John Wiley & Sons, Ltd",

}

TY - CHAP

T1 - One-Class SVMs for Hyperspectral Anomaly Detection

AU - Banerjee, Amit

AU - Burlina, Philippe

AU - Diehl, Chris

PY - 2009/11/4

Y1 - 2009/11/4

KW - Computational and detection performance of SVDD anomaly detectors

KW - Goodness-of-fit test statistic for hyperspectral imagery based on Barringhaus, Henze, Epps and Pully (BHEP) test

KW - Normalized metric appropriate for anomaly detection in spectral imagery

KW - One-class SVMs for hyperspectral anomaly detection

KW - SVDD algorithms for hyperspectral anomaly detection

KW - SVDD derivation

KW - SVDD function optimization

KW - Support Vector Data Description (SVDD)

KW - Support vector framework for hyperspectral anomaly detection

UR - http://www.scopus.com/inward/record.url?scp=84856173037&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84856173037&partnerID=8YFLogxK

U2 - 10.1002/9780470748992.ch8

DO - 10.1002/9780470748992.ch8

M3 - Chapter

AN - SCOPUS:84856173037

SN - 9780470722114

SP - 169

EP - 192

BT - Kernel Methods for Remote Sensing Data Analysis

PB - John Wiley & Sons, Ltd

ER -