Anomaly and target detection by means of nonparametric density estimation

G. A. Tidhar; S. R. Rotman

doi:10.1117/12.919638

Anomaly and target detection by means of nonparametric density estimation

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

We describe a novel completely non parametric high-dimension joint density estimation algorithm suited for anomaly and target detection using hyperspectral imaging. The new algorithm is compared against linear matched filter detection schemes with different available sample sizes, background statistics (MVN, GMM and non Gaussian). The new algorithm is shown to be superior in important cases.

Original language	American English
Title of host publication	Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII
Publisher	SPIE
ISBN (Print)	9780819490681
DOIs	https://doi.org/10.1117/12.919638
State	Published - 1 Jan 2012
Externally published	Yes
Event	18th Annual Conference on Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery - Baltimore, MD, United States Duration: 23 Apr 2012 → 27 Apr 2012

Publication series

Name	Proceedings of SPIE - The International Society for Optical Engineering
Volume	8390

Conference

Conference	18th Annual Conference on Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery
Country/Territory	United States
City	Baltimore, MD
Period	23/04/12 → 27/04/12

Keywords

Hyperspectral Imaging
Non parametric
Target detection

All Science Journal Classification (ASJC) codes

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

Access to Document

10.1117/12.919638

Cite this

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title = "Anomaly and target detection by means of nonparametric density estimation",

abstract = "We describe a novel completely non parametric high-dimension joint density estimation algorithm suited for anomaly and target detection using hyperspectral imaging. The new algorithm is compared against linear matched filter detection schemes with different available sample sizes, background statistics (MVN, GMM and non Gaussian). The new algorithm is shown to be superior in important cases.",

keywords = "Hyperspectral Imaging, Non parametric, Target detection",

author = "Tidhar, {G. A.} and Rotman, {S. R.}",

year = "2012",

month = jan,

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doi = "10.1117/12.919638",

language = "American English",

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booktitle = "Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII",

address = "United States",

note = "18th Annual Conference on Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery ; Conference date: 23-04-2012 Through 27-04-2012",

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Tidhar, GA & Rotman, SR 2012, Anomaly and target detection by means of nonparametric density estimation. in Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII., 839020, Proceedings of SPIE - The International Society for Optical Engineering, vol. 8390, SPIE, 18th Annual Conference on Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery, Baltimore, MD, United States, 23/04/12. https://doi.org/10.1117/12.919638

Anomaly and target detection by means of nonparametric density estimation. / Tidhar, G. A.; Rotman, S. R.
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII. SPIE, 2012. 839020 (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 8390).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Anomaly and target detection by means of nonparametric density estimation

AU - Tidhar, G. A.

AU - Rotman, S. R.

PY - 2012/1/1

Y1 - 2012/1/1

N2 - We describe a novel completely non parametric high-dimension joint density estimation algorithm suited for anomaly and target detection using hyperspectral imaging. The new algorithm is compared against linear matched filter detection schemes with different available sample sizes, background statistics (MVN, GMM and non Gaussian). The new algorithm is shown to be superior in important cases.

AB - We describe a novel completely non parametric high-dimension joint density estimation algorithm suited for anomaly and target detection using hyperspectral imaging. The new algorithm is compared against linear matched filter detection schemes with different available sample sizes, background statistics (MVN, GMM and non Gaussian). The new algorithm is shown to be superior in important cases.

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KW - Target detection

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M3 - Conference contribution

SN - 9780819490681

T3 - Proceedings of SPIE - The International Society for Optical Engineering

BT - Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII

PB - SPIE

T2 - 18th Annual Conference on Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery

Y2 - 23 April 2012 through 27 April 2012

ER -

Anomaly and target detection by means of nonparametric density estimation

Abstract

Publication series

Conference

Keywords

All Science Journal Classification (ASJC) codes

Access to Document

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