Selecting training and test images for optimized anomaly detection algorithms in hyperspectral imagery through robust parameter design

Frank M. Mindrup; Mark A. Friend; Kenneth W. Bauer

doi:10.1117/12.884120

Selecting training and test images for optimized anomaly detection algorithms in hyperspectral imagery through robust parameter design

Frank M. Mindrup, Mark A. Friend, Kenneth W. Bauer

Air Force Institute of Technology

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

Original language	English
Title of host publication	Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII
DOIs	https://doi.org/10.1117/12.884120
State	Published - 2011
Externally published	Yes
Event	Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII - Orlando, FL, United States Duration: Apr 25 2011 → Apr 28 2011

Publication series

Name	Proceedings of SPIE - The International Society for Optical Engineering
Volume	8048
ISSN (Print)	0277-786X

Conference

Conference	Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII
Country/Territory	United States
City	Orlando, FL
Period	4/25/11 → 4/28/11

ASJC Scopus Subject Areas

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

Keywords

anomaly detection
Hyperspectral imagery
robust parameter design
training/test set selection

Access to Document

10.1117/12.884120

Cite this

Mindrup, F. M., Friend, M. A., & Bauer, K. W. (2011). Selecting training and test images for optimized anomaly detection algorithms in hyperspectral imagery through robust parameter design. In Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII Article 80480C (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 8048). https://doi.org/10.1117/12.884120

Selecting training and test images for optimized anomaly detection algorithms in hyperspectral imagery through robust parameter design. / Mindrup, Frank M.; Friend, Mark A.; Bauer, Kenneth W.
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII. 2011. 80480C (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 8048).

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

Mindrup, FM, Friend, MA & Bauer, KW 2011, Selecting training and test images for optimized anomaly detection algorithms in hyperspectral imagery through robust parameter design. in Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII., 80480C, Proceedings of SPIE - The International Society for Optical Engineering, vol. 8048, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII, Orlando, FL, United States, 4/25/11. https://doi.org/10.1117/12.884120

Mindrup FM, Friend MA, Bauer KW. Selecting training and test images for optimized anomaly detection algorithms in hyperspectral imagery through robust parameter design. In Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII. 2011. 80480C. (Proceedings of SPIE - The International Society for Optical Engineering). doi: 10.1117/12.884120

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title = "Selecting training and test images for optimized anomaly detection algorithms in hyperspectral imagery through robust parameter design",

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Selecting training and test images for optimized anomaly detection algorithms in hyperspectral imagery through robust parameter design

Publication series

Conference

ASJC Scopus Subject Areas

Keywords

Access to Document

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Cite this