Adaptive feature-specific spectral imaging classifier (AFSSI-C)

Matthew Dunlop; Phillip Poon; Michael Gehm; Dathon Golish; Esteban Vera

Adaptive feature-specific spectral imaging classifier (AFSSI-C)

Matthew Dunlop, Phillip Poon, Michael Gehm, Dathon Golish, Esteban Vera

Research output: Contribution to journal › Conference article › peer-review

1 Scopus citations

Abstract

The AFSSI-C is a spectral imager that generates spectral classification directly, in fewer measurements than are required by traditional systems that measure the spectral datacube (which is later interpreted to make material classification). By utilizing adaptive features to constantly update conditional probabilities for the different hypotheses, the AFSSI-C avoids the overhead of directly measuring every element in the spectral datacube. The system architecture, feature design methodology, simulation results, and preliminary experimental results are given.

Original language	English
Journal	Proceedings of the International Telemetering Conference
Volume	49
State	Published - 2013
Externally published	Yes
Event	ITC/USA 2013: 49th Annual International Telemetering Conference and Technical Exhibition - Meeting all the Challenges of Telemetry, 2013 - Las Vegas,NV, United States Duration: 21 Oct 2013 → 24 Oct 2013

Keywords

Compressive sensing
Hyperspectral imaging
Spectral imager

Cite this

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title = "Adaptive feature-specific spectral imaging classifier (AFSSI-C)",

abstract = "The AFSSI-C is a spectral imager that generates spectral classification directly, in fewer measurements than are required by traditional systems that measure the spectral datacube (which is later interpreted to make material classification). By utilizing adaptive features to constantly update conditional probabilities for the different hypotheses, the AFSSI-C avoids the overhead of directly measuring every element in the spectral datacube. The system architecture, feature design methodology, simulation results, and preliminary experimental results are given.",

keywords = "Compressive sensing, Hyperspectral imaging, Spectral imager",

author = "Matthew Dunlop and Phillip Poon and Michael Gehm and Dathon Golish and Esteban Vera",

year = "2013",

language = "English",

volume = "49",

journal = "Proceedings of the International Telemetering Conference",

issn = "0884-5123",

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T1 - Adaptive feature-specific spectral imaging classifier (AFSSI-C)

AU - Dunlop, Matthew

AU - Poon, Phillip

AU - Gehm, Michael

AU - Golish, Dathon

AU - Vera, Esteban

PY - 2013

Y1 - 2013

N2 - The AFSSI-C is a spectral imager that generates spectral classification directly, in fewer measurements than are required by traditional systems that measure the spectral datacube (which is later interpreted to make material classification). By utilizing adaptive features to constantly update conditional probabilities for the different hypotheses, the AFSSI-C avoids the overhead of directly measuring every element in the spectral datacube. The system architecture, feature design methodology, simulation results, and preliminary experimental results are given.

AB - The AFSSI-C is a spectral imager that generates spectral classification directly, in fewer measurements than are required by traditional systems that measure the spectral datacube (which is later interpreted to make material classification). By utilizing adaptive features to constantly update conditional probabilities for the different hypotheses, the AFSSI-C avoids the overhead of directly measuring every element in the spectral datacube. The system architecture, feature design methodology, simulation results, and preliminary experimental results are given.

KW - Compressive sensing

KW - Hyperspectral imaging

KW - Spectral imager

UR - http://www.scopus.com/inward/record.url?scp=84897814399&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:84897814399

SN - 0884-5123

VL - 49

JO - Proceedings of the International Telemetering Conference

JF - Proceedings of the International Telemetering Conference

T2 - ITC/USA 2013: 49th Annual International Telemetering Conference and Technical Exhibition - Meeting all the Challenges of Telemetry, 2013

Y2 - 21 October 2013 through 24 October 2013

ER -

Adaptive feature-specific spectral imaging classifier (AFSSI-C)

Abstract

Keywords

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