Classification of hyperspectral spatial/spectral patterns using Gauss-Markov random fields

Heidi A. Smartt; J. Scott Tyo

doi:10.1117/12.666041

Classification of hyperspectral spatial/spectral patterns using Gauss-Markov random fields

Heidi A. Smartt, J. Scott Tyo

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

3 Scopus citations

Abstract

Hyperspectral imaging sensors capture digital images in hundreds of contiguous spectral bands, allowing remote material identification. Most algorithms for identifying materials characterize the materials according to spectral information only, ignoring potentially valuable spatial relationships. This paper investigates the use of integrated spatial and spectral information for characterizing materials. It examines the specific situation where a set of pixels has resolution such that it contains spatial patterns of mixed pixels. An autoregressive Gauss-Markov random field (GMRF) is used to model the predictability of a target pixel from neighboring pixels. At the resolution of interest, the GMRF model can successfully classify spatial patterns of aircraft and a residential area from the HYDICE airborne sensor Desert Radiance field collection at Davis Monthan Air Force Base, Arizona.

Original language	English (US)
Title of host publication	Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII
DOIs	https://doi.org/10.1117/12.666041
State	Published - 2006
Externally published	Yes
Event	Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII - Kissimmee, FL, United States Duration: Apr 17 2006 → Apr 20 2006

Publication series

Name	Proceedings of SPIE - The International Society for Optical Engineering
Volume	6233 I
ISSN (Print)	0277-786X

Other

Other	Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII
Country/Territory	United States
City	Kissimmee, FL
Period	4/17/06 → 4/20/06

Keywords

Gauss-Markov random fields
Hyperspectral classification
Hyperspectral texture

ASJC Scopus subject areas

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

Access to Document

10.1117/12.666041

Cite this

Classification of hyperspectral spatial/spectral patterns using Gauss-Markov random fields. / Smartt, Heidi A.; Tyo, J. Scott.
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII. 2006. 62330I (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 6233 I).

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

Smartt, HA & Tyo, JS 2006, Classification of hyperspectral spatial/spectral patterns using Gauss-Markov random fields. in Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII., 62330I, Proceedings of SPIE - The International Society for Optical Engineering, vol. 6233 I, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII, Kissimmee, FL, United States, 4/17/06. https://doi.org/10.1117/12.666041

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abstract = "Hyperspectral imaging sensors capture digital images in hundreds of contiguous spectral bands, allowing remote material identification. Most algorithms for identifying materials characterize the materials according to spectral information only, ignoring potentially valuable spatial relationships. This paper investigates the use of integrated spatial and spectral information for characterizing materials. It examines the specific situation where a set of pixels has resolution such that it contains spatial patterns of mixed pixels. An autoregressive Gauss-Markov random field (GMRF) is used to model the predictability of a target pixel from neighboring pixels. At the resolution of interest, the GMRF model can successfully classify spatial patterns of aircraft and a residential area from the HYDICE airborne sensor Desert Radiance field collection at Davis Monthan Air Force Base, Arizona.",

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AB - Hyperspectral imaging sensors capture digital images in hundreds of contiguous spectral bands, allowing remote material identification. Most algorithms for identifying materials characterize the materials according to spectral information only, ignoring potentially valuable spatial relationships. This paper investigates the use of integrated spatial and spectral information for characterizing materials. It examines the specific situation where a set of pixels has resolution such that it contains spatial patterns of mixed pixels. An autoregressive Gauss-Markov random field (GMRF) is used to model the predictability of a target pixel from neighboring pixels. At the resolution of interest, the GMRF model can successfully classify spatial patterns of aircraft and a residential area from the HYDICE airborne sensor Desert Radiance field collection at Davis Monthan Air Force Base, Arizona.

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Classification of hyperspectral spatial/spectral patterns using Gauss-Markov random fields

Abstract

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Keywords

ASJC Scopus subject areas

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