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

Heidi A. Smartt, J. Scott Tyo

Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish (US)
Title of host publicationAlgorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII
DOIs
StatePublished - Sep 20 2006
EventAlgorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII - Kissimmee, FL, United States
Duration: Apr 17 2006Apr 20 2006

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume6233 I
ISSN (Print)0277-786X

Other

OtherAlgorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII
CountryUnited States
CityKissimmee, FL
Period4/17/064/20/06

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

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  • Cite this

    Smartt, H. A., & Tyo, J. S. (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). https://doi.org/10.1117/12.666041