Hyperspectral Classification

Chapter

Abstract

The detailed spectra defined in a hyperspectral images posses new image processing challenges and exciting opportunities. Unlike its multispectral counterpart, hyperspectral imagery captures a level of spectral resolution that contains unique compositional and structural information about the landscape not available in other forms of remotely sensed imagery. To exploit this source of information, thematic extraction based on hyperspectral data involves isolating spectral features in the image according to their reflectance properties followed by a comparison of these properties to those on known materials. In this chapter, we will review the methods employed to extract thematic information from hyperspectral imagery and examine the algorithms called upon to process image spectra.

Keywords

Covariance Asphalt Landsat Plague Culmination 

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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Department of GeographyOhio UniversityAthensUSA

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