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Development of a quantitative basis for selection of spectral features in a vegetation monitoring systemThe development of an objective methodology for evaluation of alternative Landsat data preprocessing options, spectral transform features for monitoring vegetation, and feature summarization algorithms is presented. Based on estimates of spectral separability between a target class and its confusion classes, analysis of variance techniques are used to evaluate potential design options for large scale vegetation monitoring systems. Case studies are presented for early season and through the season spring small grains separation and for barley/other spring small grains separation. It is concluded that a basis for efficient, objective selection among alternative feature extraction approaches has been established for the large scale vegetation mapping/inventory problem. Although the approach has been demonstrated for the unitemporal class separability case, extensions to the multitemporal case are under development.
Document ID
19850028080
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Phinney, D. E.
(Lockheed Engineering and Management Services Co., Inc. Houston, TX, United States)
Smith, J. H.
(Lockheed Engineering and Management Services Co., Inc. Houston, TX, United States)
Trichel, M. C.
(NASA Johnson Space Center Houston, TX, United States)
Date Acquired
August 12, 2013
Publication Date
January 1, 1984
Subject Category
Earth Resources And Remote Sensing
Meeting Information
Meeting: International Symposium on Remote Sensing of Environment
Location: Ann Arbor, MI
Start Date: May 9, 1983
End Date: May 13, 1983
Accession Number
85A10231
Funding Number(s)
CONTRACT_GRANT: NAS9-15800
Distribution Limits
Public
Copyright
Other

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