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A comparison of the usefulness of canonical analysis, principal components analysis, and band selection for extraction of features from TMS data for landcover analysisThree feature extraction methods, canonical analysis (CA), principal component analysis (PCA), and band selection, have been applied to Thematic Mapper Simulator (TMS) data in order to evaluate the relative performance of the methods. The results obtained show that CA is capable of providing a transformation of TMS data which leads to better classification results than provided by all seven bands, by PCA, or by band selection. A second conclusion drawn from the study is that TMS bands 2, 3, 4, and 7 (thermal) are most important for landcover classification.
Document ID
19850028129
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Boyd, R. K.
(Computer Sciences Corp. Greenbelt, MD, United States)
Brumfield, J. O.
(Marshall University Huntington, WV, United States)
Campbell, W. J.
(NASA Goddard Space Flight Center Greenbelt, MD, 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
85A10280
Distribution Limits
Public
Copyright
Other

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