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Proceedings Paper

Application of principal component analysis to multisensor classification
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Paper Abstract

We are currently exploring the relationship between spatial statistical parameters of various geophysical phenomena and those of the remotely sensed image by way of principle component analysis (PCA) of radar and optical images. Issues being explored are the effects of incorporating PCA into land cover classification in an attempt to improve its accuracy. Preliminary results of using PCA in comparison with unsupervised land cover classification are presented.

Paper Details

Date Published: 29 January 1999
PDF: 9 pages
Proc. SPIE 3584, 27th AIPR Workshop: Advances in Computer-Assisted Recognition, (29 January 1999); doi: 10.1117/12.339822
Show Author Affiliations
Brian D. Corner, Univ. of Nebraska/Lincoln (United States)
Ram Mohan Narayanan, Univ. of Nebraska/Lincoln (United States)
Stephen E. Reichenbach, Univ. of Nebraska/Lincoln (United States)

Published in SPIE Proceedings Vol. 3584:
27th AIPR Workshop: Advances in Computer-Assisted Recognition
Robert J. Mericsko, Editor(s)

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