DocumentCode
3497996
Title
Subset selection analysis for the reduction of hyperspectral imagery
Author
Vélez-Reyes, Miguel ; Jiménez, Luis O.
Author_Institution
Dept. of Electr. & Comput. Eng., Puerto Rico Univ., Mayaguez, Puerto Rico
Volume
3
fYear
1998
fDate
6-10 Jul 1998
Firstpage
1577
Abstract
Presents the formulation of the dimension reduction problem using subset selection as a matrix approximation problem. A heuristic algorithm to solve this problem is presented. Numerical results using LANDSAT and AVIRIS images show that the selected bands are contained in a space that is almost aligned with the first few principal components
Keywords
image processing; remote sensing; AVIRIS images; LANDSAT images; dimension reduction problem; heuristic algorithm; hyperspectral imagery reduction; matrix approximation problem; numerical results; principal components; subset selection analysis; Covariance matrix; Hyperspectral imaging; Hyperspectral sensors; Image analysis; Image processing; Laboratories; Random variables; Remote sensing; Satellites; Vectors;
fLanguage
English
Publisher
ieee
Conference_Titel
Geoscience and Remote Sensing Symposium Proceedings, 1998. IGARSS '98. 1998 IEEE International
Conference_Location
Seattle, WA
Print_ISBN
0-7803-4403-0
Type
conf
DOI
10.1109/IGARSS.1998.691622
Filename
691622
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