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Proceedings Paper

Dynamic dimensionality reduction for hyperspectral imagery
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Paper Abstract

Data dimensionality (DR) is generally performed by first fixing size of DR at a certain number, say p and then finding a technique to reduce an original data space to a low dimensional data space with dimensionality specified by p. This paper introduces a new concept of dynamic dimensionality reduction (DDR) which considers the parameter p as a variable by varying the value of p to make p adaptive compared to the commonly used DR, referred to as static dimensionality reduction (SDR) with the parameter p fixed at a constant value. In order to materialize the DDR another new concept, referred to as progressive DR (PDR) is also developed so that the DR can be performed progressively to adapt the variable size of data dimensionality determined by varying the value of p. The advantages of the DDR over SDR are demonstrated through experiments conducted for hyperspectral image classification.

Paper Details

Date Published: 20 May 2011
PDF: 12 pages
Proc. SPIE 8048, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII, 80481I (20 May 2011); doi: 10.1117/12.884649
Show Author Affiliations
Haleh Safavi, Univ. of Maryland, Baltimore County (United States)
Keng-Hao Liu, Univ. of Maryland, Baltimore County (United States)
Chein-I Chang, Univ. of Maryland, Baltimore County (United States)


Published in SPIE Proceedings Vol. 8048:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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