Share Email Print
cover

Proceedings Paper

Linearly constrained band selection for hyperspectral imagery
Author(s): Su Wang; Chein-I Chang
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Linearly constrained adaptive beamforming has been used to design hyperspectral target detection algorithms such as constrained energy minimization (CEM) and linearly constrained minimum variance (LCMV). It linearly constrains a desired target signature while minimizing interfering effects caused by other unknown signatures. This paper investigates this idea and further uses it to develop a new approach to band selection, referred to as linear constrained band selection (LCBS) for hyperspectral imagery. It interprets a band image as a desired target signature while considering other band images as unknown signatures. With this interpretation, the proposed LCBS linearly constrains a band image while also minimizing band correlation or dependence caused by other band images. As a result, two different methods referred to as Band Correlation Minimization (BCM) and Band Correlation Constraint (BCC) can be developed for band selection. Such LCBS allows one to select desired bands for data analysis. In order to determine the number of bands required to select, p, a recently developed concept, called virtual dimensionality (VD) is used to estimate the p. Once the p is determined, a set of p desired bands can be selected by LCBS. Finally, experiments are conducted to substantiate the proposed LCBS.

Paper Details

Date Published: 8 May 2006
PDF: 12 pages
Proc. SPIE 6233, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII, 62332B (8 May 2006); doi: 10.1117/12.665286
Show Author Affiliations
Su Wang, Univ. of Maryland/Baltimore County (United States)
Chein-I Chang, Univ. of Maryland/Baltimore County (United States)


Published in SPIE Proceedings Vol. 6233:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII
Sylvia S. Shen; Paul E. Lewis, Editor(s)

© SPIE. Terms of Use
Back to Top