Fast algorithms for wavelet-based analysis of hyperspectral signatures

Hyperspectral sensors promise great improvements in the quality of information gathered for remote sensing applications. However, they also present a huge challenge to data storage and computing systems. Thus there is a great need for reliable compression schemes, as well as analysis tools that can exploit the hyperspectral data in a computationally efficient manner. It has been proposed that wavelet-based methods may be superior to currently used methods for the analysis of hyperspectral signatures. In this thesis, a wavelet-based method, as well as traditional analytical methods, was implemented and applied to hyperspectral images. The computational expense of the various methods are determined analytically and experimentally to show advantages of the wavelet-based methods. Various measures, including cross correlation, signal-to-noise ratios and Euclidean distance, are designed and implemented for comparing the differences that might exist between the outputs of the algorithms.

Name thesis:degree_name

Master of Science (MS)

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical and Computer Engineering

Grantor dc:publisher

University of Nevada, Las Vegas

Year

1999

Author dc:creator

• Li, Jiang

Contributors dc:contributor

• Lori Mann Bruce

Statement dc:rights

• IN COPYRIGHT. For more information about this rights statement, please visit http://rightsstatements.org/vocab/InC/1.0/

Language dc:language

English

Identifier

https://oasis.library.unlv.edu/rtds/1006

OAI identifier oai:identifier

oai:oasis.library.unlv.edu:rtds-2005