Award Date

1-1-1999

Degree Type

Thesis

Degree Name

Master of Science (MS)

Department

Electrical and Computer Engineering

First Committee Member

Lori Mann Bruce

Number of Pages

84

Abstract

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.

Keywords

Algorithms; Analysis; Based; Fast; Hyperspectral; Signatures; Wavelet

Controlled Subject

Electrical engineering

File Format

pdf

File Size

2488.32 KB

Degree Grantor

University of Nevada, Las Vegas

Language

English

Permissions

If you are the rightful copyright holder of this dissertation or thesis and wish to have the full text removed from Digital Scholarship@UNLV, please submit a request to digitalscholarship@unlv.edu and include clear identification of the work, preferably with URL.

Rights

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

Download


COinS