Hyperspectral imagery analysis using neural network techniques
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Authors
Gautreaux, Mark M.
Subjects
Advisors
Olsen, Richard Christopher
Walters, Donald L.
Date of Issue
1995-06
Date
June 1995
Publisher
Monterey, California. Naval Postgraduate School
Language
en_US
Abstract
Every material has a unique electromagnetic reflectance/emission signature which can be used to identify it. Hyperspectral imagers, by collecting high spectral resolution data, provide the ability to identify these spectral signatures. Utilization and exploitation of hyperspectral data is challenging because of the enormous data volume produced by these imagers. Most current processing and analyzation techniques involve dimensionality reduction, during which some information is lost. This thesis demonstrates the ability of neural networks and the Kohonen Self-Organizing Map to classify hyperspectral data. The possibility of real time processing is addressed. (AN)
Type
Thesis
Description
Series/Report No
Department
Applied Physics
Organization
Identifiers
NPS Report Number
Sponsors
Funding
Format
99 p.
Citation
Distribution Statement
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.