Abstract

The first portion of this study checked water, vegetation, and urban class features of LANDSAT TM data for univariate normality using Pearson's system of frequency curves. Results indicated that of the 144 image bands tested 135 were determined to be normal in distribution. The second part of the study developed an image generator that uses the mean, covariance matrix and intraband correlation of LANDSAT TM images to create synthetic class scenes. Imagery composed of multiple synthetic class scenes, which ranged from normal to non-normal in their distributions, were classified using a maximum likelihood classifier. No significant difference in classification accuracy was found between the normally distributed data and the non-normal image data.

Library of Congress Subject Headings

Remote sensing--Data processing; Imaging systems--Image quality--Data processing; Remote sensing--Mathematics; Multivariate analysis

Publication Date

7-1-1995

Document Type

Thesis

Department, Program, or Center

Chester F. Carlson Center for Imaging Science (COS)

Advisor

Salvaggio, Carl

Advisor/Committee Member

Mason, Sterling

Advisor/Committee Member

Schott, John

Comments

Note: imported from RIT’s Digital Media Library running on DSpace to RIT Scholar Works. Physical copy available through RIT's The Wallace Library at: G70.4.F748 1995

Campus

RIT – Main Campus

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