Abstract

This study compares the performance of two non-parametric classifiers and Gaussian Maximum Likelihood (GML) for the classification of LANDSAT TM 30-meter resolution six-band data. The mathematical assumptions made in developing GML are valid if the pixels that constitute the training classes are normally distributed. Since it requires a model of the data, GML is termed a "parametric" classifier. Of current interest are new classification methodologies that make no assumptions about the statistical distribution of the pixels in the training class; these approaches are termed "non-parametric" classifiers. This study will compare the n-Dimensional Probability Density Function (nPDF) essentially a projection technique that reduces data dimensionality, and an advanced neural network that utilizes fiizzy-set mathematics, the Fuzzy ARTMAP, to the traditional GML approach to image classification. The different approaches will be compared for statistical classification accuracy and computational efficiency.

Library of Congress Subject Headings

Remote sensing--Mathematics; Gaussian processes; Image processing--Statistical methods; Image processing--Digital techniques

Publication Date

9-6-1995

Document Type

Thesis

Department, Program, or Center

Chester F. Carlson Center for Imaging Science (COS)

Advisor

Schott, John

Advisor/Committee Member

Anderson, Peter

Advisor/Committee Member

Easton, Roger

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 .N488 1995

Campus

RIT – Main Campus

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