Abstract

A study is described in which optimal textural and spectral features are selected for scene segmentation. A set of 46 textural features and 3 spectral features were available for image classification. A method was developed which used a thresholded separability measure to select the best features for scene segmentation. The measure was based on the Mahalanobis distance between class means. The optimal feature selection process was applied to a variety of images and classification results using 4 features ranged from 91% to 97% with independent data sets. The use of the thresholded Mahalanobis-like distance for optimal feature selection was compared to the more common thresholded divergence separability measure and was found to choose features which were equally good for classification. The Mahalanobis-like measure had the additional advantage of using only 1/6 the time needed to calculate the divergence measure.

Library of Congress Subject Headings

Remote sensing--Data processing; Image processing--Digital techniques; Imaging systems--Image quality

Publication Date

5-16-1990

Document Type

Thesis

Department, Program, or Center

Chester F. Carlson Center for Imaging Science (COS)

Advisor

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: G70.4R674 1990

Campus

RIT – Main Campus

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