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


Document Type


Department, Program, or Center

Chester F. Carlson Center for Imaging Science (COS)


Schott, John


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