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Proceedings Paper

Infrared target model validation using gray-level co-occurrence matrices
Author(s): Jeffrey S. Sanders
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Paper Abstract

This paper presents results of experiments in infrared signature characterization using gray-level co-occurrence matrices (GLCMs). GLCMs are a method of characterizing image content and have been used for tasks such as image segmentation and texture synthesis. Image characteristics that are implicitly included in GLCMs are all of the histogram- based statistics as well as spatial structure and spatial phase. It is desired that GLCMs can be used to compare a pair of images and provide a meaningful, quantitative measure of similarity that correlates well with human observer results. The experiments presented here were primarily concerned with the infrared signatures of ground targets, but are extendable to any type of image. Tools and methodologies were developed to calculate the GLCMs for a measured image of a ground vehicle and compare it to a computer-generated image of a three-dimensional signature model. Multiple metrics were used to compare the resultant GLCMs and the most promising is a metric adapted from tracking algorithms which provides a quantitative measure of similarity of ensembles of GLCMs.

Paper Details

Date Published: 14 July 1999
PDF: 10 pages
Proc. SPIE 3699, Targets and Backgrounds: Characterization and Representation V, (14 July 1999); doi: 10.1117/12.352940
Show Author Affiliations
Jeffrey S. Sanders, Simulation Technologies, Inc. (United States)


Published in SPIE Proceedings Vol. 3699:
Targets and Backgrounds: Characterization and Representation V
Wendell R. Watkins; Dieter Clement; William R. Reynolds, Editor(s)

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