Infrared target model validation using gray-level co-occurrence matrices

摘要

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.