Target detection using texture operators

James M. Coggins

doi:10.1117/12.665798

18 May 2006 Target detection using texture operators

James M. Coggins

Proceedings Volume 6234, Automatic Target Recognition XVI; 62340G (2006) https://doi.org/10.1117/12.665798
Event: Defense and Security Symposium, 2006, Orlando (Kissimmee), Florida, United States

Abstract

This study is an initial investigation into the efficacy of texture operators for detection of military vehicle targets-in-the-clear in SAR imagery. The specific study is a very simple problem that aims to evaluate a particular feature set that arises in an approach to computer vision called spatial spectroscopy. Spatial spectroscopy begins by partitioning the image's spatial (Fourier) spectrum using a bank of filters. The filters compute a multiscale, truncated Taylor Series expansion at each pixel. Suitably extended on generic images, this feature space is capable of producing a unique pattern describing each pixel. The objective, of course, is not to uniquely distinguish each pixel but to form groups of pixels corresponding to targets in SAR that are distinct from background pixels. Thus, nonlinear operators are required to fold, twist, and bend the feature space in ways that cause pixels that make up targets to group together. The particular nonlinear operators for a study depend on the invariances and equivariances of the problem. In the present case, a large suite of operators is applied to the image data and principal discriminant analysis is used to select the most relevant features. Texture operators are found to be effective at discriminating targets from background.

Citation Download Citation

James M. Coggins "Target detection using texture operators", Proc. SPIE 6234, Automatic Target Recognition XVI, 62340G (18 May 2006); https://doi.org/10.1117/12.665798

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KEYWORDS

Spectroscopy

Image filtering

Target detection

Convolution

Visualization

Gaussian filters

Imaging spectroscopy

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