Quadratic distance classifier for multiclass SAR ATR using correlation filters

Abhijit Mahalanobis; Arthur V. Forman Jr.; Mark Roger Bower; Nathalie Day; Rich F Cherry

doi:10.1117/12.145517

28 May 1993 Quadratic distance classifier for multiclass SAR ATR using correlation filters

Abhijit Mahalanobis, Arthur V. Forman Jr., Mark Roger Bower, Nathalie Day, Rich F Cherry

Proceedings Volume 1875, Ultrahigh Resolution Radar; (1993) https://doi.org/10.1117/12.145517
Event: OE/LASE'93: Optics, Electro-Optics, and Laser Applications in Scienceand Engineering, 1993, Los Angeles, CA, United States

Abstract

The recognition of targets in synthetic aperture radar (SAR) imagery using a quadratic classifier is proposed. Correlators are used to compute distances under an optimum transform to measure similarity between ideal reference images and the actual data. The transform is a filter which responds to features specifically useful for discrimination. This is attractive for model based training since only a similarity in features is required between the actual images and their class models rather than a precise match in pixel values. The quadratic terms are unaffected by shifting of the input image while linear terms are computed using shift-invariant correlation. The system is thus non-linear but shift-invariant. Specifically, 'distance' vectors are generated by the filter banks which are analyzed by a rudimentary rule base to determine whether the input is a target image or clutter. In this paper, we describe a SAR automatic target recognizer (ATR) with results for 3 and 5 class problems. the data used is actual SAR imagery of military targets.

Citation Download Citation

Abhijit Mahalanobis, Arthur V. Forman Jr., Mark Roger Bower, Nathalie Day, and Rich F Cherry "Quadratic distance classifier for multiclass SAR ATR using correlation filters", Proc. SPIE 1875, Ultrahigh Resolution Radar, (28 May 1993); https://doi.org/10.1117/12.145517

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