A rectangular-fit classifier for synthetic aperture radar automatic target recognition

John A. Saghri; Daniel A. Cary

doi:10.1117/12.740236

24 September 2007 A rectangular-fit classifier for synthetic aperture radar automatic target recognition

John A. Saghri, Daniel A. Cary

Proceedings Volume 6696, Applications of Digital Image Processing XXX; 66961B (2007) https://doi.org/10.1117/12.740236
Event: Optical Engineering + Applications, 2007, San Diego, California, United States

Abstract

The utility of a rectangular-fit classifier for Synthetic Aperture Radar Automatic Target Recognition (SAR ATR) is examined. The target is fitted with and modeled as a rectangle that can best approximate its boundary. The rectangular fit procedure involves 1) a preprocessing phase to remove the background clutter and noise, 2) a pose detection phase to establish the alignment of the rectangle via a least squares straight line fitting algorithm, and 3) size determination phase via stretching the width and the height dimensions of the rectangle in order to encapsulate a pre-specified, e.g., 90%, of the points in the target. A training set composed of approximately half the total images in the MSTAR public imagery database are used to obtain and record the statistical variations in the width and height of the resulting rectangles for each potential target. The remaining half of the images is then used to assess the performance of this classifier. Preliminary results using minimum Euclidean and Mahalanobis distance classifiers show overall accuracies of 44% and 42%, respectively. Although the classification accuracy is relatively low, this technique can be successfully used in combination with other classifiers such as peaks, edges, corners, and shadow-based classifiers to enhance their performances. A unique feature of the rectangular fit classifier is that it is rotation invariant in its present form. However, observation of the dataset reveals that in general the shapes of the targets in SAR imagery are not fully rotation invariant. Thus, the classification accuracy is expected to improve considerably using multiple training sets, i.e., one training set generated and used for each possible pose. The tradeoff is the increased computation complexity which tends to be offset by ever increasing efficiency and speed of the processing hardware and software. The rectangular fit classifier can also be used as a pose detection routine and/or in conjunction with other ATR schemes, such as shadow-based ATR, that require an initial pose detection phase prior to matching.

Citation Download Citation

John A. Saghri and Daniel A. Cary "A rectangular-fit classifier for synthetic aperture radar automatic target recognition", Proc. SPIE 6696, Applications of Digital Image Processing XXX, 66961B (24 September 2007); https://doi.org/10.1117/12.740236

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