Selecting training images with support vector machines for composite correlation filters in SAR ATR

Daniel W. Carlson; Jack G. Riddle; Donald E. Waagen

doi:10.1117/12.487433

12 September 2003 Selecting training images with support vector machines for composite correlation filters in SAR ATR

Daniel W. Carlson, Jack G. Riddle, Donald E. Waagen

Proceedings Volume 5095, Algorithms for Synthetic Aperture Radar Imagery X; (2003) https://doi.org/10.1117/12.487433
Event: AeroSense 2003, 2003, Orlando, Florida, United States

Abstract

The focus of this paper is a genetic algorithms based method to automate the construction of local feature based composite class models that capture the salient characteristics of configuration variants of vehicle targets in SAR imagery and increase the performance of SAR recognition systems. The recognition models are based on quasi-invariant local features, SAR scattering center locations and magnitudes. The approach uses an efficient SAR recognition system as an evaluation function to determine the fitness of candidate members of a genetic population of new models and synthetically generates composite class models that are more similar to existing configurations than those configurations are to each other. Intuitively, specific features of models of versions A and B of an object may not match, because they are outside of some tolerance, while they may both match some synthetic version C that is somewhere in the middle. Experimental recognition results are presented in terms of receiver operating characteristic (ROC) curves to show the improvements in SAR recognition performance utilizing composite class models of configuration variants of MSTAR vehicle targets.

Citation Download Citation

Daniel W. Carlson, Jack G. Riddle, and Donald E. Waagen "Selecting training images with support vector machines for composite correlation filters in SAR ATR", Proc. SPIE 5095, Algorithms for Synthetic Aperture Radar Imagery X, (12 September 2003); https://doi.org/10.1117/12.487433

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