Composite class models for SAR recognition

This paper focuses on a genetic algorithm based method that automates the construction of local feature based composite class models to 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. Experimental results are given on the fitness of the composite models and the similarity of both the original training model configurations and the synthesized composite models to the test configurations. In addition, results are presented to show the SAR recognition performance and pose accuracy for training models and composite class models of configuration variants of MSTAR vehicle targets.