Increasing the discrimination of SAR recognition models
Bir Bhanu, Grinnell Jones · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2001
The focus of this paper is optimizing recognition models for Synthetic Aperture Radar signatures of vehicles to improve the performance of a recognition algorithm under the extended operating conditions of target articulation, occlusion and configuration variants. The recognition models are based on quasi-invariant local features, scattering center locations and magnitudes. The approach determines the similarities and differences among the various vehicle models. Methods to penalize similar features or reward dissimilar features are used to increase the distinguishability of the recognition model instances. Extensive experimental recognition results are presented in terms of confusion matrices and receiver operating characteristic curves to show the improvements in recognition performance for MSTAR vehicle targets with articulation, configuration variants and occlusion.