Summary
The focus of this chapter is on methods for improving the performance of a model-based system for recognizing vehicles in synthetic aperture radar (SAR) imagery under the extended operating conditions of object articulation, occlusion, and configuration variants. The fundamental approach uses recognition models based on quasi-invariant local features, radar scattering center locations, and magnitudes. Three basic extensions to this approach are discussed: (1) incorporation of additional features; (2) exploitation of a priori knowledge of object similarity represented and stored in the model-base; and (3) integration of multiple recognizers at different look angles. Extensive experimental recognition results are presented in terms of receiver operating characteristic (ROC) curves to show the effects of these extensions on SAR recognition performance for real vehicle targets with articulation, configuration variants, and occlusion.
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Bhanu, B., Jones, G. (2005). Methods for Improving the Performance of an SAR Recognition System. In: Bhanu, B., Pavlidis, I. (eds) Computer Vision Beyond the Visible Spectrum. Advances in Pattern Recognition. Springer, London. https://doi.org/10.1007/1-84628-065-6_2
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DOI: https://doi.org/10.1007/1-84628-065-6_2
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