The high sample density and rich feature set of 3D SAR makes high performance target recognition possible even in noisy environments. The recognition performance with 3D imaging is examined for full target templates and for feature decompositions of the target. Spherical SAR with di§erent Fourier-based focusing across di§erent frequencies provides 3D image volumes that can be further segmented to reveal component surfaces of the target. The sparse clustering of distinct target components and the high adjacent voxel density in these cluster add to increase performance in 3D object understanding. Robust recognition depends on how well these templates and features perform across di§erent noise levels. Target templates consist of multiple orthogonal views of the target. The components in each view can then be further segmented into points of high reáection (point scatterers), like-oriented surfaces, edge or surface clusters, or even di§erent surface roughness components. Recognition then consists of either a whole target template or a feature aggregated target model which are both similar to the noise-free image data but di§erent enough from the image data of dissimilar targets. This approach leads to more heuristic recognition algorithms but also leads to a more detailed understanding of the target and its most distinct features. This may lead to better target understanding, as well as, an ability to identify any feature variations from the originally imaged target model. Alternative recognition approaches or even this approach may be automated to apply to more generic 3D imaged objects, but key to a more detailed understanding is the ability to determine why or what features led to the identiÖcation.
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