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In this paper, the feasibility of scattering mechanism identification from limited synthetic aperture radar (SAR) data is investigated for civilian vehicles. Wide-angle 3D SAR imaging is considered with limitations in both frequency/look-angle and polarization samples. Accordingly, the two main problems, namely, sparse reconstruction of wide-angle data and identification of scattering mechanisms using two polarization channels, are jointly addressed. A methodology that involves compressed-sensing (CS) imaging, processing of sub-aperture images, and application of  H /&#x1FB1; decomposition to dual-circular polarization (DCP) mode is proposed. The 2D and 3D maps of entropy ( H ) and alpha-angle (&#x1FB1;) parameters and  H /&#x1FB1; classification results are evaluated by using simulation and the real GOTCHA dataset. The approach is tested with a complementary situation that consists of back-projection (BP) imaging of complete data plus decomposition of full-polarimetric (FP) data. A good correlation between full-available and most-limited cases, i.e., BP-FP vs. CS-DCP, is observed especially for the &#x1FB1; signatures. The results indicate reasonably accurate retrieval of canonical mechanisms from a very small subset, i.e., around 0.23% of total samples of each DCP channel.

Application of H /ᾱ Decomposition to Limited and Dual-Polarimetric 3D SAR Data of Civilian Vehicles