Polarimetric SAR characterization of man-made structures in urban areas using normalized circular-pol correlation coefficients

Polarimetric Synthetic Aperture Radar (SAR) backscatter from man-made structures in urban areas is quite different than backscatter from predominantly natural areas. Backscatter from natural areas is often reflection symmetric; i.e., characterized by near zero values for covariance matrix off-diagonal terms of the form < SHVSHH*>, < SHVSVV*> and their conjugates. A new approach is proposed to detect scattering from non-reflection symmetric structures using circular-pol, RR-LL, correlation coefficients, vertical bar rho vertical bar. This method creates a normalization term, vertical bar rho(0)vertical bar, and then forms a ratio, vertical bar rho vertical bar/vertical bar rho(0)vertical bar. The normalization term, vertical bar rho(0)vertical bar, contains the same diagonal terms of the covariance matrix. The (SHVSHH*) and (SHVSVV*) off-diagonal terms and their conjugates are purposely set to zero. The ratio, vertical bar rho vertical bar/rho(0)vertical bar, is rewritten as a product of separable helicity (tau) and orientation angle (theta) dependencies. The mathematical form of the tau dependence is a resonant singularity, or pole, term. This pole significantly enhances returns from man-made, high helicity, non-reflection symmetric structures. These structures have values of tau near the resonance value at tau = +/- 1. Natural scatterers possess very strong RR/LL symmetry (tau approximate to 0) and the pole response for them is correspondingly weak. The dependence of vertical bar p vertical bar/vertical bar p(0)vertical bar on the orientation angle (theta) is known from previous studies to be useful for measuring urban building alignments (relative to the azimuth direction) and measuring surface topography. The ratio vertical bar p vertical bar/vertical bar p(0)vertical bar reduces much of the un-needed image detail of backscatter variations from natural areas of different surface roughness. This image simplification further facilitates detection of localized man-made targets. Published by Elsevier Inc.