This paper proposes a method to detect buildings and houses whose walls are not parallel to Synthetic Aperture Radar (SAR) flight path. Experimental observations show that it is difficult to detect these targets because of small backscattering characteristics. The detection method is based on the correlation coefficient in the circular polarization basis, taking full advantage of Polarimetric SAR (POLSAR) data. Since the correlation coefficient is real-valued for natural distributed targets with reflection symmetry and for non-natural targets orthogonal to illumination direction, and it becomes a complex number for non-natural targets aligned not orthogonal to radar Line-Of-Sight (LOS), the value seems to be an effective index for detection of obliquely aligned non-natural targets. The detection results are shown using the X-band Polarimetric and Interferometric SAR (Pi-SAR) single-path data set in conjunction with other polarimetric indices.

This paper takes full advantage of polarimetric scattering parameters and total power to classify polarimetric SAR image data. The parameters employed here are total power, polarimetric entropy, and averaged alpha angle (alphabar). Since these parameters are independent each other and represent all the scattering characteristics, they seem to be one of the best combinations to classify Polarimetric Synthetic Aperture Radar (POLSAR) images. Using unsupervised classification scheme with iterative Maximum Likelihood classifier, it is possible to decompose multi-look averaged coherency matrix with complex Wishart distribution effectively. The classification results are shown using Pi-SAR image data set comparing with other representative methods.