Satellite-borne or aircraft-borne synthetic aperture radar (SAR) is useful for high resolution imaging analysis for terrain surface monitoring or surveillance, particularly in optically harsh environments. For surveillance application, there are various approaches for automatic target recognition (ATR) of SAR images aiming at monitoring unidentified ships or aircraft. In addition, various types of analyses for full polarimetric data have been developed recently because it can provide significant information to identify structure of targets, such as vegetation, urban, sea surface areas. ATR generally consists of two processes, one is target feature extraction including target area determination, and the other is classification. In this paper, we propose novel methods for these two processes that suit full polarimetric exploitation. As the target area extraction method, we introduce a peak signal-to noise ratio (PSNR) based synthesis with full polarimetric SAR images. As the classification method, the circular polarization basis conversion is adopted to improve the robustness especially to variation of target rotation angles. Experiments on a 1/100 scale model of X-band SAR, demonstrate that our proposed method significantly improves the accuracy of target area extraction and classification, even in noisy or target rotating situations.

Classification of terrain is one of the most important applications of Polarimetric Synthetic Aperture Radar (POLSAR) image analysis. This paper presents a simple method to classify terrain by the use of the correlation coefficients in the circular polarization basis together with the total power of the scattering matrix in the X-band. The reflection symmetry condition that the co-polarized and the cross-polarized correlations are close to zero for natural distributed scatterers is utilized to extract characteristic parameters of small forests or cluster of trees, and oriented urban building blocks with respect to the direction of the radar illumination. Both of these kinds of scatterers are difficult to identify in high resolution POLSAR images of complex urban areas. The indices employed here are the correlation coefficient, a modified coefficient normalized by the reflection symmetric conditional case, and the total power. It is shown that forest areas and oriented building blocks are easily detected and identified. The terrain classification yielded by these combinations is very accurate as confirmed by photographic ground truth images.

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.