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The increased amount of information measured by fully polarimetric SAR give additional knowledge about ground scatterers. Making the best use of the polarimetric information is crucial for target detection, amongst other applications. Several representations of the data, such as polarimetric decompositions, have been proposed to summarize the information into polarimetric features. The relation between these features with physical properties of the scatterers have been studied in depth. The different approaches to target detection proposed make use of different polarimetric features and different properties of the targets. The goal of this paper is two-fold: to give a brief review of polarimetric features usually used for target detection, and to combine them optimally for vehicle detection in open field, in large natural scenes. The study's backbone is a large airborne data-set in X-, S-, and L-bands, in which several flights following different flight tracks were performed around a controlled area with a dozen vehicles. At first, a univariate study is performed to evaluate the contrast provided by individual polarimetric features between vehicles and different types of natural covers. Then, optimal subsets of polarimetric features for distinguishing vehicles in open field from natural cover are determined using random forest classifiers. The multivariate approach yielded better detection results for all wavelengths, but brought more significant improvement as the wavelength increases. At X-band, the total received power is one of the best predictive parameter for vehicle detection, while the scattering mechanism characterization becomes more important at S- and L-bands.

In polarimetric Synthetic Aperture Radar (SAR) images, speckle is removed by multilooking and the local covariance matrix is the main parameter of interest. In the covariance matrix from a backscatter with reflection symmetry, the terms hhShv>⟩, vvShv*>, and their complex conjugates are 0. The backscatter from natural covers, such as fields and forested areas is typically reflection symmetric, as these four elements have near zero values. The backscatter from urban areas and man-made structures is substantially different, and the backscatter from buildings not aligned with the radar line of sight usually does not have reflection symmetry. A novel block-diagonality test statistic for reflection symmetry with a Constant False Alarm Rate property is proposed. It is compared to an approximate test built on a change detection test statistic for Wishart distributed covariance matrices. Their use on quad-polarimetric data in different situations shows their high potential for manmade structure detection. Applied after an orientation correction of the covariance matrices, these test statistics highlight with high contrast buildings and urban areas. We also apply this test for ship detection at sea, and show that while the results are unconvincing at X-band, it can also be applied at longer wavelengths such as L-band.