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The increased amount of information measured by fully polarimetric synthetic aperture radar (SAR) give additional knowledge about ground scatterers. Making the best use of the polarimetric information is crucial for target detection, among 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 has 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 article is twofold: 1) to give a brief review of polarimetric features usually used for target detection, and 2) to combine them optimally for vehicle detection in open fields, in large natural scenes. The study's backbone is a large airborne dataset 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 fields 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 the X-band, the total received power is one of the best predictive parameters 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 $\bm{S_{hh}} \bm{S}_{\bm{hv}}^{*}$, $\bm{S_{vv}} \bm{S}_{\bm{hv}}^{*}$, 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 with 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 man-made 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.

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 ${\langle \boldsymbol{S}_{hh}\boldsymbol{S}_{hv}^*\rangle }$, ${\langle \boldsymbol{S}_{vv}\boldsymbol{S}_{hv}^*\rangle }$, 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 man-made 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.