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Hybrid-polarimetric architecture may become a new option for spaceborne quadrature-polarimetric (quad-pol) synthetic aperture radar (SAR) mainly due to the range ambiguity improvement without loss of polarization information. To inherit the analytical methods developed for the conventional quad-pol SAR data, it is necessary to transform the hybrid quad-pol SAR data into a linear polarimetric basis. However, this operation will result in deterioration of azimuth ambiguity performance. The theoretical analysis on range and azimuth ambiguities in quad-pol SAR has been relatively complete, which is also summarized in this article. Yet, there is no real data to verify the equivalence of polarization information and to compare the ambiguity levels. Based on P-band airborne experiment, real hybrid and conventional quad-pol SAR data are used for comparative verification. In addition, considering that polarimetric target decomposition is often used in the practical applications, we suggest a new perspective from target decomposition for the ambiguities in quad-pol SAR. Identical polarimetric processing results for two sets of comparative data verify that hybrid quad-pol SAR data are indistinguishable from conventional quad-pol SAR data. The demonstrated intensity images and decomposed RGB images with azimuth ambiguities, acquired by the two quad-pol modes, completely conform to the theory. [ABSTRACT FROM AUTHOR]

Polarimetric synthetic aperture radar (SAR) has been extensively used in various remote sensing applications. In this article, a unified framework is designed to compare the classification performance of different polarimetric systems, which include quad-polarimetric (QP), compact-polarimetric (CP), and dual-polarimetric (DP). To avoid problems, such as the lack of uniform standards in feature extraction, the classification algorithm is directly based on the statistical characteristics of the coherency/covariance matrix and is implemented by extending the Wishart mixture model (WMM). The GF-3 data set in San Francisco and the AIRSAR agricultural data set in Flevoland are used in the experiment, and the following conclusions are generated. QP can achieve the highest classification accuracy in all classification tasks. When distinguishing three typical classes (water, urban, and vegetation) with very different scattering characteristics, the performance of different polarimetric systems is similar, and QP has only a slight advantage. For classification tasks of different classes with similar scattering characteristics, CP performs better in agricultural scenes, and the overall accuracy (OA) is only reduced by 3%–4% compared with QP. DP performs better in urban scenes, and OA is only reduced by 1%–3% compared with QP. These conclusions can provide guidance for future payloads’ design and the choice of polarimetric operation mode for existing multi-polarimetric SAR systems to achieve the purpose of giving full play to the advantages of different polarimetric systems. [ABSTRACT FROM AUTHOR]