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Estimation of the Degree of Polarization for Hybrid/Compact and Linear Dual-Pol SAR Intensity Images: Principles and Applications

机译:混合/紧凑和线性双极化SAR强度图像的偏振度估计:原理和应用

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摘要

Analysis and comparison of linear and hybrid/compact dual-polarization (dual-pol) synthetic aperture radar (SAR) imagery have gained a wholly new importance in the last few years, in particular, with the advent of new spaceborne SARs such as the Japanese ALOS PALSAR, the Canadian RADARSAT-2, and the German TerraSAR-X. Compact polarimetry,udhybrid dual-pol, and quad-pol modes are newly promoted in the literature for future SAR missions. In this paper, we investigate and compare different hybrid/compact and linear dual-pol modes in terms of the estimation of the degree of polarization (DoP). The DoP has long been recognized as one of the most important parameters characterizing a partially polarized electromagnetic wave. It can be effectively used to characterize the information content of SAR data. We study and compare the information content of the intensity data provided by different hybrid/compact and linear dual-pol SAR modes. For this purpose, we derive the joint distribution of multilook SAR intensity images. We use thisuddistribution to derive the maximum likelihood and moment-based estimators of the DoP in hybrid/compact and linear dual-pol modes.We evaluate and compare the performance of these estimators for different modes on both synthetic and real data, which are acquired by RADARSAT-2 spaceborne and NASA/JPL airborne SAR systems, over various terrain types such as urban, vegetation, and ocean.
机译:线性和混合/紧凑型双极化(dual-pol)合成孔径雷达(SAR)图像的分析和比较在最近几年中已具有全新的重要性,特别是随着日本等新型星载SAR的出现ALOS PALSAR,加拿大RADARSAT-2和德国TerraSAR-X。紧凑极化法,双双极化和四极化模式在文献中得到了新的推广,可用于未来的SAR任务。在本文中,我们根据极化程度(DoP)的估计来研究和比较不同的混合/紧凑和线性双极化模式。长期以来,DoP被认为是表征部分极化电磁波的最重要参数之一。它可以有效地表征SAR数据的信息内容。我们研究并比较了由不同的混合/紧凑型和线性双极化SAR模式提供的强度数据的信息内容。为此,我们导出了多视SAR强度图像的联合分布。我们使用这种 uddistribution来推导混合/紧凑和线性双极化模式下DoP的最大似然和基于矩的估计量。我们评估和比较这些估计量在合成和实际数据上不同模式下的性能。由RADARSAT-2星载和NASA / JPL机载SAR系统采集,覆盖了各种地形类型,例如城市,植被和海洋。

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