In this contribution, we investigate PAZ co-polarimetric SAR data applicability for surface movement mapping and scattering characterization. PAZ simultaneously collects SAR imagery in both VV and HH channels. Using a small stack of PAZ data, we apply the real-valued impulse response function correlation to identify constantly coherent scatterers (CCS), separately in VV and HH, in the course of time series InSAR (Interferometric SAR) processing. The proposed method has an advantage to selecting the CCS with minimal incoherent scatterer inclusion and exact radar location, which can eventually lead to the precise deformation time series estimations of all CCS, and a high-precision surface movement map. Moreover, we apply the co-polarimetric phase difference (CPD) method to classify the CCS in terms of scattering mechanisms which provides a new attribute to every individual CCS. We recognize the sibling pairs by both thresholding the spatial distance between any two CCS observed separately in VV and HH, and using common scattering characteristic as a new criterion. The deformation estimates of sibling pairs are used to reduce the biases in the deformation estimates of every ground target. The proposed methods are demonstrated in a test site, in the northern part of the Netherlands, using 10 co-polarimetric SAR data acquired between September 2019 and April 2020. The results show that $83.5\%$ sibling pairs behave a linear deformation trend over time, and that the other pairs show a correlation between their deformation and temperature, and the sibling pairs with the surface, dihedral, volume scattering mechanisms account for $62\%,12\%$ and $26\%$, respectively. We conclude that by combining data from VV and HH polarization as siblings, PAZ co-polarimetric SAR data are highly suited to map surface changes and characterize surface features.

在这项贡献中，我们研究了PAZ同极化SAR数据在表面运动测绘和散射特征分析中的适用性。PAZ同时在VV和HH通道中收集SAR图像。我们使用一小堆PAZ数据，应用实值脉冲响应函数相关性，以在时间序列InSAR（干涉SAR）处理过程中分别在VV和HH中识别恒定相干散射体（CCS）。所提出的方法具有选择具有最小非相干散射体夹杂物和精确雷达位置的CCS的优势，这最终可以导致对所有CCS进行精确的变形时间序列估计，并获得高精度的表面运动图。此外，我们使用共极化相位差（CPD）方法根据散射机制对CCS进行分类，这为每个单独的CCS提供了新的属性。我们通过对在VV和HH中分别观察到的任意两个CCS之间的空间距离进行阈值化，并使用共同的散射特性作为新的标准，来识别兄弟对。兄弟对对的变形估计用于减少每个地面目标的变形估计中的偏差。使用在2019年9月至2020年4月之间获得的10个同极化SAR数据，在荷兰北部的一个测试地点演示了所建议的方法。结果表明，并使用共同的散射特性作为新标准。兄弟对对的变形估计用于减少每个地面目标的变形估计中的偏差。使用在2019年9月至2020年4月之间获得的10个同极化SAR数据，在荷兰北部的一个测试地点演示了所建议的方法。结果表明，并使用共同的散射特性作为新标准。兄弟对对的变形估计用于减少每个地面目标的变形估计中的偏差。使用在2019年9月至2020年4月之间获得的10个同极化SAR数据，在荷兰北部的一个测试地点演示了所建议的方法。结果表明，$83.5％$ 兄弟姐妹对随时间呈线性变形趋势，而其他对则表现出它们的变形与温度之间的相关性，并且兄弟姐妹对具有表面，二面体，体积散射机制 $62％，12％$ 和 $26％$， 分别。我们得出结论，通过将来自VV和HH极化的数据合并为同级，PAZ共极化SAR数据非常适合于绘制表面变化并表征表面特征。