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Reflection of and vision for the decomposition algorithm development and application in earth observation studies using PolSAR technique and data
Remote Sensing of Environment ( IF 13.5 ) Pub Date : 2021-05-13 , DOI: 10.1016/j.rse.2021.112498
Dingfeng Duan , Yong Wang

After reflecting on the past decomposition studies using the polarimetric synthetic aperture radar (PolSAR) technique and data in Earth observation (EO) studies, three primary issues are identified. Elements C12 and C32 of a covariance matrix, [C], are essential in the decomposition and cannot be ignored. Existing algorithms cannot adequately distinguish urban targets with large azimuth orientation angles from vegetation. The algorithms are complex in the formulation and procedure. To resolve the issues and envision future algorithm development, we have articulated three key modules. They are a separation factor to separate an azimuthally symmetric or asymmetric target, a diplane to model an asymmetric target in an urban area, and a procedure to derive an equivalent azimuth-orientation angle for the diplane. Then, a four-component decomposition algorithm was developed. The algorithm has been applied to multiple airborne and spaceborne PolSAR C- and L-band datasets covering areas in Canada, France, Morocco, and the USA. The primary radar target types included trihedral and dihedral corner reflectors (CRs), airport runway/taxiway, urban targets with azimuth-orientation angles ranging between 0° and 45°, ocean and inland water surfaces, city parks, grassland, forests, farmland, and desert. The separation factor delineates a symmetric or asymmetric target at a correct average rate of 92.7%. The diplane coupled with the derived equivalent azimuth-orientation angles correctly modeled radar backscatter from dihedral CRs and urban asymmetric targets. The algorithm delineated each type of ground target with an appropriate and dominant single, double, or volume scattering. Furthermore, the algorithm has four readily interpretable components, and its mathematical expression is not complicated. Therefore, the primary objectives to resolve the above three issues and to have an algorithm with well-balanced usability in EO studies and complexity in formulation and procedure are achieved.



中文翻译:

分解算法的发展与应用PolSAR技术和数据在地球观测研究中的反思与展望

在回顾了过去使用极化合成孔径雷达(PolSAR)技术进行的分解研究和对地观测(EO)研究中的数据后,确定了三个主要问题。元件Ç 12Ç 32的协方差矩阵的,[ C ^],在分解中必不可少,因此不能忽略。现有的算法不能充分地将具有大方位角的城市目标与植被区分开。该算法在制定和程序方面很复杂。为了解决这些问题并展望未来的算法开发,我们阐明了三个关键模块。它们是用于分离方位对称或非对称目标的分离因子,用于模拟市区中非对称目标的双平面以及用于导出双平面等效方位角的过程。然后,开发了一种四分量分解算法。该算法已应用于覆盖加拿大,法国,摩洛哥和美国地区的多个机载和机载PolSAR C和L波段数据集。雷达的主要目标类型包括三面和二面角反射器(CR),机场跑道/滑行道,方位角在0°至45°之间的城市目标,海洋和内陆水面,城市公园,草地,森林,农田,和沙漠。分离因子以正确的平均比率92.7%描绘出对称或不对称的目标。双平面与导出的等效方位角相结合,可以正确地模拟二面角CR和城市非对称目标的雷达反向散射。该算法使用适当且占优势的单,双或体积散射来描绘每种类型的地面目标。此外,该算法具有四个易于解释的组成部分,其数学表达式并不复杂。所以,

更新日期:2021-05-13
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