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Calibration of Spaceborne Polarimetric SAR Data Using Particle Swarm Optimization

机译:基于粒子群算法的星载极化SAR数据标定

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摘要

A calibration method for spaceborne polarimetric SAR data using a particle swarm optimization (PSO) is discussed in this report. Recently some satellites which have polarimetric synthetic aperture radar were launched, and the polarimetric data analysis techniques are being developed for terrain classification, forest biomass and soil moisture estimations, etc. Thus, polarimetric calibration becomes an important issue for accurate polarimetric analysis. However, typical polarimetric calibration methods have some restrictions. For example, Freeman method requires the polarimetric data which satisfy reflection symmetry and does not estimate cross-talks. Thus, it is desired that a polarimetric calibration method is needed to estimate all polarimetric calibration parameters by using polarimetric data without considering reflection symmetry. In this report, a polarimetric calibration technique based on a particle swarm optimization is proposed. This proposed method can estimate cross-talks, channel imbalances and Faraday rotation angle using one trihedral corner reflector and the measured polarimetric SAR data with non-reflection symmetry.
机译:本报告讨论了使用粒子群优化(PSO)的星载极化SAR数据的校准方法。最近,发射了一些带有极化合成孔径雷达的卫星,并且正在开发极化数据分析技术以用于地形分类,森林生物量和土壤湿度估算等。因此,极化校准成为准确极化分析的重要问题。但是,典型的偏振校准方法有一些限制。例如,弗里曼方法需要满足反射对称性并且不估计串扰的极化数据。因此,期望需要一种偏振校准方法来通过使用偏振数据而不考虑反射对称性来估计所有偏振校准参数。在本报告中,提出了一种基于粒子群优化的极化校正技术。该方法可以利用一个三面角反射器和测得的具有非反射对称性的极化SAR数据来估计串扰,信道不平衡和法拉第旋转角度。

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