基于卡尔曼滤波的PS-InSAR地表形变预测方法
发布时间：2019-07-19 18:15
【摘要】：PS-In SAR是用于监测大范围地表形变的微波遥感技术,可提供精确地表形变信息,但该技术无法对形变趋势进行预测。现有形变预测方法只能预测少数监测点的形变,不适用于大面积预测。针对这些问题,提出一种基于卡尔曼滤波的PS-In SAR地表形变预测方法。结合PS-In SAR方法的技术流程,从理论上推导设计卡尔曼滤波器,通过真实的多时相SAR数据对该方法进行验证。实验结果表明,该算法可充分利用PS-In SAR形变监测信息,有效预测大面积观测区域的形变趋势。
[Abstract]:PS-In SAR is a microwave remote sensing technology for monitoring large-scale surface deformation, which can provide accurate surface deformation information, but this technology can not predict the deformation trend. The existing deformation prediction methods can only predict the deformation of a few monitoring points, and are not suitable for large area prediction. In order to solve these problems, a PS-In SAR surface deformation prediction method based on Kalman filter is proposed. Combined with the technical flow of PS-In SAR method, the Kalman filter is deduced and designed theoretically, and the method is verified by real multi-temporal SAR data. The experimental results show that the algorithm can make full use of PS-In SAR deformation monitoring information to effectively predict the deformation trend of large area observation area.
【作者单位】： 中国科学院电子学研究所;中国科学院空间信息处理与应用系统技术重点实验室;中国科学院大学;
【基金】：中国科学院“百人计划”项目(Y53Z180390) 民政部国家减灾中心项目(8435-01)资助
【分类号】：TP722.6
本文编号：2516420
[Abstract]:PS-In SAR is a microwave remote sensing technology for monitoring large-scale surface deformation, which can provide accurate surface deformation information, but this technology can not predict the deformation trend. The existing deformation prediction methods can only predict the deformation of a few monitoring points, and are not suitable for large area prediction. In order to solve these problems, a PS-In SAR surface deformation prediction method based on Kalman filter is proposed. Combined with the technical flow of PS-In SAR method, the Kalman filter is deduced and designed theoretically, and the method is verified by real multi-temporal SAR data. The experimental results show that the algorithm can make full use of PS-In SAR deformation monitoring information to effectively predict the deformation trend of large area observation area.
【作者单位】： 中国科学院电子学研究所;中国科学院空间信息处理与应用系统技术重点实验室;中国科学院大学;
【基金】：中国科学院“百人计划”项目(Y53Z180390) 民政部国家减灾中心项目(8435-01)资助
【分类号】：TP722.6
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