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利用InSAR变形监测结果进行形变机理反演时,由于InSAR获取的数据点众多,且往往含有较多的误差乃至粗差点,严重制约了反演计算的效率和可靠性。为此,本文提出顾及InSAR变形监测数据的物理空间相关性来设立协方差函数,并依据协方差函数确定四叉树象限分解阈值和最大象限大小的自适应四叉树分解InSAR数据压缩算法。本算法能够在尽可能保留形变信号特征细节信息的同时,极大地降低InSAR数据量。论文以西安地区地面沉降InSAR形变监测结果为例进行了试验分析,验证了该算法的有效性。结果表明,该方法能够在不损失形变信号特征的同时,有效地实现InSAR数据压缩和噪声消除的目的。
Inversion of deformation mechanism using InSAR deformation monitoring results, due to InSAR many data points, and often contains more errors and even coarse points, which seriously hampered the efficiency and reliability of the inversion calculation. Therefore, this paper proposes an adaptive quadtree decomposition InSAR data compression algorithm that establishes the covariance function taking into account the physical spatial correlation of InSAR deformation monitoring data and determines the quadrant tree quadrant decomposition threshold and the maximum quadrant size according to the covariance function. The algorithm can keep the detail information of the deformation signal as much as possible while greatly reducing the InSAR data volume. In the paper, the case analysis of InSAR deformation monitoring of ground subsidence in Xi’an is carried out as an example to verify the effectiveness of the algorithm. The results show that this method can effectively achieve InSAR data compression and noise cancellation without losing the characteristics of deformation signals.