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De-noising method of InSAR data based on empirical mode decomposition and land deformation monitoring application

机译:基于经验模态分解和土地变形监测的InSAR数据降噪方法

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The applicable choice of filters for InSAR is one of the key procedures, which is associated with the quality of interferogram. The data of InSAR interferogram was decomposed by empirical mode decomposition (EMD). A given signal was decomposed into different Intrinsic Mode Functions (IMFs) filled with the condition. Further investigation of the algorithm is demonstrated below with regard to the multi-resolution standpoint. Empirical mode decomposition includes two operators. The IMF calculation operator and residual calculation operator define the process of similar to high frequency and low frequency filters. Then, the multi-solution structure is realized by decomposing the low frequency step by step. Therefore, the filtered noise-related IMFs together with the other IMFs can be used to restructure the denoised signal. The processing result has confirmed this method feasibility. Comparing the empirical mode decomposition with the general methods, such as median filter, Lee filter, Goldstein filter, using the quantitative evaluation index, i.e., standard deviation (STD) and equivalent number of looks (ENL), the result shows that empirical mode decomposition is powerful to interferogram speckle noise suppression and residues reduction, as well as it can be preserved details information. The method proposed can improve the accuracy of interferometric products.
机译:InSAR滤波器的适用选择是关键程序之一,它与干涉图的质量有关。 InSAR干涉图的数据通过经验模态分解(EMD)进行分解。给定信号被分解为充满该条件的不同本征模式函数(IMF)。关于多分辨率的观点,下面对该算法作了进一步的研究。经验模式分解包括两个运算符。 IMF计算运算符和残差计算运算符定义类似于高频和低频滤波器的过程。然后,通过逐步分解低频来实现多解决方案结构。因此,滤波后的与噪声相关的IMF与其他IMF一起可用于重构去噪信号。处理结果证实了该方法的可行性。将经验模态分解与中值滤波器,Lee滤波器,Goldstein滤波器等通用方法进行比较,并使用定量评估指标,即标准差(STD)和等效视数(ENL),结果表明,经验模态分解对干涉图斑点噪声抑制和残留减少有强大的作用,并且可以保留细节信息。所提出的方法可以提高干涉测量产品的精度。

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