基于经验模态分解(EMD),提出了一种改进的永久散射体(PS)探测方法。对干涉图进行多尺度分解,基于梯度的自适应滤波对分解的本征模函数中的噪声进行低信噪比区域强滤波和高信噪比区域弱滤波,估算滤波后各PS候选(PSC)点的噪声相位。基于各PSC点的幅度和相位稳定性,对选取的PSC点的相位信息进行分析,判断其作为PS点的概率,进而选取可靠的PS点。实验结果表明:与传统的PS点选取方法相比,所提方法避免了探测过程中PS点的误判和漏判,准确性更高。

In this study, an improved method based on empirical mode decomposition is proposed for the detection of persistent scatterers (PSs). The interferograms are decomposed from different angles, and noises contained in the decomposed intrinsic mode function (IMF) are strongly filtered in the low signal-to-noise ratio regions and weakly filtered in the high signal-to-noise ratio regions based on gradient adaptive filtering; the noise phase of each persistent scatterer candidate (PSC) point is estimated after filtering. Based on the stability of the amplitude and the phase of each PSC point, the phase information of the selected PSC point is analyzed to determine its probability of being a PS point, and the reliable PS points are selected. The experimental results denote that the proposed method avoids misjudgment and omission possibilities in the process of PS point detection with higher accuracy when compared to that exhibited by the traditional PS point detection method.