永久散射体识别是用PS-InSAR方法获取地面沉降数据的关键环节之一，其最佳阈值的设定直接影响PS点的精度和密度。本文基于大数据统计的分析方法——ROC曲线法定量分析和确定PS点识别的最佳阈值。选择3种典型的PS识别方法，绘制每种方法在不同阈值条件下的ROC曲线图，当ROC曲线下面积越大，方法越优。依据最佳阈值位于ROC曲线左上部位的特征，采用敏感度与特异度之和（Se+Sp）最大的方法可定量判定出最佳阈值的取值。在最佳阈值条件下，识别的PS点具有正选率足够高、误选率足够小，PS点的密度足够大的特性。为进一步验证该方法的可行性，本文以北京龙潭公园地区为PS识别的实验区，用振幅离差指数法、相关系数法，以及两种阈值相结合的双阈值法3种方法进行实验识别PS点，并根据ROC曲线判得3种方法的最佳阈值。研究结果发现：（1）振幅离差T_D识别PS点的最佳阈值为0.45；相干系数Tγ识别PS点的最佳阈值为0.45；振幅离差T_D、相干系数Tγ双重阈值识别PS点的最佳阈值（T_D，Tγ）为（0.50，0.50）。（2）用振幅离差T_D和相干系数Tγ双阈值法识别PS点，得到的ROC曲线下面积AUC=0.762，高于单阈值法，表明双重阈值法识别PS点优于单一阈值的PS点识别方法。研究表明ROC曲线可定量化确定PS点的最佳阈值，而且该方法可进一步推广于GIS空间分析、遥感解译过程中阈值的定量化筛选。

Permanent Scatterer (PS) identification is a key step in the PS-InSAR technology, which is mainly used for obtaining ground subsidence data. The density and accuracy of the PS points are determined by setting the optimal threshold of PS identification. Receiver Operating Characteristic (ROC) curve is used to quantitatively analyze and determine the optimal threshold for PS identification.The ROC curve is drawn with some thresholds of every PS identification algorithm. According to the ROC curve, the larger area under the ROC curve indicates that the PS recognition method is more reliable. When the area under the ROC curve is sufficiently large, the optimal threshold of PS identification, which is the closest to the upper left of the ROC curve, is determined quantitatively according to the maximum sum of the sensitivity and specificity of the ROC curve. The positive ratio of PS points is sufficiently high, the false positive ratio of PS points is sufficiently low, and the density of the PS point is sufficient, using the optimal threshold.The PS points are identified with 60 X-band TerraSAR-X images (2010—2017) by three algorithms as amplitude dispersion (T_D), correlation coefficient (T_γ), and dual-threshold (T_D, T_γ) with amplitude dispersion index (ADI) and correlation coefficient index (CCI). The experimental area is approximately Beijing Longtan Park. First, three ROC curves are drawn separately with the algorithms ADI, CCI, and dual-threshold. Second, the optimal thresholds of every algorithm have been calculated according to the maximum sum of the sensitivity and specificity of ROC curve. Research found that: (1) the optimal threshold of ADI is T_D=0.45; the optimal threshold of CCI is T_γ=0.45; the optimal threshold of dual-threshold of ADI and CCI is (T_D, T_γ) = (0.50, 0.50). (2) The area under the ROC curve of dual threshold is AUC=0.762, which is higher than the AUC of the single threshold algorithm, such as ADI and CCI. Evidently the dual-threshold algorithm is much better than the single threshold of ADI or CCI to identify the PS points.Result of this research shows that the ROC curve can not only quantitatively determine the optimal threshold of PS identification, but can also be further applied for the quantitative selection of the thresholds during GIS spatial analysis and remote sensing image interpretation.