提出了一种干涉合成孔径雷达(InSAR, Synthetic Aperture Radar Interferometry)回波信号时间去相关分析的新方法,该方法主要包括三个步骤：采用自适应区域增长算法(IDAN, Intensity-Driven Adaptive Neighborhood)估计所有干涉子集的相干性；利用迭代最小二乘去除估计量偏差；采用相干性分解技术对无偏样本相干性进行分离,获得精确的时间去相关分量.以美国南加州洛杉矶地区的ENVISAT ASAR数据集为例,对新方法和现有方法进行了比较研究.结果表明,新的融合算法能够获得更加可靠、精度更高的时间去相关分量,并具有非阈值和近乎完全的自适应性.本文的研究将有利于改善与时间相干性有关的地球物理参数反演,也有利于地表变化周期性和随时间变化的气候环境实时监测等.

We presented a novel approach for accurate temporal coherence decorrelation analysis of InSAR echo signal. The proposed algorithm is divided into three steps: Firstly, using the modified Intensity-Driven Adaptive Neighborhood (IDAN) algorithm to estimate interferometric coherence; then using least squares fitting to remove deviation; finally, obtaining accurate temporal coherence decorrelation component with coherence decomposition technique by separating approximate unbiased coherence. We analyzed the ENVISAT ASAR data from the area of Los Angeles with the new method. The result was compared with the existing methods. The result shows that the new fusion algorithm is able to obtain more reliable and accurate temporal coherence decorrelation component. Meanwhile, the characteristics for the new method of non-threshold and almost adaptive performance were proved.