基于时间序列的InSAR相干性量级估计

Abstract

本文提出了一种适用于InSAR数据处理的自适应相干性量级估计方法,该方法能够满足复信号随机平稳的假设前提,并兼顾运算效率与估计精度.此方法生成的相干图具有很好的分布特征,避免了影像空间分辨率的损失.提出的算法分为两个步骤:(1)根据地物后向散射特性,对时间序列SAR影像进行聚类分析,选择具有同分布的样本,保证SAR影像质地平稳条件;(2)对干涉图进行条纹频率估计,采用极大似然(ML)条纹频率估计方法去除系统相位引起的复信号非平稳性,并根据Cramer-Rao边界条件改善条纹频率的估计精度.以美国南加州洛杉矶地区的ENVISAT ASAR数据集为例,本文将新方法与现有方法进行了量化分析.结果表明,较传统方法而言,基于时间序列的相干性估计方法能够得到更可靠、精度更高、空间特征更鲜明的干涉相干图. In this paper,we present a novel approach for accurate coherence estimation,based on InSAR data stack.The main advantage of the proposed method is to meet the assumptions that the complex signals are local stationary,and meanwhile take into account the computational efficiency.Therefore,it is possible to obtain a very accurate coherence estimate without loss of resolution.Concretely,two-step is applied to adaptive algorithm:(1) nonparametric hypothesis test is firstly employed to cluster pixels with same statistical distributions;(2) the modified version of maximum likelihood fringe rate estimate is then used to eliminate the non-stationarity of complex signals.The accuracy of such estimation is improved by Cramer-Rao bounds.Experimental results with Envisat ASAR datasets over Los Angeles areas show that the new method performs well under different situations.