Adaptive Covariance Matrix Estimation for Multi-Baseline InSAR Data Stacks
Adaptive Covariance Matrix Estimation for Multi-Baseline InSAR Data Stacks
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DOI：
10.1109/tgrs.2014.2303516
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发表时间：
2014-02
影响因子：
8.2
通讯作者：
M. Schmitt;Johannes L. Schönberger;Uwe Stilla
中科院分区：
文献类型：
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作者：
M. Schmitt;Johannes L. Schönberger;Uwe Stilla
对于合成孔径雷达（SAR）成像的许多多维应用，每个分辨率单元的协方差矩阵的估计是一个关键的处理步骤。这项工作的背景是多基线干涉SAR数据集的协方差矩阵估计的应用。为了确保局部平稳性，这是需要一个无偏的估计，自适应技术是必要的。在本文中，自适应协方差矩阵估计的一种新方法，提出和评估的基础上，从图像处理领域的措施。该过程是围绕检查是否相邻的像素属于相同的统计分布，作为当前调查的像素通过应用阈值的相应的概率密度函数的想法。然后使用所有内点像素来估计参考像素的复协方差矩阵。从该协方差矩阵中，提取幅度和干涉相位值，然后将其针对堆栈中的所有像素进行组合，以便采用通常用于图像去噪研究的滤波效率评估技术。实验结果表明，该算法在保持图像细节的同时，具有较高的滤波效率。除此之外，它被发现是特别适合于小尺寸的堆栈的共注册的SAR图像。
For many multidimensional applications of synthetic aperture radar (SAR) imaging, the estimation of the covariance matrix for each resolution cell is a critical processing step. The context of this work is the application of covariance matrix estimation for multi-baseline interferometric SAR data sets. In order to ensure local stationarity, which is needed for an unbiased estimation, adaptive techniques are necessary. In this paper, a new approach for adaptive covariance matrix estimation is proposed and evaluated based on measures known from the field of image processing. The procedure is centered around the idea of checking whether the neighboring pixels belong to the same statistical distribution as the currently investigated pixel by applying a threshold to the respective probability density function. All inlier pixels are then used to estimate the complex covariance matrix of the reference pixel. From this covariance matrix, both amplitude and interferometric phase values are extracted, which are then combined for all pixels in the stack in order to employ techniques for the evaluation of filtering efficiency that are typically used in image denoising research. It is found that the proposed algorithm provides high filtering efficiency and good detail preservation at the same time. Apart from that, it is found to be particularly suitable for small-sized stacks of coregistered SAR imagery.