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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing >Adaptive Covariance Matrix Estimation for Multi-Baseline InSAR Data Stacks
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Adaptive Covariance Matrix Estimation for Multi-Baseline InSAR Data Stacks

机译：多基线InSAR数据堆栈的自适应协方差矩阵估计

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摘要

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.
机译：对于合成孔径雷达（SAR）成像的许多多维应用程序，每个分辨率单元的协方差矩阵的估计是关键的处理步骤。这项工作的背景是协方差矩阵估计在多基线干涉SAR数据集中的应用。为了确保无偏估计所需的局部平稳性，自适应技术是必需的。本文提出了一种新的自适应协方差矩阵估计方法，并基于图像处理领域中已知的措施对其进行了评估。该过程围绕以下思想：通过将阈值应用于相应的概率密度函数来检查相邻像素是否属于与当前研究的像素相同的统计分布。然后，所有内部像素都用于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，然后将其相结合以用于堆栈中的所有像素，以便采用通常用于图像降噪研究的滤波效率评估技术。发现所提算法同时具有较高的滤波效率和良好的细节保存性。除此之外，发现它特别适用于小尺寸的共同注册的SAR图像堆栈。

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