摘要翻译：
多程SAR干涉测量（InSAR）最独特的优点是能够在大范围内反演长期的地球物理参数，如线性形变率。最近，在文献[1]中提出了一种基于对象的多通道InSAR框架，作为典型的单像素方法（如持久散射体干涉法(PSI)）或基于像素簇的方法（如Squeesar）的替代方案。这使得能够在对象级别上利用InSAR相位堆栈的固有特性。在此基础上，研究了相位张量固有的低秩特性，提出了一种基于对象的低秩张量分解(RoMIO)的鲁棒多程InSAR技术。我们证明，滤波InSAR相位叠加可以在典型环境下将常规多程InSAR技术（如PSI）估计的地球物理参数的精度提高10～30倍。提出的方法特别有效地对抗离群点，如具有未建模相位的像素。这些优点反过来可以有效地减少可靠估计所需的图像数量。利用高分辨率TerraSAR-X图像堆验证了该方法的良好性能。
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英文标题：
《Object-based Multipass InSAR via Robust Low Rank Tensor Decomposition》
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作者：
Jian Kang, Yuanyuan Wang, Michael Schmitt and Xiao Xiang Zhu
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最新提交年份：
2018
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Image and Video Processing        图像和视频处理
分类描述：Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.
用于图像、视频和多维信号的形成、捕获、处理、通信、分析和显示的理论、算法和体系结构。感兴趣的主题包括：数学，统计，和感知图像和视频建模和表示；线性和非线性滤波、去模糊、增强、恢复和重建退化、低分辨率或层析数据；无损和有损压缩编码；分割、对齐和识别；图像渲染、可视化和打印；计算成像，包括超声、断层和磁共振成像；以及图像和视频的分析、合成、存储、搜索和检索。
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英文摘要：
  The most unique advantage of multipass SAR interferometry (InSAR) is the retrieval of long term geophysical parameters, e.g. linear deformation rates, over large areas. Recently, an object-based multipass InSAR framework has been proposed in [1], as an alternative to the typical single-pixel methods, e.g. Persistent Scatterer Interferometry (PSI), or pixel-cluster-based methods, e.g. SqueeSAR. This enables the exploitation of inherent properties of InSAR phase stacks on an object level. As a followon, this paper investigates the inherent low rank property of such phase tensors, and proposes a Robust Multipass InSAR technique via Object-based low rank tensor decomposition (RoMIO). We demonstrate that the filtered InSAR phase stacks can improve the accuracy of geophysical parameters estimated via conventional multipass InSAR techniques, e.g. PSI, by a factor of ten to thirty in typical settings. The proposed method is particularly effective against outliers, such as pixels with unmodeled phases. These merits in turn can effectively reduce the number of images required for a reliable estimation. The promising performance of the proposed method is demonstrated using high-resolution TerraSAR-X image stacks.
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PDF链接：
https://arxiv.org/pdf/1801.07499