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New Approaches for Robust and Efficient Detection of Persistent Scatterers in SAR Tomography

机译:SAR断层成像中持久散射体的鲁棒高效检测新方法

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摘要

Persistent scatterer interferometry (PSI) has the ability to acquire submeter-scale digital elevation model (DEM) and millimeter-scale deformation. A limitation to the application of PSI is that only single persistent scatterers (SPSs) are detected, and pixels with multiple dominant scatterers from different sources are discarded in PSI processing. Synthetic aperture radar (SAR) tomography is a promising technique capable of resolving layovers. In this paper, new approaches based on a novel two-tier network aimed at robust and efficient detection of persistent scatterers (PSs) are presented. The calibration of atmospheric phase screen (APS) and the detection of PSs can be jointly implemented in the novel two-tier network. A residue-to-signal ratio (RSR) estimator is proposed to evaluate whether the APS is effectively calibrated and to select reliable PSs with accurate estimation. In the first-tier network, a Delaunay triangulation network is constructed for APS calibration and SPS detection. RSR thresholding is used to adjust the first-tier network by discarding arcs and SPS candidates (SPSCs) with inaccurate estimation, yielding more than one main network in the first-tier network. After network adjustment, we attempt to establish reliable SPS arcs to connect the main isolated networks, and the expanded largest connected network is then formed with more manual structure information subtracted. Furthermore, rather than the weighted least square (WLS) estimator, a network decomposition WLS (ND-WLS) estimator is proposed to accelerate the retrieval of absolute parameters from the expanded largest connected network, which is quite useful for large network inversion. In the second-tier network, the remaining SPSs and all the double PSs (DPSs) are detected and estimated with reference to the expanded largest connected network. Compared with traditional two-tier network-based methods, more PSs can be robustly and efficiently detected by the proposed new approaches. Experiments on interferometric high resolution TerraSAR-X SAR images are given to demonstrate the merits of the new approaches.
机译:持久散射干涉测量(PSI)具有获取亚米级数字高程模型(DEM)和毫米级变形的能力。 PSI应用的局限性在于,仅检测到单个持久性散射体(SPS),并且在PSI处理中会丢弃来自不同来源的具有多个主要散射体的像素。合成孔径雷达(SAR)层析成像是一种有前途的技术,能够解决停工问题。在本文中,提出了一种基于新颖的两层网络的新方法,旨在稳健而有效地检测持久性散射体(PS)。大气相屏蔽(APS)的校准和PS的检测可以在新颖的两层网络中共同实现。提出了一种残留信号比(RSR)估计器,以评估APS是否得到有效校准,并选择具有准确估计值的可靠PS。在第一层网络中,构建了Delaunay三角网,用于APS校准和SPS检测。 RSR阈值用于通过以不准确的估计丢弃弧和SPS候选(SPSC)来调整第一层网络,从而在第一层网络中产生多个主网络。经过网络调整后,我们尝试建立可靠的SPS弧以连接主要的隔离网络,然后通过减去更多的手动结构信息来形成扩展的最大连接网络。此外,提出了一种网络分解WLS(ND-WLS)估计器,而不是加权最小二乘(WLS)估计器,以加速从扩展的最大连接网络中检索绝对参数,这对于大型网络反演非常有用。在第二层网络中,参考扩展后的最大已连接网络来检测和估计其余的SPS和所有双PS(DPS)。与传统的基于两层网络的方法相比,所提出的新方法可以更有效地检测到更多的PS。给出了干涉测量高分辨率TerraSAR-X SAR图像的实验,以证明新方法的优点。

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