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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing. >A Network-Based Enhanced Spectral Diversity Approach for TOPS Time-Series Analysis
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A Network-Based Enhanced Spectral Diversity Approach for TOPS Time-Series Analysis

机译：基于网络的增强光谱分集方法用于TOPS时间序列分析

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摘要

For multitemporal analysis of synthetic aperture radar (SAR) images acquired with a terrain observation by progressive scan (TOPS) mode, all acquisitions from a given satellite track must be coregistered to a reference coordinate system with accuracies better than 0.001 of a pixel (assuming full SAR resolution) in the azimuth direction. Such a high accuracy can be achieved through geometric coregistration, using precise satellite orbits and a digital elevation model, followed by a refinement step using a time-series analysis of coregistration errors. These errors represent the misregistration between all TOPS acquisitions relative to the reference coordinate system. We develop a workflow to estimate the time series of azimuth misregistration using a network-based enhanced spectral diversity (NESD) approach, in order to reduce the impact of temporal decorrelation on coregistration. Example time series of misregistration inferred for five tracks of Sentinel-1 TOPS acquisitions indicates a maximum relative azimuth misregistration of less than 0.01 of the full azimuth resolution between the TOPS acquisitions in the studied areas. Standard deviation of the estimated misregistration time series for different stacks varies from 1.1e-3 to 2e-3 of the azimuth resolution, equivalent to 1.6-2.8 cm orbital uncertainty in the azimuth direction. These values fall within the 1-sigma orbital uncertainty of the Sentinel-1 orbits and imply that orbital uncertainty is most likely the main source of the constant azimuth misregistration between different TOPS acquisitions. We propagate the uncertainty of individual misregistration estimated with ESD to the misregistration time series estimated with NESD and investigate the different challenges for operationalizing NESD.
机译：对于通过逐行扫描（TOPS）模式通过地形观测获取的合成孔径雷达（SAR）图像的多时相分析，必须将从给定卫星轨道获取的所有图像共同配准到参考坐标系，其精度要优于0.001像素（假设已满） SAR分辨率）。可以通过使用精确的卫星轨道和数字高程模型进行几何定心，然后执行对定心误差的时间序列分析的精炼步骤来实现如此高的精度。这些误差表示所有TOPS采集相对于参考坐标系之间的配准错误。我们开发了一个工作流，以使用基于网络的增强频谱分集（NESD）方法来估计方位失准的时间序列，以减少时间去相关对共聚焦的影响。针对Sentinel-1 TOPS采集的五个轨迹推断出的重合失调的示例时间序列表明，在研究区域中，最大相对方位角重合偏差小于TOPS捕获之间完整方位角分辨率的0.01。不同堆栈的估计重合时间序列的标准偏差在方位角分辨率的1.1e-3至2e-3之间变化，相当于方位角方向上1.6-2.8 cm的轨道不确定性。这些值落在Sentinel-1轨道的1sigma轨道不确定性之内，这意味着轨道不确定性很可能是不同TOPS采集之间恒定方位角重合不准的主要来源。我们将用ESD估算的个人配准失准的不确定性传播到用NESD估算的配准失准时间序列，并研究实施NESD的不同挑战。

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