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Applications of High-Resolution, Cross-Track, Pushbroom Satellite Images With the SETSM Algorithm

机译:SETSM算法在高分辨率,跨轨,推扫式卫星图像中的应用

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Digital elevation models (DEMs) and surface displacement maps (SDMs) obtained from repeat satellite imagery provide critical measurements of changes in the earth's surface over large, remote areas. While DEMs are typically extracted from stereo pairs obtained along the same orbital pass (i.e., in-track stereo), single acquisitions are more abundant and stereo pairs created from repeat-single scan images offer the potential to increase the temporal and spatial coverage of DEMs. If precisely coregistered, repeat cross-track images may also provide measurements of surface displacement through feature tracking. We present a methodology for obtaining both DEMs and SDMs from sequences of repeat, submeter resolution, pushbroom satellite images that build upon the framework of the Surface Extraction from TIN-based Search-space Minimization algorithm. We first demonstrate a Local Surface Fitting (LSF) for reducing the noise in DEMs caused by narrow convergence angles between cross-track image stereo pairs. We then detail a procedure in which a reference image stereo pair is combined with a third repeat image to simultaneously extract surface elevation and displacement, providing DEMs and SDMs that are precisely coregistered and optimally terrain corrected. We conclude with example applications to a fjord with moving icebergs and a fast-flowing glacier and an assessment of DEM quality assessment and improvement by the LSF.
机译:从重复的卫星图像获得的数字高程模型(DEM)和地表位移图(SDM)提供了对大片偏远地区地球表面变化的关键测量结果。尽管DEM通常是从沿同一轨道通过的立体声对中提取的(即轨道内立体声),但单次采集更为丰富,并且由重复单次扫描图像创建的立体声对提供了增加DEM的时间和空间覆盖范围的潜力。如果精确地共配准,则重复的跨轨图像也可以通过特征跟踪来提供表面位移的测量值。我们提出了一种方法,该方法可从重复序列,亚米级分辨率,推扫式卫星图像序列(同时基于基于TIN的搜索空间最小化算法提取表面的框架)中获得DEM和SDM。我们首先展示了局部曲面拟合(LSF),用于减少DEM中由跨轨图像立体声对之间的狭窄会聚角引起的噪声。然后,我们将详细介绍一个过程,在该过程中,将参考图像立体对与第三重复图像进行组合以同时提取表面高程和位移,从而提供可精确配准和最佳地形校正的DEM和SDM。我们以示例应用程序结束,该应用程序应用于移动的冰山和快速流动的冰川的峡湾,以及LSF对DEM质量评估和改进的评估。

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