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首页> 外文期刊>GIScience & remote sensing >Automated stereo-photogrammetric DEM generation at high latitudes: Surface Extraction with TIN-based Search-space Minimization (SETSM) validation and demonstration over glaciated regions
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Automated stereo-photogrammetric DEM generation at high latitudes: Surface Extraction with TIN-based Search-space Minimization (SETSM) validation and demonstration over glaciated regions

机译：在高纬度地区自动生成立体摄影测绘的DEM：基于TIN的搜索空间最小化（SETSM）验证和在冰川地区的演示，进行表面提取

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摘要

Digital elevation models (DEMs) are critical to a wide range of geoscience investigations. High-latitude and polar regions are particularly challenging for automated, stereo-photogrammetric DEM extraction due to the abundance of surfaces that are low-contrast and repetitively textured, such as snow and shadowed terrain, and have discontinuities such as in crevasse fields, glacier calving faces or iceberg edges. Sub-meter, stereo-mode satellite imagery of high geometric and radiometric quality is becoming increasingly accessible, offering the potential for dramatically increasing the spatial coverage and quality of high-latitude DEMs. Here we demonstrate and validate automated DEMs generated from the Surface Extraction with Triangulated Irregular Network-based Search-space Minimization (SETSM) algorithm designed for these challenging terrains using only the satellite rational polynomial coefficients (RPCs). Comparison between 2-m DEMs created from WorldView image pairs and low-altitude LiDAR point clouds in west Greenland give DEM biases of less than 5m, which is the maximum systematic RPC error. Co-registration with the LiDAR data reduces the DEM RMS error to ~20cm, which is comparable to the uncertainty of the LiDAR data. We demonstrate SETSM's automatic RPC refinement and bias reduction by successfully extracting a high-quality DEM from Pleiades stereo pair images with large RPC errors. Finally, we provide examples of SETSM DEMs that demonstrate their utility for a range of applications of interest to polar scientists.
机译：数字高程模型（DEM）对广泛的地球科学研究至关重要。高纬度和极地地区对于自动立体摄影测量DEM提取尤其具有挑战性，因为存在大量低对比度且重复纹理的表面（例如雪和阴影地形），并且具有不连续性（例如在裂隙野，冰川崩塌中）脸或冰山边缘。高几何和辐射质量的亚米立体模式卫星图像正变得越来越容易获得，为大幅提高高纬度DEM的空间覆盖范围和质量提供了潜力。在这里，我们演示并验证了仅基于卫星有理多项式系数（RPC）的，针对不具挑战性的地形而设计的基于三角不规则网络的搜索空间最小化（SETSM）算法从表面提取生成的自动DEM。由WorldView影像对创建的2 m DEM与格陵兰西部的低空LiDAR点云之间的比较得出DEM偏差小于5 m，这是最大的系统RPC误差。与LiDAR数据的共配准可将DEM RMS误差减小到〜20cm，这与LiDAR数据的不确定性相当。通过成功地从具有较大RPC错误的Pleiades立体对图像中提取高质量的DEM，我们演示了SETSM的自动RPC精炼和偏差减少。最后，我们提供SETSM DEM的示例，以证明其在极地科学家感兴趣的一系列应用中的实用性。

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