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Accuracy Assessment of Digital Surface Models Based on WorldView-2 and ADS80 Stereo Remote Sensing Data

机译：基于WorldView-2和ADS80立体遥感数据的数字表面模型的精度评估

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Digital surface models (DSMs) are widely used in forest science to model the forest canopy. Stereo pairs of very high resolution satellite and digital aerial images are relatively new and their absolute accuracy for DSM generation is largely unknown. For an assessment of these input data two DSMs based on a WorldView-2 stereo pair and a ADS80 DSM were generated with photogrammetric instruments. Rational polynomial coefficients (RPCs) are defining the orientation of the WorldView-2 satellite images, which can be enhanced with ground control points (GCPs). Thus two WorldView-2 DSMs were distinguished: a WorldView-2 RPCs-only DSM and a WorldView-2 GCP-enhanced RPCs DSM. The accuracy of the three DSMs was estimated with GPS measurements, manual stereo-measurements, and airborne laser scanning data (ALS). With GCP-enhanced RPCs the WorldView-2 image orientation could be optimised to a root mean square error (RMSE) of 0.56 m in planimetry and 0.32 m in height. This improvement in orientation allowed for a vertical median error of −0.24 m for the WorldView-2 GCP-enhanced RPCs DSM in flat terrain. Overall, the DSM based on ADS80 images showed the highest accuracy of the three models with a median error of 0.08 m over bare ground. As the accuracy of a DSM varies with land cover three classes were distinguished: herb and grass, forests, and artificial areas. The study suggested the ADS80 DSM to best model actual surface height in all three land cover classes, with median errors <1.1 m. The WorldView-2 GCP-enhanced RPCs model achieved good accuracy, too, with median errors of −0.43 m for the herb and grass vegetation and −0.26 m for artificial areas. Forested areas emerged as the most difficult land cover type for height modelling; still, with median errors of −1.85 m for the WorldView-2 GCP-enhanced RPCs model and −1.12 m for the ADS80 model, the input data sets evaluated here are quite promising for forest canopy modelling.
机译：数字表面模型（DSM）在森林科学中广泛用于对森林冠层进行建模。立体声对的高分辨率卫星和数字航拍图像相对较新，并且对于DSM生成的绝对精度在很大程度上尚不清楚。为了评估这些输入数据，使用摄影测量仪器生成了两个基于WorldView-2立体声对的DSM和一个ADS80 DSM。有理多项式系数（RPC）定义了WorldView-2卫星图像的方向，可以通过地面控制点（GCP）对其进行增强。因此，区分了两个WorldView-2 DSM：一个仅WorldView-2 RPC的DSM和一个WorldView-2 GCP增强的RPC DSM。这三个DSM的准确性是通过GPS测量，手动立体测量和机载激光扫描数据（ALS）进行估算的。使用GCP增强的RPC，可以将WorldView-2图像方向优化为平面图的均方根误差（RMSE）为0.56 m，高度为0.32 m。定向的改进使得在平坦地形中使用WorldView-2 GCP增强的RPC DSM的垂直中值误差为-0.24 m。总体而言，基于ADS80图像的DSM显示了这三个模型中最高的精度，在裸露地面上的中值误差为0.08 m。由于DSM的准确性随土地覆盖而变化，因此可分为三类：草药和草皮，森林和人工区域。研究表明，ADS80 DSM可以最好地模拟所有三种土地覆盖类别中的实际表面高度，中值误差<1.1 m。 WorldView-2 GCP增强的RPCs模型也获得了良好的精度，草药和草木植被的中值误差为-0.43 m，人工区域的中值误差为-0.26 m。森林地区成为高度建模中最困难的土地覆盖类型。尽管如此，WorldView-2 GCP增强的RPCs模型的中位数误差为-1.85 m，而ADS80模型的中位数误差为-1.12 m，此处评估的输入数据集对于森林冠层建模非常有希望。

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