首页> 外文期刊>ISPRS Journal of Photogrammetry and Remote Sensing >Generation of DSMs from SPOT-5 in-track HRS and across-track HRG stereo data using spatiotriangulation and autocalibration
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Generation of DSMs from SPOT-5 in-track HRS and across-track HRG stereo data using spatiotriangulation and autocalibration

机译:使用空间三角测量和自动校准功能,从SPOT-5轨内HRS和跨轨HRG立体声数据生成DSM

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摘要

Digital terrain models (DTMs) were extracted from SPOT-5 High Resolution Stereoscopic (HRS, 10m resolution) in-track stereo-images and High Resolution Geometric (HRG, 5m resolution) across-track stereo-images using a three-dimensional (3D) multisensor physical model developed at the Canada Centre for Remote Sensing, Natural Resources Canada, and were evaluated against precise lidar data. Firstly, the stereo HRS and HRG photogrammetric bundle block adjustments using spatiotriangulation and autocalibration were set-up with 10 ground control points and errors of about half-resolution were obtained over 190 and 95 independent checkpoints (ICPs): 6.4m, 6.8m and 5.1m in X, Yand Z axes, and 2.6m, 2.2m and 2.9m in X, Y and Z axes for HRS and HRG, respectively. The internal accuracy are, however, better because these errors include the half-pixel image plotting error and the 3m cartographic error on ICPs. Only the results with HRS were achieved with autocalibration of the lens to correct for the radial distortions due to the largest number of pixels. The DTMs were then generated using an area-based multi-scale image matching method and 3D semi-automatic editing tools, and then compared to lidar data with 0.2m accuracy in elevation. An elevation error with 68% confidence level (LE68) of 5.2m and 6.5m were achieved over the full area for HRS and HRG, respectively. Since the DTM is in fact a digital surface model where the height, or a part, of different land cover classes (trees, houses) is included, the accuracy is depending on the land cover types. Using previous 3D visual classification, different classes (forests, residential areas, bare surfaces) were generated to take into account the height of the surfaces (natural and human-made) in the accuracy evaluation. LE68 values of 3.2m to 6.7m were thus obtained depending on the land cover types. On the other hand, LE68 values of 2.4m and 2.2m were obtained over bare surfaces for HRS and HRG, respectively. These last results are more representative of the real stereo SPOT-5 potential for DTM, compliant with the highest topographic standard.
机译:使用三维(3D)技术从SPOT-5高分辨率立体图像(HRS,10m分辨率)轨道内立体图像和高分辨率几何图形(HRG,5m分辨率)跨轨道立体图像中提取数字地形模型(DTM)。 )多传感器物理模型是在加拿大自然资源部加拿大遥感中心开发的,并根据精确的激光雷达数据进行了评估。首先,使用空间三角测量和自动校准功能设置了立体HRS和HRG摄影测量束块调整,并设置了10个地面控制点,并在190m和95个独立检查点(ICP)上获得了大约一半的分辨率:6.4m,6.8m和5.1对于HRS和HRG,分别在X,Yand Z轴上的m和X,Y和Z轴上的2.6m,2.2m和2.9m。但是,内部精度更好,因为这些误差包括ICP上的半像素图像绘制误差和3m制图误差。镜头的自动校准只能校正HRS的结果,以校正由于像素数量最多而引起的径向变形。然后使用基于区域的多尺度图像匹配方法和3D半自动编辑工具生成DTM,然后将DTM与海拔高度为0.2m的激光雷达数据进行比较。对于HRS和HRG,在整个区域内分别实现了5.2m和6.5m的68%置信度(LE68)的仰角误差。由于DTM实际上是一个数字表面模型,其中包括不同土地覆被类别(树木,房屋)的高度或一部分,因此精度取决于土地覆被类型。使用以前的3D视觉分类,生成了不同的类(森林,居民区,裸露的表面),以在准确性评估中考虑表面(自然和人造)的高度。因此,根据土地覆盖类型,得出的LE68值为3.2m至6.7m。另一方面,HRS和HRG的裸露表面的LE68值分别为2.4m和2.2m。这些最后的结果更能代表DTM的真实立体声SPOT-5潜力,符合最高的地形标准。

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