Planner surfaces play an important role in several mapping and planning applications in urban areas. Automated algorithms to extract such surfaces are essential since they reduce the time and cost when compared to editing these DEMs manually. These surfaces could be constructed from either correlation-based DEMs or LIDAR point clouds. The advantages of the first over the second make it more appealing in generating 3D planer surfaces. In this paper we outline an automated algorithm to delineate 3D surfaces in DEMs compiled from a set of high resolution digital aerial photographs. Each cell in the DEM is assigned three attributes representing: height from bare ground, local statistics of neighboring elevations, and homogeneity of pixel intensities in corresponding aerial photos. These attributes are used to classify DEM points to ground and non-ground points using a feedforward back-propagation neural network. Candidate non-ground points are further segmented into different surfaces based on their tendency to lie in a plane. The 3D parameters of roof planes are then estimated using a robust estimator and regression analyses. DEM points contiguous to the roof patches are augmented if they pass an intensity compatibility check. Results showed an average increase in the precision of the DEMs’ heights for roof posts from 48 cm to 15 cm.

中文翻译：

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从基于相关的DEMS描绘计划器表面

规划器曲面在城市地区的一些地图和规划应用程序中起着重要作用。自动化的提取此类曲面的算法至关重要，因为与手动编辑这些DEM相比，它们减少了时间和成本。这些表面可以从基于相关的DEM或LIDAR点云构建。第一种优于第二种的优势使其在生成3D平面方面更具吸引力。在本文中，我们概述了一种自动算法，用于描绘由一组高分辨率数字航空照片汇编而成的DEM中的3D表面。DEM中的每个像元都被分配了三个属性，分别表示：距裸地的高度，相邻海拔的局部统计数据以及相应航拍照片中像素强度的均匀性。这些属性用于使用前馈反向传播神经网络将DEM点分类为地面点和非地面点。候选非地面点根据其位于平面中的趋势进一步细分为不同的表面。然后使用稳健估计器和回归分析来估计屋顶平面的3D参数。如果屋顶贴片通过强度兼容性检查，则与它们相邻的DEM点将增加。结果显示，屋顶柱的DEM高度精度平均从48 cm增加到15 cm。如果屋顶贴片通过强度兼容性检查，则与它们相邻的DEM点将增加。结果显示，屋顶柱的DEM高度精度平均从48 cm增加到15 cm。如果屋顶贴片通过强度兼容性检查，则与它们相邻的DEM点将增加。结果显示，屋顶柱的DEM高度精度平均从48 cm增加到15 cm。