Abstract
Abstract. Computation of a DTM from a DSM is a well-known and very important task. We derive the DTM by a procedure consisting of ground points extraction, surface interpolation and triangulation by a canonical mesh if the terrain is flat or has only moderate changes in elevation. In regions with steep slopes, such as at riversides, and with man-made 3D structures, such as around bridges, interpolation artifacts and suppression of high-resolution details can lead to coarse errors in local elevations even for the building detection task. The eligible regions must be therefore detected and at least locally reprocessed. For detection, we search for connected components of a certain minimum size with negative relative elevations. For reconstruction, we suppress the points with erroneously reconstructed DSM values and interpolate the surface by means of L1 splines. Finally, these meshes must be fused into one single DTM mesh. We applied land cover classification to demonstrate the usability of our correction. The overall accuracy amounts to around 88% while the number of faulty assignments due to incorrect DTMs can be significantly reduced.
Highlights
MotivationAccurate Digital Terrain Models (DTMs) are required in many applications. For instance, DTMs are important for urban planning or navigation tasks
The steep slope is mis-interpreted by the classification algorithm as rise of an elevated object, as it is the case for e.g. building walls
We presented an approach to correct a digital terrain model in critical regions
Summary
Accurate Digital Terrain Models (DTMs) are required in many applications. For instance, DTMs are important for urban planning or navigation tasks. Learning a model based on neural architecture reminding ConvNet with some 17 million labeled points, (Hu, Yuan, 2016) interpreted a neighborhood of a 3D point as an image with 128 × 128 pixels and extracted the ground points according to the output of the last fully-connected layer This approach was improved by (Gevaert et al, 2018) who argued that in high-resolution data, such small images may not contain the largest non-terrain object and who proposed using atrous convolutions on large images instead of image down-sampling. They mentioned that many easy training examples can be created by a rule-based approach. A Simulation system as e.g. Virtual Battlespace (VBS) enable the generation of terrain databases whereby the meshes could be exported to VBS (Haufel et al, 2017)
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