Abstract
Abstract. In this work, a new filtering approach is proposed for a fully automatic Digital Terrain Model (DTM) extraction from very high resolution airborne images derived Digital Surface Models (DSMs). Our approach represents an enhancement of the existing DTM extraction algorithm Multi-directional and Slope Dependent (MSD) by proposing parameters that are more reliable for the selection of ground pixels and the pixelwise classification. To achieve this, four main steps are implemented: Firstly, 8 well-distributed scanlines are used to search for minima as a ground point within a pre-defined filtering window size. These selected ground points are stored with their positions on a 2D surface to create a network of ground points. Then, an initial DTM is created using an interpolation method to fill the gaps in the 2D surface. Afterwards, a pixel to pixel comparison between the initial DTM and the original DSM is performed utilising pixelwise classification of ground and non-ground pixels by applying a vertical height threshold. Finally, the pixels classified as non-ground are removed and the remaining holes are filled. The approach is evaluated using the Vaihingen benchmark dataset provided by the ISPRS working group III/4. The evaluation includes the comparison of our approach, denoted as Network of Ground Points (NGPs) algorithm, with the DTM created based on MSD as well as a reference DTM generated from LiDAR data. The results show that our proposed approach over performs the MSD approach.
Highlights
Having an accurate and reliable Digital Terrain Model (DTM) is beneficial for numerous mapping applications in photogrammetry and remote sensing, such as object detection
We especially focus on area 1 (“Inner City”) and area 2 (“High Riser”)
4.2 Parameter settings The parameters used for the Multi-directional and Slope Dependent (MSD) approach are provided in Table 1, and for the network of ground points (NGPs) approach accordantly in Table 2; the same parameters were used for both areas
Summary
Having an accurate and reliable DTM is beneficial for numerous mapping applications in photogrammetry and remote sensing, such as object detection. High resolution stereo images from airborne or satellite platforms can achieve sub-meter Ground Sample Distance (GSD) and have yielded the opportunity to produce a high resolution and accurate Digital Surface Models (DSMs) by using dense image matching technique (Hirschmuller, 2008). The two most common approaches to generating DSMs are based on images using stereo image matching techniques and Light Detection and Ranging (LiDAR). DSM derived from stereo image matching often contains holes as a result of occlusion and mismatches (Krauß et al, 2015). Such holes can be filled by interpolation (Krauß & d’Angelo, 2011). LiDAR data yields more well defined DSMs and the objects outlines are well defined (Perko et al, 2015; Tian et al, 2014)
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