Abstract
Automatically extracting DTM from DSM or LiDAR data by distinguishing non-ground points from ground points is an important issue. Many algorithms for this issue are developed, however, most of them are targeted at processing dense LiDAR data, and lack the ability of getting DTM from low resolution DSM. This is caused by the decrease of distinction on elevation variation between steep terrains and surface objects. In this paper, a method called two-steps semi-global filtering (TSGF) is proposed to extract DTM from low resolution DSM. Firstly, the DSM slope map is calculated and smoothed by SGF (semi-global filtering), which is then binarized and used as the mask of flat terrains. Secondly, the DSM is segmented with the restriction of the flat terrains mask. Lastly, each segment is filtered with semi-global algorithm in order to remove non-ground points, which will produce the final DTM. The first SGF is based on global distribution characteristic of large slope, which distinguishes steep terrains and flat terrains. The second SGF is used to filter non-ground points on DSM within flat terrain segments. Therefore, by two steps SGF non-ground points are removed robustly, while shape of steep terrains is kept. Experiments on DSM generated by ZY3 imagery with resolution of 10-30m demonstrate the effectiveness of the proposed method.
Highlights
DTM (Digital Terrain Model) is an important surveying product, which can be used in many applications such as terrain analysis (White and Wang, 2003; Martha et al, 2010; Zhan et al, 2015) and generation of DOM (Digital Ortho Map) or TDOM (True Ortho Map) (Habib et al, 2007), etc
Visual verification and quantitative comparison between Progressive morphological filtering (PMF) and two-steps semi-global filtering (TSGF) are analysed
It is clear that the proposed TSGF is better than PMF especially in mountainous terrains (b and c) since the former filtered the non-ground points as well as kept the terrains while PMF spoiled the terrains
Summary
DTM (Digital Terrain Model) is an important surveying product, which can be used in many applications such as terrain analysis (White and Wang, 2003; Martha et al, 2010; Zhan et al, 2015) and generation of DOM (Digital Ortho Map) or TDOM (True Ortho Map) (Habib et al, 2007), etc. DTM contains only terrain information while elevation dataset like LiDAR data or DSM (Digital Surface Model) generated by image matching consists of both terrains and surface objects which are known as ground points and non-ground points, respectively. In order to get DTM, non-ground points need to be identified and removed from LiDAR data or DSM, such process is called as filtering (Vosselman and Sithole, 2004). LiDAR dataset usually has a density of at least one point per square meter, topographic relief prominently differs from non-ground points in perspective of elevation variation (Figure 1). Most of filtering methods perform well in flat terrains but they are prone to fail in steep terrains, where there is almost no difference between topographic relief and non-ground points
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