Bare-earth extraction and DTM generation from photogrammetric point clouds including the use of an existing lower-resolution DTM

Debella-Gilo, Misganu

doi:10.1080/01431161.2016.1194543

Cited by 20 publications

(13 citation statements)

References 25 publications

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“…Nordbo et al [ 105 ] employed existing building mask (geographical information on the coastline and building edges) to assist the generation of large-scale urban DTMs using airborne Lidar data. Debella-Gilo [ 106 ] combined high-resolution photogrammetric point clouds (similar to Lidar point clouds) with existing low-resolution DTMs, which worked as reference trend surface, to produce high-resolution DTMs. Instead of direct use of spectral information, Saeidi et al [ 107 ] extracted the NDVI (Normalized Difference Vegetation Index) feature to assist the classification of bare ground, trees attached to steep terrains and large buildings in complicated landscapes and produced high-quality DTMs.…”

Section: Discussionmentioning

confidence: 99%

State-of-the-Art: DTM Generation Using Airborne LIDAR Data

Chen

Gao

Devereux

2017

Sensors

166

120

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Digital terrain model (DTM) generation is the fundamental application of airborne Lidar data. In past decades, a large body of studies has been conducted to present and experiment a variety of DTM generation methods. Although great progress has been made, DTM generation, especially DTM generation in specific terrain situations, remains challenging. This research introduces the general principles of DTM generation and reviews diverse mainstream DTM generation methods. In accordance with the filtering strategy, these methods are classified into six categories: surface-based adjustment; morphology-based filtering, triangulated irregular network (TIN)-based refinement, segmentation and classification, statistical analysis and multi-scale comparison. Typical methods for each category are briefly introduced and the merits and limitations of each category are discussed accordingly. Despite different categories of filtering strategies, these DTM generation methods present similar difficulties when implemented in sharply changing terrain, areas with dense non-ground features and complicated landscapes. This paper suggests that the fusion of multi-sources and integration of different methods can be effective ways for improving the performance of DTM generation.

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Section: Discussionmentioning

confidence: 99%

State-of-the-Art: DTM Generation Using Airborne LIDAR Data

Chen

Gao

Devereux

2017

Sensors

166

120

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“…As shown in Figure 3, when the grid size is set to a large value such as 1 l , the lowest point will not fall near the ridge, thus it is difficult to completely retain the true terrain at the ridge during subsequent surface fitting. When the grid size is set to a small value such as 2 l , the lowest point will fall on the roof of the building, and the fitted terrain will deviate from the real terrain, resulting in incomplete filtering of the buildings. At the same time, the threshold in the filtering process is not changed adaptively, which will affect the reconstruction result.…”

Section: Reconstruction Methods Based On Surface Fittingmentioning

confidence: 99%

“…The information of a digital elevation model (DEM) is required for many applications, therefore it is necessary to reconstruct a DEM from a DSM by removing the above-ground objects such as buildings. The DEM reconstruction is involved in photogrammetry [1,2], laser detection and ranging (LiDAR) [3][4][5][6], or InSAR [7][8][9][10]. Many methods have been proposed in this subject especially in the field of LiDAR [6], however reconstruction research based on InSARs is relatively rare.…”

Section: Introductionmentioning

confidence: 99%

Coherent Markov Random Field-Based Unreliable DSM Areas Segmentation and Hierarchical Adaptive Surface Fitting for InSAR DEM Reconstruction

Qian

Wang

et al. 2020

Sensors

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A digital elevation model (DEM) can be obtained by removing ground objects, such as buildings, in a digital surface model (DSM) generated by the interferometric synthetic aperture radar (InSAR) system. However, the imaging mechanism will cause unreliable DSM areas such as layover and shadow in the building areas, which seriously affect the elevation accuracy of the DEM generated from the DSM. Driven by above problem, this paper proposed a novel DEM reconstruction method. Coherent Markov random field (CMRF) was first used to segment unreliable DSM areas. With the help of coherence coefficients and residue information provided by the InSAR system, CMRF has shown better segmentation results than traditional traditional Markov random field (MRF) which only use fixed parameters to determine the neighborhood energy. Based on segmentation results, the hierarchical adaptive surface fitting (with gradually changing the grid size and adaptive threshold) was set up to locate the non-ground points. The adaptive surface fitting was superior to the surface fitting-based method with fixed grid size and threshold of height differences. Finally, interpolation based on an inverse distance weighted (IDW) algorithm combining coherence coefficient was performed to reconstruct a DEM. The airborne InSAR data from the Institute of Electronics, Chinese Academy of Sciences has been researched, and the experimental results show that our method can filter out buildings and identify natural terrain effectively while retaining most of the terrain features.

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“…In morphological filtering, the ground is defined as the lowest point within a specified neighborhood. Variations of this method include: making the threshold dependent on the distance to the center point (Sithole and Vosselman, 2005) or adapting the filter to the slope calculated from an existing DTM (Sithole and Vosselman, 2005;Debella-Gilo, 2016). Morphological methods are very sensitive to the size of the search neighborhood.…”

Section: Figure 52: An Overview Of Sources Of Errors In Dtm Extractimentioning

confidence: 99%

“…Therefore, we include features which describe the surface topography over a larger area as input for the FCN. These features are inspired by DTM extraction methods which take an existing DTM into account (Sithole and Vosselman, 2005;Debella-Gilo, 2016) and the surface-based or interpolation methods (Kraus and Pfeifer, 1998). Namely, we define a grid over the DSM and select the point with the elevation which corresponds to the lowest 10% of the pixels in the cell.…”

Section: Proposed Networkmentioning

confidence: 99%

Unmanned aerial vehicle mapping for settlement upgrading

Gevaert¹

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Bare-earth extraction and DTM generation from photogrammetric point clouds including the use of an existing lower-resolution DTM

Cited by 20 publications

References 25 publications

State-of-the-Art: DTM Generation Using Airborne LIDAR Data

State-of-the-Art: DTM Generation Using Airborne LIDAR Data

Coherent Markov Random Field-Based Unreliable DSM Areas Segmentation and Hierarchical Adaptive Surface Fitting for InSAR DEM Reconstruction

Unmanned aerial vehicle mapping for settlement upgrading

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