Filtering Photogrammetric Point Clouds Using Standard Lidar Filters Towards DTM Generation

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Abstract

Digital Terrain Models (DTMs) can be generated from point clouds acquired by laser scanning or photogrammetric dense matching. During the last two decades, much effort has been paid to developing robust filtering algorithms for the airborne laser scanning (ALS) data. With the point cloud quality from dense image matching (DIM) getting better and better, the research question that arises is whether those standard Lidar filters can be used to filter photogrammetric point clouds as well. Experiments are implemented to filter two dense matching point clouds with different noise levels. Results show that the standard Lidar filter is robust to random noise. However, artefacts and blunders in the DIM points often appear due to low contrast or poor texture in the images. Filtering will be erroneous in these locations. Filtering the DIM points pre-processed by a ranking filter will bring higher Type II error (i.e. non-ground points actually labelled as ground points) but much lower Type I error (i.e. bare ground points labelled as non-ground points). Finally, the potential DTM accuracy that can be achieved by DIM points is evaluated. Two DIM point clouds derived by Pix4Dmapper and SURE are compared. On grassland dense matching generates points higher than the true terrain surface, which will result in incorrectly elevated DTMs. The application of the ranking filter leads to a reduced bias in the DTM height, but a slightly increased noise level.
Original languageEnglish
Title of host publicationISPRS TC II Mid-term Symposium “Towards Photogrammetry 2020”, 4–7 June 2018, Riva del Garda, Italy
Subtitle of host publicationISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Place of PublicationRiva del Garda, Italy
PublisherInternational Society for Photogrammetry and Remote Sensing (ISPRS)
Pages319-326
Number of pages8
DOIs
Publication statusPublished - 28 May 2018
EventISPRS Technical Commission II Symposium 2018: Towards Photogrammetry 2020 - Congress Centre, Riva del Garda, Italy
Duration: 3 Jun 20187 Jun 2018
https://www.isprs.org/tc2-symposium2018/

Publication series

NameISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
PublisherISPRS
VolumeIV-2

Conference

ConferenceISPRS Technical Commission II Symposium 2018
CountryItaly
CityRiva del Garda
Period3/06/187/06/18
Internet address

Fingerprint

digital terrain model
lidar
filter
ranking
laser
artifact
texture
grassland
experiment

Keywords

  • ITC-GOLD

Cite this

Zhang, Z., Gerke, M., Vosselman, G., & Yang, M. Y. (2018). Filtering Photogrammetric Point Clouds Using Standard Lidar Filters Towards DTM Generation. In ISPRS TC II Mid-term Symposium “Towards Photogrammetry 2020”, 4–7 June 2018, Riva del Garda, Italy : ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences (pp. 319-326). (ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences; Vol. IV-2). Riva del Garda, Italy: International Society for Photogrammetry and Remote Sensing (ISPRS). https://doi.org/10.5194/isprs-annals-IV-2-319-2018
Zhang, Z. ; Gerke, Markus ; Vosselman, G. ; Yang, M.Y. / Filtering Photogrammetric Point Clouds Using Standard Lidar Filters Towards DTM Generation. ISPRS TC II Mid-term Symposium “Towards Photogrammetry 2020”, 4–7 June 2018, Riva del Garda, Italy : ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Riva del Garda, Italy : International Society for Photogrammetry and Remote Sensing (ISPRS), 2018. pp. 319-326 (ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences).
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abstract = "Digital Terrain Models (DTMs) can be generated from point clouds acquired by laser scanning or photogrammetric dense matching. During the last two decades, much effort has been paid to developing robust filtering algorithms for the airborne laser scanning (ALS) data. With the point cloud quality from dense image matching (DIM) getting better and better, the research question that arises is whether those standard Lidar filters can be used to filter photogrammetric point clouds as well. Experiments are implemented to filter two dense matching point clouds with different noise levels. Results show that the standard Lidar filter is robust to random noise. However, artefacts and blunders in the DIM points often appear due to low contrast or poor texture in the images. Filtering will be erroneous in these locations. Filtering the DIM points pre-processed by a ranking filter will bring higher Type II error (i.e. non-ground points actually labelled as ground points) but much lower Type I error (i.e. bare ground points labelled as non-ground points). Finally, the potential DTM accuracy that can be achieved by DIM points is evaluated. Two DIM point clouds derived by Pix4Dmapper and SURE are compared. On grassland dense matching generates points higher than the true terrain surface, which will result in incorrectly elevated DTMs. The application of the ranking filter leads to a reduced bias in the DTM height, but a slightly increased noise level.",
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Zhang, Z, Gerke, M, Vosselman, G & Yang, MY 2018, Filtering Photogrammetric Point Clouds Using Standard Lidar Filters Towards DTM Generation. in ISPRS TC II Mid-term Symposium “Towards Photogrammetry 2020”, 4–7 June 2018, Riva del Garda, Italy : ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. IV-2, International Society for Photogrammetry and Remote Sensing (ISPRS), Riva del Garda, Italy, pp. 319-326, ISPRS Technical Commission II Symposium 2018, Riva del Garda, Italy, 3/06/18. https://doi.org/10.5194/isprs-annals-IV-2-319-2018

Filtering Photogrammetric Point Clouds Using Standard Lidar Filters Towards DTM Generation. / Zhang, Z.; Gerke, Markus; Vosselman, G.; Yang, M.Y.

ISPRS TC II Mid-term Symposium “Towards Photogrammetry 2020”, 4–7 June 2018, Riva del Garda, Italy : ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Riva del Garda, Italy : International Society for Photogrammetry and Remote Sensing (ISPRS), 2018. p. 319-326 (ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences; Vol. IV-2).

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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AU - Yang, M.Y.

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N2 - Digital Terrain Models (DTMs) can be generated from point clouds acquired by laser scanning or photogrammetric dense matching. During the last two decades, much effort has been paid to developing robust filtering algorithms for the airborne laser scanning (ALS) data. With the point cloud quality from dense image matching (DIM) getting better and better, the research question that arises is whether those standard Lidar filters can be used to filter photogrammetric point clouds as well. Experiments are implemented to filter two dense matching point clouds with different noise levels. Results show that the standard Lidar filter is robust to random noise. However, artefacts and blunders in the DIM points often appear due to low contrast or poor texture in the images. Filtering will be erroneous in these locations. Filtering the DIM points pre-processed by a ranking filter will bring higher Type II error (i.e. non-ground points actually labelled as ground points) but much lower Type I error (i.e. bare ground points labelled as non-ground points). Finally, the potential DTM accuracy that can be achieved by DIM points is evaluated. Two DIM point clouds derived by Pix4Dmapper and SURE are compared. On grassland dense matching generates points higher than the true terrain surface, which will result in incorrectly elevated DTMs. The application of the ranking filter leads to a reduced bias in the DTM height, but a slightly increased noise level.

AB - Digital Terrain Models (DTMs) can be generated from point clouds acquired by laser scanning or photogrammetric dense matching. During the last two decades, much effort has been paid to developing robust filtering algorithms for the airborne laser scanning (ALS) data. With the point cloud quality from dense image matching (DIM) getting better and better, the research question that arises is whether those standard Lidar filters can be used to filter photogrammetric point clouds as well. Experiments are implemented to filter two dense matching point clouds with different noise levels. Results show that the standard Lidar filter is robust to random noise. However, artefacts and blunders in the DIM points often appear due to low contrast or poor texture in the images. Filtering will be erroneous in these locations. Filtering the DIM points pre-processed by a ranking filter will bring higher Type II error (i.e. non-ground points actually labelled as ground points) but much lower Type I error (i.e. bare ground points labelled as non-ground points). Finally, the potential DTM accuracy that can be achieved by DIM points is evaluated. Two DIM point clouds derived by Pix4Dmapper and SURE are compared. On grassland dense matching generates points higher than the true terrain surface, which will result in incorrectly elevated DTMs. The application of the ranking filter leads to a reduced bias in the DTM height, but a slightly increased noise level.

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Zhang Z, Gerke M, Vosselman G, Yang MY. Filtering Photogrammetric Point Clouds Using Standard Lidar Filters Towards DTM Generation. In ISPRS TC II Mid-term Symposium “Towards Photogrammetry 2020”, 4–7 June 2018, Riva del Garda, Italy : ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Riva del Garda, Italy: International Society for Photogrammetry and Remote Sensing (ISPRS). 2018. p. 319-326. (ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences). https://doi.org/10.5194/isprs-annals-IV-2-319-2018