Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/20562
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dc.contributorDepartment of Land Surveying and Geo-Informaticsen_US
dc.creatorDeng, SSen_US
dc.creatorShi, WZen_US
dc.date.accessioned2015-08-28T04:28:43Z-
dc.date.available2015-08-28T04:28:43Z-
dc.identifier.urihttp://hdl.handle.net/10397/20562-
dc.language.isoenen_US
dc.rights© Author(s) 2013. This work is distributed under the Creative Commons Attribution 3.0 License.en_US
dc.rightsThe following publication Deng, S. S., & Shi, W. Z. (2013). Integration of different filter algorithms for improving the ground surface extraction from airborne LIDAR data. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(Part 2/W1), 105-110 is available at https://doi.org/10.5194/isprsarchives-XL-2-W1-105-2013en_US
dc.subjectAirborne LiDARen_US
dc.subjectFilter algorithmen_US
dc.subjectIntegrationen_US
dc.subjectFiltering erroren_US
dc.subjectStatisticsen_US
dc.titleIntegration of different filter algorithms for improving the ground surface extraction from airborne LiDAR dataen_US
dc.typeConference Paperen_US
dc.identifier.spage105en_US
dc.identifier.epage110en_US
dc.identifier.volumeXL-2/W1en_US
dc.identifier.doi10.5194/isprsarchives-XL-2-W1-105-2013en_US
dcterms.abstractAn important step for processing airborne Light Detection And Ranging (LiDAR) data is point cloud filtering. Points striking on vegetation and man-made objects and low points (points significantly lower than neighboring points) are filtered out, leaving ground points for generation of digital terrain models (DTM). A variety of filter algorithms have been developed, which have disparate performance in different landscape and environment. This study investigates the potential of integrating the results of different filter algorithms for improving the ground surface extraction from the LiDAR point cloud. A simple procedure was proposed based on a statistical approach to identify and remove filtering errors and combine ground points from each filtering result. The procedure was tested in an area with rugged terrain covered by dense vegetation of variable heights. The filtering results of two popular filter algorithms, progressive TIN (Triangulated Irregular Network) densification and hierarchical robust interpolation, were integrated. The filtering results of two algorithms and the integration result were qualitatively evaluated. The evaluation results indicated that the proposed integration procedure can remove most vegetation points that were not filtered out by filter algorithms, and combine ground points from each filtering result.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationThe international archives of photogrammetry, remote sensing and spatial information sciences, volume XL-2/W1, 2013 8th International Symposium on Spatial Data Quality, 30 May - 1 June 2013, Hong Kong, 2013, v. XL-2/W1, p. 105-110en_US
dcterms.isPartOfThe international archives of photogrammetry, remote sensing and spatial information sciencesen_US
dcterms.issued2013-
dc.identifier.rosgroupidr67571-
dc.description.ros2012-2013 > Academic research: refereed > Publication in refereed journalen_US
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_IR/PIRAen_US
dc.description.pubStatusPublisheden_US
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