Joint classification of ALS and DIM point clouds

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dc.identifier.uri http://dx.doi.org/10.15488/5099
dc.identifier.uri https://www.repo.uni-hannover.de/handle/123456789/5143
dc.contributor.author Politz, F.
dc.contributor.author Sester, M.
dc.date.accessioned 2019-07-04T11:37:33Z
dc.date.available 2019-07-04T11:37:33Z
dc.date.issued 2019
dc.identifier.citation Politz, F.; Sester, M.: Joint classification of ALS and DIM point clouds. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives 42 (2019), Nr. 2/W13, S. 1113-1120. DOI: https://doi.org/10.5194/isprs-archives-XLII-2-W13-1113-2019
dc.description.abstract National mapping agencies (NMAs) have to acquire nation-wide Digital Terrain Models on a regular basis as part of their obligations to provide up-to-date data. Point clouds from Airborne Laser Scanning (ALS) are an important data source for this task; recently, NMAs also started deriving Dense Image Matching (DIM) point clouds from aerial images. As a result, NMAs have both point cloud data sources available, which they can exploit for their purposes. In this study, we investigate the potential of transfer learning from ALS to DIM data, so the time consuming step of data labelling can be reduced. Due to their specific individual measurement techniques, both point clouds have various distinct properties such as RGB or intensity values, which are often exploited for classification of either ALS or DIM point clouds. However, those features also hinder transfer learning between these two point cloud types, since they do not exist in the other point cloud type. As the mere 3D point is available in both point cloud types, we focus on transfer learning from an ALS to a DIM point cloud using exclusively the point coordinates. We are tackling the issue of different point densities by rasterizing the point cloud into a 2D grid and take important height features as input for classification. We train an encoder-decoder convolutional neural network with labelled ALS data as a baseline and then fine-tune this baseline with an increasing amount of labelled DIM data. We also train the same network exclusively on all available DIM data as reference to compare our results. We show that only 10% of labelled DIM data increase the classification results notably, which is especially relevant for practical applications. eng
dc.language.iso eng
dc.publisher London : International Society for Photogrammetry and Remote Sensing
dc.relation.ispartof 4th ISPRS Geospatial Week 2019, Juni 10-14, 2019, Enschede, The Netherlands
dc.relation.ispartofseries International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives 42 (2019), Nr. 2/W13
dc.rights CC BY 4.0
dc.rights.uri https://creativecommons.org/licenses/by/4.0/
dc.subject Airborne Laser Scanning eng
dc.subject Dense Image Matching eng
dc.subject encoder-decoder Network eng
dc.subject point cloud eng
dc.subject transfer learning eng
dc.subject Antennas eng
dc.subject Decoding eng
dc.subject Image matching eng
dc.subject Laser applications eng
dc.subject Neural networks eng
dc.subject Signal encoding eng
dc.subject Classification results eng
dc.subject Convolutional neural network eng
dc.subject Encoder-decoder eng
dc.subject Measurement techniques eng
dc.subject National mapping agencies eng
dc.subject Point cloud eng
dc.subject Transfer learning eng
dc.subject Classification (of information) eng
dc.subject.ddc 550 | Geowissenschaften ger
dc.title Joint classification of ALS and DIM point clouds
dc.type article
dc.type conferenceObject
dc.type Text
dc.relation.issn 1682-1750
dc.relation.doi https://doi.org/10.5194/isprs-archives-XLII-2-W13-1113-2019
dc.bibliographicCitation.issue 2/W13
dc.bibliographicCitation.volume 42
dc.bibliographicCitation.firstPage 1113
dc.bibliographicCitation.lastPage 1120
dc.description.version publishedVersion
tib.accessRights frei zug�nglich


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