Unsupervised Clustering of Multi-Perspective 3D Point Cloud Data in Marshes: A Case Study

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dc.contributor.authorNguyen, Chuyen
dc.contributor.authorStarek, Michael J.
dc.contributor.authorTissot, Philippe E.
dc.contributor.authorGibeaut, James C.
dc.date.accessioned2021-06-02T19:14:52Z
dc.date.available2021-06-02T19:14:52Z
dc.date.issued2019-11-19
dc.date.issued2019-11-19
dc.description.abstractDense three-dimensional (3D) point cloud data sets generated by Terrestrial Laser Scanning (TLS) and Unmanned Aircraft System based Structure-from-Motion (UAS-SfM) photogrammetry have different characteristics and provide different representations of the underlying land cover. While there are differences, a common challenge associated with these technologies is how to best take advantage of these large data sets, often several hundred million points, to efficiently extract relevant information. Given their size and complexity, the data sets cannot be efficiently and consistently separated into homogeneous features without the use of automated segmentation algorithms. This research aims to evaluate the performance and generalizability of an unsupervised clustering method, originally developed for segmentation of TLS point cloud data in marshes, by extending it to UAS-SfM point clouds. The combination of two sets of features are extracted from both datasets: “core” features that can be extracted from any 3D point cloud and “sensor specific” features unique to the imaging modality. Comparisons of segmented results based on producer’s and user’s accuracies allow for identifying the advantages and limitations of each dataset and determining the generalization of the clustering method. The producer’s accuracies suggest that UAS-SfM (94.7%) better represents tidal flats, while TLS (99.5%) is slightly more suitable for vegetated areas. The users’ accuracies suggest that UAS-SfM outperforms TLS in vegetated areas with 98.6% of those points identified as vegetation actually falling in vegetated areas whereas TLS outperforms UAS-SfM in tidal flat areas with 99.2% user accuracy. Results demonstrate that the clustering method initially developed for TLS point cloud data transfers well to UAS-SfM point cloud data to enable consistent and accurate segmentation of marsh land cover via an unsupervised method.en_US
dc.identifier.citationNguyen C, Starek MJ, Tissot P, Gibeaut J. Unsupervised Clustering of Multi-Perspective 3D Point Cloud Data in Marshes: A Case Study. Remote Sensing. 2019; 11(22):2715. https://doi.org/10.3390/rs11222715en_US
dc.identifier.doihttps://doi.org/10.3390/rs11222715
dc.identifier.doihttps://doi.org/10.3390/rs11222715
dc.identifier.urihttps://hdl.handle.net/1969.6/89668
dc.identifier.urihttps://hdl.handle.net/1969.6/89668
dc.publisherMultidisciplinary Digital Publishing Instituteen_US
dc.subjectTLSen_US
dc.subjectUASen_US
dc.subjectStructure-from-motionen_US
dc.subjectVoxelizationen_US
dc.subjectClusteringen_US
dc.subjectMarshen_US
dc.subjectK-meansen_US
dc.titleUnsupervised Clustering of Multi-Perspective 3D Point Cloud Data in Marshes: A Case Studyen_US
dc.typeArticleen_US

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