Unsupervised Clustering Method for Complexity Reduction of Terrestrial Lidar Data in Marshes

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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.accessioned2021-06-02T19:46:42Z
dc.date.available2021-06-02T19:46:42Z
dc.date.issued2018-01-18
dc.description.abstractAccurate characterization of marsh elevation and landcover evolution is important for coastal management and conservation. This research proposes a novel unsupervised clustering method specifically developed for segmenting dense terrestrial laser scanning (TLS) data in coastal marsh environments. The framework implements unsupervised clustering with the well-known K-means algorithm by applying an optimization to determine the “k” clusters. The fundamental idea behind this novel framework is the application of multi-scale voxel representation of 3D space to create a set of features that characterizes the local complexity and geometry of the terrain. A combination of point- and voxel-generated features are utilized to segment 3D point clouds into homogenous groups in order to study surface changes and vegetation cover. Results suggest that the combination of point and voxel features represent the dataset well. The developed method compresses millions of 3D points representing the complex marsh environment into eight distinct clusters representing different landcover: tidal flat, mangrove, low marsh to high marsh, upland, and power lines. A quantitative assessment of the automated delineation of the tidal flat areas shows acceptable results considering the proposed method is unsupervised with no training data. Clustering results based on K-means are also compared to results based on the Self Organizing Map (SOM) clustering algorithm. Results demonstrate that the developed multi-scale voxelization approach and representative feature set are transferrable to other clustering algorithms, thereby providing an unsupervised framework for intelligent scene segmentation of TLS point cloud data in marshes.en_US
dc.identifier.citationNguyen C, Starek MJ, Tissot P, Gibeaut J. Unsupervised Clustering Method for Complexity Reduction of Terrestrial Lidar Data in Marshes. Remote Sensing. 2018; 10(1):133. https://doi.org/10.3390/rs10010133en_US
dc.identifier.doihttps://doi.org/10.3390/rs10010133
dc.identifier.urihttps://hdl.handle.net/1969.6/89670
dc.publisherMultidisciplinary Digital Publishing Instituteen_US
dc.subjectTerrestrial lidaren_US
dc.subjectVoxelizationen_US
dc.subjectClusteringen_US
dc.subjectMarshen_US
dc.subjectK-meansen_US
dc.subjectSOMen_US
dc.titleUnsupervised Clustering Method for Complexity Reduction of Terrestrial Lidar Data in Marshesen_US
dc.typeArticleen_US

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