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RSS FeedsRemote Sensing, Vol. 11, Pages 342: Content-Sensitive Multilevel Point Cluster Construction for ALS Point Cloud Classification (Remote Sensing)


9 february 2019 10:00:34

Remote Sensing, Vol. 11, Pages 342: Content-Sensitive Multilevel Point Cluster Construction for ALS Point Cloud Classification (Remote Sensing)

Airborne laser scanning (ALS) point cloud classification is a challenge due to factors including complex scene structure, various densities, surface morphology, and the number of ground objects. A point cloud classification method is presented in this paper, based on content-sensitive multilevel objects (point clusters) in consideration of the density distribution of ground objects. The space projection method is first used to convert the three-dimensional point cloud into a two-dimensional (2D) image. The image is then mapped to the 2D manifold space, and restricted centroidal Voronoi tessellation is built for initial segmentation of content-sensitive point clusters. Thus, the segmentation results take the entity content (density distribution) into account, and the initial classification unit is adapted to the density of ground objects. The normalized cut is then used to segment the initial point clusters to construct content-sensitive multilevel point clusters. Following this, the point-based hierarchical features of each point cluster are extracted, and the multilevel point-cluster feature is constructed by sparse coding and latent Dirichlet allocation models. Finally, the hierarchical classification framework is created based on multilevel point-cluster features, and the AdaBoost classifiers in each level are trained. The recognition results of different levels are combined to effectively improve the classification accuracy of the ALS point cloud in the test process. Two scenes are used to experimentally test the method, and it is compared with three other state-of-the-art techniques. Digg Facebook Google StumbleUpon Twitter
153 viewsCategory: Geology, Physics
Remote Sensing, Vol. 11, Pages 313: Long-Term Dynamic of Poyang Lake Surface Water: A Mapping Work Based on the Google Earth Engine Cloud Platform (Remote Sensing)
Remote Sensing, Vol. 11, Pages 341: Improving the Performance of Galileo Uncombined Precise Point Positioning Ambiguity Resolution Using Triple-Frequency Observations (Remote Sensing)
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