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AIRBORNE LIDAR POINT CLOUD CLASSIFICATION FUSION WITH DIM POINT CLOUD
2020
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Abstract. Airborne Light Detection And Ranging (LiDAR) point clouds and images data fusion have been widely studied. However, with recent developments in photogrammetric technology, images can now provide dense image matching (DIM) point clouds. To make use of such DIM points, a sample selection framework is introduced. That is, first, the geometric features of LiDAR points and DIM points are extracted. Each feature per point is considered a sample. Then we extend the binary TrAdaboost
doi:10.5194/isprs-archives-xliii-b2-2020-375-2020
fatcat:lxaeohr3jfbwnndh5lepdwz6je