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3D object-based classification for vehicle extraction from airborne LiDAR data by combining point shape information with spatial edge
2010
2010 IAPR Workshop on Pattern Recognition in Remote Sensing
The problem of vehicle extraction using airborne laser scanning (ALS) is studied under the framework of object-based point cloud analysis (OBPA). Object extraction relies on the partitioning of raw ALS data into various segments approximating semantic entities followed by classification. A 3D segmentation method working directly on point cloud is used, which features the detection of local arbitrary modes and the globally optimized organization of segments concurrently. To make the segmentation
doi:10.1109/prrs.2010.5742804
fatcat:n37zf6l4nffy3fnn2v5rtgjtdm