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Title: An algorithm to automatically extract the roof points from airborne LIDAR data
Authors: Chio, Shih-Hong
Contributors: 地政系
Keywords: Airborne lidar data;Building reconstruction;Data snooping;Forward selection;Ground elevation;Least squares fitting;LIDAR data;Mathematic morphologies;Quad trees;Range images;Splitting and merging;Algorithms;Forestry;Merging;Optical radar;Remote sensing;Roofs;Three dimensional;Trees (mathematics)
Date: 2007
Issue Date: 2015-07-13 15:16:28 (UTC+8)
Abstract: Terrain information is implied in airborne LIDAR data. Especially roof points among them are very important for 3-D building reconstruction. Therefore, this paper will present an algorithm to automatically extract roof points from airborne LIDAR data. Based on the assumption that the elevation of roof points is higher than ground elevation and the roofs are either horizontal or oblique, the roof area outlines from pure LIDAR data are roughly detected from possible roof range images, generated from LIDAR data, by image spitting and merging as well as mathematic morphology. Afterwards, some SEED regions are extracted by least squares fitting based on quad-tree spitting and merging segmentation from the LIDAR data extracted according to the detected roof area outlines. These SEED regions are used to merge adjacent LIDAR points for extracting more complete roof points by forward selection data snooping algorithm. From the experiments, the feasibility of the proposed approach will be proved.
Relation: 28th Asian Conference on Remote Sensing 2007, ACRS 2007,Volume 3, Pages 1950-1955
28th Asian Conference on Remote Sensing 2007, ACRS 2007,12 November 2007 through 16 November 2007,Kuala Lumpur
Data Type: conference
Appears in Collections:[地政學系] 會議論文

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