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題名 An algorithm to automatically extract the roof points from airborne LIDAR data
作者 Chio, Shih-Hong
邱式鴻
貢獻者 地政系
關鍵詞 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)
日期 2007
上傳時間 13-Jul-2015 15:16:28 (UTC+8)
摘要 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.
關聯 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
資料類型 conference
dc.contributor 地政系-
dc.creator (作者) Chio, Shih-Hong-
dc.creator (作者) 邱式鴻-
dc.date (日期) 2007-
dc.date.accessioned 13-Jul-2015 15:16:28 (UTC+8)-
dc.date.available 13-Jul-2015 15:16:28 (UTC+8)-
dc.date.issued (上傳時間) 13-Jul-2015 15:16:28 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/76489-
dc.description.abstract (摘要) 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.-
dc.format.extent 176 bytes-
dc.format.mimetype text/html-
dc.relation (關聯) 28th Asian Conference on Remote Sensing 2007, ACRS 2007,Volume 3, Pages 1950-1955-
dc.relation (關聯) 28th Asian Conference on Remote Sensing 2007, ACRS 2007,12 November 2007 through 16 November 2007,Kuala Lumpur-
dc.subject (關鍵詞) 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)-
dc.title (題名) An algorithm to automatically extract the roof points from airborne LIDAR data-
dc.type (資料類型) conferenceen