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Building model reconstruction from L...

Ma, Ruijin.

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Building model reconstruction from LIDAR data and aerial photographs.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Building model reconstruction from LIDAR data and aerial photographs./
Author:	Ma, Ruijin.
Description:	181 p.
Notes:	Source: Dissertation Abstracts International, Volume: 66-01, Section: B, page: 0164.
Contained By:	Dissertation Abstracts International66-01B.
Subject:	Geodesy. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3160801
ISBN:	0496946277

Building model reconstruction from LIDAR data and aerial photographs.
Ma, Ruijin.

Building model reconstruction from LIDAR data and aerial photographs. - 181 p.

Source: Dissertation Abstracts International, Volume: 66-01, Section: B, page: 0164.

Thesis (Ph.D.)--The Ohio State University, 2005.

The objective of this research is to reconstruct 3D building models from imagery and LIDAR data. The images used are stereo aerial photographs with known imaging orientation parameters so that 3D ground coordinates can be calculated from conjugate points; and 3D ground objects can be projected to image spaces. To achieve this objective, a method of synthesizing both imagery data and LIDAR data is explored; thus, the advantages of both data sets are utilized to derive 3D building models with a high accuracy. In order to reconstruct complex building models, the polyhedral building model is employed in this research. Correspondingly, the reconstruction method is a data-driven oriented.

ISBN: 0496946277Subjects--Topical Terms:

550741
Geodesy.

Building model reconstruction from LIDAR data and aerial photographs.
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040 $a UnM $c UnM
100 1 $a Ma, Ruijin. $3 1908466
245 1 0 $a Building model reconstruction from LIDAR data and aerial photographs.
300 $a 181 p.
500 $a Source: Dissertation Abstracts International, Volume: 66-01, Section: B, page: 0164.
500 $a Adviser: Rongxing Li.
502 $a Thesis (Ph.D.)--The Ohio State University, 2005.
520 $a The objective of this research is to reconstruct 3D building models from imagery and LIDAR data. The images used are stereo aerial photographs with known imaging orientation parameters so that 3D ground coordinates can be calculated from conjugate points; and 3D ground objects can be projected to image spaces. To achieve this objective, a method of synthesizing both imagery data and LIDAR data is explored; thus, the advantages of both data sets are utilized to derive 3D building models with a high accuracy. In order to reconstruct complex building models, the polyhedral building model is employed in this research. Correspondingly, the reconstruction method is a data-driven oriented.
520 $a The general research procedure can be summarized as: (a) building detection from LIDAR data; (b) 3D building model reconstruction; (c) LIDAR data and imagery data co-registration; and (d) building model refinement. The main role of aerial image data in this research is to improve the geometric accuracy of a building model.
520 $a The major contributions of this research lie in four aspects: (1) Two algorithms are developed to perform LIDAR segmentation. Compared with the algorithms proposed by other researchers, these two algorithms work well in urban and suburban areas. In addition, they can keep fine features on the ground; (2) An algorithm of building boundary regularization is proposed in this study. Compared with the commonly used MDL algorithm, it is simple to implement and fast in computation. Longer line segments have larger weights in its adjustment process. This agrees with the fact that longer line segments have more accurate azimuths provided that the accuracy of ending points are the same for all segments; (3) A new method of 3D building model reconstruction from LIDAR data is developed. It is comprised of constructing surface topology, calculating corners from surface intersection, and ordering points of a roof surface in their correct sequence; and (4) A new framework of building model refinement from aerial imagery data is proposed. It refines building models in a consistent approach; and it utilized stereo imagery information and roof constraints in deriving refined building models.
590 $a School code: 0168.
650 4 $a Geodesy. $3 550741
650 4 $a Remote Sensing. $3 1018559
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690 $a 0799
710 2 0 $a The Ohio State University. $3 718944
773 0 $t Dissertation Abstracts International $g 66-01B.
790 1 0 $a Li, Rongxing, $e advisor
790 $a 0168
791 $a Ph.D.
792 $a 2005
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3160801