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Integration of photogrammetry and LIDAR.

Ghanma, Mwafag.

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Integration of photogrammetry and LIDAR.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Integration of photogrammetry and LIDAR./
Author:	Ghanma, Mwafag.
Description:	156 p.
Notes:	Source: Dissertation Abstracts International, Volume: 67-04, Section: B, page: 2203.
Contained By:	Dissertation Abstracts International67-04B.
Subject:	Geotechnology. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=NR13697
ISBN:	9780494136973

Integration of photogrammetry and LIDAR.
Ghanma, Mwafag.

Integration of photogrammetry and LIDAR. - 156 p.

Source: Dissertation Abstracts International, Volume: 67-04, Section: B, page: 2203.

Thesis (Ph.D.)--University of Calgary (Canada), 2006.

Photogrammetry is the art and science of deriving accurate 3D metric and descriptive object information from multiple analog and digital images. Photogrammetric reconstruction produces surfaces that are rich in semantic information, which can be clearly recognized in the captured imagery. The inherent redundancy associated with photogrammetric restitution results in highly accurate surfaces. Nevertheless, the extended amount of effort and time required by the photogrammetric reconstruction procedure is a major disadvantage. In LIDAR mapping, spatial coordinates of object space points are directly acquired, enabling quick turnaround of mapping products. Still, the positional nature of LIDAR points makes it difficult to derive semantic surface information such as discontinuities and types of observed structures. Additionally, no inherent redundancy is available in the reconstructed surfaces that may be utilized to enhance the accuracy of such surfaces. The complementary characteristics between photogrammetry and LIDAR, if exploited, can lead to a more complete surface description. The synergic advantages of both systems can be fully utilized only after the precise calibration of both systems and the successful registration of the photogrammetric and LIDAR data relative to a common reference frame. In this thesis, two new methodologies are introduced for the co-registration of LIDAR and photogrammetric datasets. Generally, a registration methodology has to deal with three issues: registration primitives, transformation function, and similarity measure. One track of methodologies uses straight-lines while the other uses planar patches as the registration primitives. The mathematical model and similarity measures corresponding to both types of primitives are also realized. In the straight-lines track, the registration methodology is implemented in two ways; one step and two step procedures. The two-step procedure, besides registering the involved datasets, was meant to facilitate the detection of systematic errors in the imaging system. Also, the two-step procedure extended the purpose of LIDAR-imagery integration to more general LIDAR-LIDAR dataset registration. For the purpose of studying the effect of LIDAR data processing on the registration outcomes, a number of techniques were considered for extracting straight-lines form LIDAR datasets. In one attempt, straight lines were extracted from intersecting segmented LIDAR patches, while in a lower cost attempt, LIDAR intensity and range images interpolated in different methods were used for the same purpose. (Abstract shortened by UMI.)

ISBN: 9780494136973Subjects--Topical Terms:

1018558
Geotechnology.

Integration of photogrammetry and LIDAR.
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245 1 0 $a Integration of photogrammetry and LIDAR.
300 $a 156 p.
500 $a Source: Dissertation Abstracts International, Volume: 67-04, Section: B, page: 2203.
502 $a Thesis (Ph.D.)--University of Calgary (Canada), 2006.
520 $a Photogrammetry is the art and science of deriving accurate 3D metric and descriptive object information from multiple analog and digital images. Photogrammetric reconstruction produces surfaces that are rich in semantic information, which can be clearly recognized in the captured imagery. The inherent redundancy associated with photogrammetric restitution results in highly accurate surfaces. Nevertheless, the extended amount of effort and time required by the photogrammetric reconstruction procedure is a major disadvantage. In LIDAR mapping, spatial coordinates of object space points are directly acquired, enabling quick turnaround of mapping products. Still, the positional nature of LIDAR points makes it difficult to derive semantic surface information such as discontinuities and types of observed structures. Additionally, no inherent redundancy is available in the reconstructed surfaces that may be utilized to enhance the accuracy of such surfaces. The complementary characteristics between photogrammetry and LIDAR, if exploited, can lead to a more complete surface description. The synergic advantages of both systems can be fully utilized only after the precise calibration of both systems and the successful registration of the photogrammetric and LIDAR data relative to a common reference frame. In this thesis, two new methodologies are introduced for the co-registration of LIDAR and photogrammetric datasets. Generally, a registration methodology has to deal with three issues: registration primitives, transformation function, and similarity measure. One track of methodologies uses straight-lines while the other uses planar patches as the registration primitives. The mathematical model and similarity measures corresponding to both types of primitives are also realized. In the straight-lines track, the registration methodology is implemented in two ways; one step and two step procedures. The two-step procedure, besides registering the involved datasets, was meant to facilitate the detection of systematic errors in the imaging system. Also, the two-step procedure extended the purpose of LIDAR-imagery integration to more general LIDAR-LIDAR dataset registration. For the purpose of studying the effect of LIDAR data processing on the registration outcomes, a number of techniques were considered for extracting straight-lines form LIDAR datasets. In one attempt, straight lines were extracted from intersecting segmented LIDAR patches, while in a lower cost attempt, LIDAR intensity and range images interpolated in different methods were used for the same purpose. (Abstract shortened by UMI.)
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