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Using aerial range feature descripto...

Hu, Yanyan.

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Using aerial range feature descriptor (ARFD) for LiDAR strip adjustment and mosaicing.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Using aerial range feature descriptor (ARFD) for LiDAR strip adjustment and mosaicing./
Author:	Hu, Yanyan.
Description:	93 p.
Notes:	Source: Masters Abstracts International, Volume: 54-03.
Contained By:	Masters Abstracts International54-03(E).
Subject:	Engineering, Electronics and Electrical. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1572573
ISBN:	9781321470505

Using aerial range feature descriptor (ARFD) for LiDAR strip adjustment and mosaicing.
Hu, Yanyan.

Using aerial range feature descriptor (ARFD) for LiDAR strip adjustment and mosaicing. - 93 p.

Source: Masters Abstracts International, Volume: 54-03.

Thesis (M.S.)--The University of Texas at Arlington, 2014.

This item must not be sold to any third party vendors.

Light Detection and Ranging (LiDAR) has become the primary surface data extraction mapping technique nowadays. Airborne LiDAR system consists of three major components, which are laser ranging and scanning unit, Global Position System (GPS) and Inertia Measurement Unit (IMU). LiDAR data are collected in a rectangular strip-wise pattern with 10%--30% overlapping ratio between adjacent strips. When the calibration parameters within the system components are not accurately determined, systematic errors might occur within LiDAR strips. The systematic errors lead to strip discrepancies, which will further affect the quality of end-user products like Digital Elevation Map (DEM) or Digital Surface Map (DSM). In order to reduce or eliminate the discrepancies, various LiDAR strip adjustment methods have been proposed.

ISBN: 9781321470505Subjects--Topical Terms:

626636
Engineering, Electronics and Electrical.

Using aerial range feature descriptor (ARFD) for LiDAR strip adjustment and mosaicing.
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020 $a 9781321470505
035 $a (MiAaPQ)AAI1572573
035 $a AAI1572573
040 $a MiAaPQ $c MiAaPQ
100 1 $a Hu, Yanyan. $3 3170903
245 1 0 $a Using aerial range feature descriptor (ARFD) for LiDAR strip adjustment and mosaicing.
300 $a 93 p.
500 $a Source: Masters Abstracts International, Volume: 54-03.
500 $a Adviser: Venkat Devarajan.
502 $a Thesis (M.S.)--The University of Texas at Arlington, 2014.
506 $a This item must not be sold to any third party vendors.
520 $a Light Detection and Ranging (LiDAR) has become the primary surface data extraction mapping technique nowadays. Airborne LiDAR system consists of three major components, which are laser ranging and scanning unit, Global Position System (GPS) and Inertia Measurement Unit (IMU). LiDAR data are collected in a rectangular strip-wise pattern with 10%--30% overlapping ratio between adjacent strips. When the calibration parameters within the system components are not accurately determined, systematic errors might occur within LiDAR strips. The systematic errors lead to strip discrepancies, which will further affect the quality of end-user products like Digital Elevation Map (DEM) or Digital Surface Map (DSM). In order to reduce or eliminate the discrepancies, various LiDAR strip adjustment methods have been proposed.
520 $a Aerial Range Feature Descriptor (ARFD) and a corresponding ARFD-based LiDAR strip matching algorithm are developed to solve strip adjustment problem for two LiDAR strips. The algorithm transforms one strip onto the other in such a way that relative discrepancies between the two LiDAR strips are minimized. The transform model is equivalent to the homography matrix relating the two strips and can be determined by taking advantages of feature-based image registration technique like Scale Invariant Feature Transformation (SIFT). However, with the presence of more than two LiDAR strips, the strip matching problem becomes a strip mosaicing problem and a more sophisticated algorithm is required to mosaic multiple LiDAR strips.
520 $a In this thesis, a LiDAR strips mosaicing algorithm is proposed and the performance is evaluated under various conditions. It takes advantages of the ARFD-based LiDAR strip matching algorithm and extends it to multiple strips. The novelty of the proposed algorithm is that it borrows the concept of bundle adjustment from computer vision community and utilizes the systematic bias model to reduce/eliminate the global strip discrepancies.
590 $a School code: 2502.
650 4 $a Engineering, Electronics and Electrical. $3 626636
650 4 $a Remote Sensing. $3 1018559
650 4 $a Computer Science. $3 626642
690 $a 0544
690 $a 0799
690 $a 0984
710 2 $a The University of Texas at Arlington. $b Electrical Engineering. $3 1018571
773 0 $t Masters Abstracts International $g 54-03(E).
790 $a 2502
791 $a M.S.
792 $a 2014
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1572573