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CLC number: P237.3

On-line Access: 2011-05-09

Received: 2010-07-02

Revision Accepted: 2010-12-13

Crosschecked: 2011-02-28

Cited: 7

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Citations:  Bibtex RefMan EndNote GB/T7714

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Journal of Zhejiang University SCIENCE C 2011 Vol.12 No.5 P.417-429


Intelligent optimization of seam-line finding for orthophoto mosaicking with LiDAR point clouds

Author(s):  Hong-chao Ma, Jie Sun

Affiliation(s):  School of Remote Sensing, Wuhan University, Wuhan 430079, China, State Key Lab for Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

Corresponding email(s):   hchma@whu.edu.cn

Key Words:  Light detection and ranging (LiDAR), Filter, A* algorithm, Mosaicking, Seam-line

Hong-chao Ma, Jie Sun. Intelligent optimization of seam-line finding for orthophoto mosaicking with LiDAR point clouds[J]. Journal of Zhejiang University Science C, 2011, 12(5): 417-429.

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author="Hong-chao Ma, Jie Sun",
journal="Journal of Zhejiang University Science C",
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%0 Journal Article
%T Intelligent optimization of seam-line finding for orthophoto mosaicking with LiDAR point clouds
%A Hong-chao Ma
%A Jie Sun
%J Journal of Zhejiang University SCIENCE C
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%N 5
%P 417-429
%@ 1869-1951
%D 2011
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C1000235

T1 - Intelligent optimization of seam-line finding for orthophoto mosaicking with LiDAR point clouds
A1 - Hong-chao Ma
A1 - Jie Sun
J0 - Journal of Zhejiang University Science C
VL - 12
IS - 5
SP - 417
EP - 429
%@ 1869-1951
Y1 - 2011
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.C1000235

A detailed study was carried out to find optimal seam-lines for mosaicking of images acquired by an airborne light detection and ranging (LiDAR) system. High ground objects labeled as obstacles can be identified by delineating black holes from filtered point clouds obtained by filtering the raw laser scanning dataset. An innovative a* algorithm is proposed that can automatically make the seam-lines keep away from these obstacles in the registered images. This method can intelligently optimize the selection of seam-lines and improve the quality of orthophotos. A simulated grid image was first used to analyze the effect of different heuristic functions on path planning. Three subsets of LiDAR data from Xi’an, Dunhuang, and Changyang in Northwest China were obtained. A quantitative method including pixel intensity, hue, and texture was used. With our proposed method, 9.4%, 8.7%, and 9.8% improvements were achieved in Dunhuang, Xi’an, and Changyang, respectively.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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