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

http://doi.org/10.1631/jzus.C1000235


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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%T Intelligent optimization of seam-line finding for orthophoto mosaicking with LiDAR point clouds
%A Hong-chao Ma
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%P 417-429
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%I Zhejiang University Press & Springer
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TY - JOUR
T1 - Intelligent optimization of seam-line finding for orthophoto mosaicking with LiDAR point clouds
A1 - Hong-chao Ma
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VL - 12
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.C1000235


Abstract: 
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.

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Reference

[1]Aurenhammer, F., 1991. Voronoi diagrams: a survey of a fundamental geometric data structure. ACM Comput. Surv., 23(3):345-405.

[2]Axelsson, P., 1999. Processing of laser scanner data algorithms and applications. ISPRS J. Photogr. Remote Sens., 54(2-3):138-147.

[3]Bagchi, A., Mahanti, A., 1983. Search algorithms under different kinds of heuristics—a comparative study. J. ACM, 30(1):1-27.

[4]Baltsavias, E.P., 1999a. Airborne laser scanning: existing systems and orther resources. ISPRS J. Photogr. Remote Sens., 54(2-3):164-198.

[5]Baltsavias, E.P., 1999b. A comparison between photogrammetry and laser scanning. ISPRS J. Photogr. Remote Sens., 54(2-3):83-94.

[6]Brovelli, M.A., Cannata, M., Longoni, U.M., 2002. Managing and Processing LiDAR Data within GRASS. Proc. Open Source GIS-GRASS Users Conf., unpaginated CD-ROM.

[7]Cherkassky, B.V., Goldberg, A.V., Radzik, T., 1996. Shortest paths algorithms: theory and experimental evaluation. Math. Program., 73(2):129-174.

[8]Chon, J., Kim, H., 2006. Determination of the optimal seam-lines in image mosaicking with the dynamic programming (DP) on the converted cost space. LNCS, 4029:750-757.

[9]Davis, J., 1998. Mosaics of Scenes with Moving Objects. Proc. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, p.354-360.

[10]Davis, J.C., 1986. Statistics and Data Analysis in Geology. Wiley, New York, p.656.

[11]Dechter, R., Pearl, J., 1985. Generalized best-first search strategies and the optimality of A*. J. ACM, 32(3):505-536.

[12]Dechter, R., Pearl, J., 1988. The Optimality of A*. In: Kanal, L., Kumar, V. (Eds.), Search in Artificial Intelligence. Springer-Verlag, p.166-199.

[13]Fernandez, E., Marti, R., 1999. GRASP for seam drawing in mosaicking of aerial photographic maps. J. Heurist., 5(2):181-197.

[14]Gelperin, D., 1977. On the optimality of A*. Artif. Intell., 8(1):69-76.

[15]Ghallab, M., Allard, D., 1983. Aε—an Efficient near Admissible Heuristic Search Algorithm. Proc. 8th Int. Joint Conf. on Artficial Intelligence, 2:789-791.

[16]Harris, L.R., 1973. The Bandwith Heuristic Search. Proc. 3rd Int. Joint Conf. on Artificial Intelligence, p.23-29.

[17]Hart, P.E., Nilsson, N., Raphael, B., 1968. A formal basis for the heuristic determination of minimum cost paths. IEEE Trans. Syst. Sci. Cybern., 4(2):100-107.

[18]Kerschner, M., 2001. Seam-line detection in colour orthophoto mosaicking by use of twin snakes. ISPRS J. Photogr. Remote Sens., 56(1):53-64.

[19]Koll, A., Kaindl, H., 1992. A New Approach to Dynamic Weighting. Proc. 10th European Conf. on Artificial Intelligence, p.16-17.

[20]Korf, R.E., 1988. Optimal Path-Finding Algorithms. In: Kanal, L., Kumar, D. (Eds.), Search in Artificial Intelligence. Springer-Verlag, p.223-267.

[21]Kraus, K., Pfeifer, N., 1998. Determination of terrain models in wooded areas with airborne laser scanner data. ISPRS J. Photogr. Remote Sens., 53(4):193-203.

[22]Kraus, K., Pfeifer, N., 2001. Advanced DTM generation from LiDAR data. Int. Arch. Photogr. Remote Sens. Spat. Inform. Sci., 34(3/W4):23-30.

[23]Lester, P., 2005. A* Path Finding for Beginners. Available from http://www.policyalmanac.org/games/aStarTutorial.htm [Accessed on Sept., 2010].

[24]Mahanti, A., Ray, K., 1988. Network Search Algorithms with Modifiable Heuristics. In: Kanal, L., Kumar, D. (Eds.), Search in Artificial Intelligence. Springer-Verlag, p.200-222.

[25]Martelli, A., 1977. On the complexity of admissible search algorithms. Artif. Intell., 8(1):1-13.

[26]Mero, L., 1984. A heuristic search algorithm with modifiable estimate. Artif. Intell., 23(1):13-27.

[27]Pearl, J., 1984. Heuristics: Intelligent Search Strategies for Computer Problem Solving. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA.

[28]Pearl, J., Kim, J.H., 1982. Studies in semi-admissible heuristics. IEEE Trans. PAMI, 4(4):392-400.

[29]Pohl, I., 1973. The Avoidance of (Relative) Catastrophe, Heuristic Competence, Genuine Dynamic Weighting and Computational Issues in Heuristic Problem Solving. Proc. 3rd Int. Joint Conf. on Artificial Intelligence, p.12-17.

[30]Priestnall, G., Jaafar, J., Duncan, A., 2000. Extracting urban features from LiDAR digital surface models. Comput. Envir. Urban Syst., 24(2):65-78.

[31]Schickler, W., Thorpe, A., 1998. Operational Procedure for Automatic True Orthophoto Generation. Int. Archives of Photogrammetry and Remote Sensing, p.527-532.

[32]Shiren, Y., Li, L., Peng, G., 1989. Two-dimensional seam-point searching in digital image mosaicking. Photogr. Eng. Remote Sens., 55(1):49-53.

[33]Sithole, G., Vosselman, G., 2003. Automatic Structure Detection in a Point-Cloud of an Urban Landscape. Proc. 2nd GRSS/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas, p.67-71.

[34]Sithole, G., Vosselman, G., 2004. Experimental comparison of filter algorithms for bare-Earth extraction from airborne laser scanning point clouds. ISPRS J. Photogr. Remote Sens., 59(1-2):85-101.

[35]Zhang, K., Whitman, D., 2005. Comparison of three algorithms for filtering airborne LiDAR data. Photogr. Eng. Remote Sens., 71(3):313-324.

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