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Zhou, M.; Tang, L.-L.; Li, C.-R.; Peng, Z.; Li, J.-M. (2012)
Publisher: Copernicus Publications
Languages: English
Types: Article
Subjects: TA1-2040, T, TA1501-1820, Applied optics. Photonics, Engineering (General). Civil engineering (General), Technology

Classified by OpenAIRE into

ACM Ref: GeneralLiterature_MISCELLANEOUS
LiDAR is capable of obtaining three dimension coordinates of the terrain and targets directly and is widely applied in digital city, emergent disaster mitigation and environment monitoring. Especially because of its ability of penetrating the low density vegetation and canopy, LiDAR technique has superior advantages in hidden and camouflaged targets detection and recognition. Based on the multi-echo data of LiDAR, and combining the invariant moment theory, this paper presents a recognition method for classic airplanes (even hidden targets mainly under the cover of canopy) using KD-Tree segmented point cloud data. The proposed algorithm firstly uses KD-tree to organize and manage point cloud data, and makes use of the clustering method to segment objects, and then the prior knowledge and invariant recognition moment are utilized to recognise airplanes. The outcomes of this test verified the practicality and feasibility of the method derived in this paper. And these could be applied in target measuring and modelling of subsequent data processing.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Bin, X., 2008. Research on object extraction and measurement based on LiDAR data, Master Thesis, Academy of OptoElectronics, Chinese Academy of Sciences, Beijing, China.
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    • Marino, R. and W. Davis, 2005. Jigsaw: a foliage-penetrating 3D imaging laser radar system. Lincoln Laboratory Journal, 15(1).
    • Moore, A., 1991. An intoductory tutorial on kd-trees. PhD Thesis, Efficient Memory-based Learning for Robot Control, Computer Laboratory, University of Cambridge, Cambridge, England.
    • Prokhorov, D.V., 2009. Object recognition in 3D LiDAR data with recurrent neural network. Computer Vision and Pattern Recognition Workshops 2009, California, USA, pp. 9-15.
    • Sahibsingh, A., Kenneth, J., Robert, B., 1997. Aircraft Identification by Moment Invariants. IEEE Transactions on Computers, Vol. C-26, No. 1. 1977, pp. 39-46.
    • Zhongliang, Q., Wenjun, W., 1992. Automatic ship classification by superstructure moment invariants and two-stage classifier. Singapore ICCS/ISITA '92, 1992, pp. 544-547.
  • No related research data.
  • No similar publications.