Article, Other literature type English OPEN
Zhou, M. ; Tang, L.-L. ; Li, C.-R. ; Peng, Z. ; Li, J.-M. (2012)
  • Publisher: Copernicus Publications
  • Journal: (issn: 2194-9034, eissn: 2194-9034)
  • Related identifiers: doi: 10.5194/isprsarchives-XXXIX-B3-199-2012
  • Subject: TA1-2040 | T | TA1501-1820 | Applied optics. Photonics | Engineering (General). Civil engineering (General) | Technology
    acm: 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.
  • References (11)
    11 references, page 1 of 2

    Bin, X., 2008. Research on object extraction and measurement based on LiDAR data, Master Thesis, Academy of OptoElectronics, Chinese Academy of Sciences, Beijing, China.

    Buck, J., Malm, A., Zakel, A., Krause, B., Tiemann, B., 2007. High-resolution 3D coherent laser radar imaging. Laser Radar Technology and Applications XII. Proc. of SPIE, Vol. 6550, pp. 655002.

    Golovinskiy, A., Kim, V., Funkhouser, T., 2009. Shape-based recognition of 3D point clouds in urban environments. Computer Vision, 2009 IEEE 12th International Conference on, Kyoto, Japan, pp. 2154-2161.

    Hans-Gerd, M., George, V., 1999. Two algorithms for extracting building models from raw laser altimetry data. ISPRS Journal of Photogrammetry & Remote Sensing 54, 1999, pp. 153-163.

    Hu, M., 1962. Visual pattern recognition by moment invariants. Information Theory, IRE Transactions, 8(2), pp. 179-187.

    Jinhui, L., Zhuoxun S., 2009. New Approach of Imagery Generation and Target Recognition Based on 3D LIDAR Data. The Ninth International Conference on Electronic Measurement & Instruments, 2009, pp. 612-616.

    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.

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