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ZHANG Yongjun, HONG Weichen, WAN Yi. Registration of the HRSIs and the LiDAR point clouds based on the distance transformation model[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20220028
Citation: ZHANG Yongjun, HONG Weichen, WAN Yi. Registration of the HRSIs and the LiDAR point clouds based on the distance transformation model[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20220028

Registration of the HRSIs and the LiDAR point clouds based on the distance transformation model

doi: 10.13203/j.whugis20220028
  • Received Date: 2021-12-20
    Available Online: 2022-04-07
  • High resolution satellite images (HRSIs) can provide spectral characteristics observation information of ground objects at low cost and high frequency,while Light Detection and Ranging (LiDAR) point clouds can provide fine geometric structure.The fusion of two kinds of data can realize complementary advantages,and further improve the accuracy and automation of ground object classification and information extraction.The realization of geometric registration with sub-pixel accuracy is the premise of two kinds of data fusion.Methods:In this paper,a fast registration method based on line element distance transformation model is proposed.The point clouds are used as the control source,and the typical line elements such as building edges in the point clouds are projected into the image space through the initial RPC parameters of the satellite image,and the iterative closest point registration is carried out with the line elements in the satellite image,so as to achieve geometric registration by means of refining RPC parameters.In this method,the distance transformation model is used as the search table of the iterative closest point,which greatly improves the operation efficiency.Furthermore,the latest progressive robust solution strategy is adopted at the same time of the closest point iteration,so as to ensure the robustness of registration in the case of too much noise.In this paper,registration experiments are carried out on GeoEye-2 data,Gaofen-7 data,WorldView-3 data with LiDAR point clouds data.Results:The results proves that the proposed method can achieve a registration accuracy of 0.4-0.7 m on three kinds of images by using the GCPs which are accurately measured and the operator's internal control points as checkpoints.Conclusions:It is significantly better than the strategy of mapping point clouds to 2D image and then registering through multi-mode matching.
  • [1] Wong A, Orchard J. Efficient FFT-Accelerated Approach to Invariant Optical-LIDAR Registration[J]. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(11):3917-3925
    [2] Kim C, Habib A. Object-based Integration of Photogrammetric and LiDAR Data for Automated Generation of Complex Polyhedral Building Models[J]. Sensors, 2009, 9(7):5679-5701
    [3] Mastin A, Kepner J, Fisher J. Automatic Registration of LIDAR and Optical Images of Urban Scenes[C]//2009 IEEE Conference on Computer Vision and Pattern Recognition,IEEE, 2009:2639-2646
    [4] 姚春静.机载LiDAR点云数据与遥感影像配准的方法研究[D].武汉大学,2010
    [5] Barsai G, Yilmaz A, Nagarajan S, et al. Registration of Images to LiDAR and GIS Data Without Establishing Explicit Correspondences[J]. Photogrammetric Engineering&Remote Sensing, 2017, 83(10):705-716
    [6] Lahat D, Adali T, Jutten C. Multimodal Data Fusion:An Overview of Methods, Challenges, and Prospects[J]. Proceedings of the IEEE, 2015, 103(9):1449-1477
    [7] Advanced Remote Sensing:Terrestrial Information Extraction and Applications[M]. Academic Press, 2019.
    [8] Gruen A, Akca D. Least Squares 3D Surface and Curve Matching[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2005, 59(3):151-174
    [9] Akca D. Matching of 3D Surfaces and Their Intensities[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2007, 62(2):112-121
    [10] Habib A F, Shin S, Kim C, et al. Integration of Photogrammetric and LiDAR Data in a Multi-primitive Triangulation Environment[M]//Innovations in 3D Geo Information Systems. Berlin, Heidelberg:Springer, 2006:29-45
    [11] Shorter N, Kasparis T. Autonomous Registration of LiDAR Data to Single Aerial Image[C]//IGARSS 2008-2008 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2008, 5:V-216-V-219 Aldelgawy M, Detchev I D, Habib A F. Alternative Procedures for the Incorporation of LiDAR-derived Linear and Areal Features for Photogrammetric Geo-Referencing[C]//Proceedings of the American Society for Photogrammetry and Remote Sensing (ASPRS) Annual Conference, Portland, OR, USA. 2008, 28
    [12] Zheng S, Huang R, Zhou Y. Registration of Optical Images with LiDAR Data and Its Accuracy Assessment[J]. Photogrammetric Engineering&Remote Sensing, 2013, 79(8):731-741
    [13] Huang R, Zheng S, Hu K. Registration of Aerial Optical Images with LiDAR Data Using the Closest Point Principle and Collinearity Equations[J]. Sensors, 2018, 18(6):1770-1770
    [14] Li J, Hu Q, Ai M. RIFT:Multi-modal Image Matching Based on Radiation-Variation Insensitive Feature Transform[J]. IEEE Transactions on Image Processing, 2019, 29:3296-3310
    [15] Chetverikov D, Svirko D, Stepanov D, et al. The Trimmed Iterative Closest Point Algorithm[C]//Object Recognition Supported by User Interaction for Service Robots. IEEE, 2002, 3:545-548
    [16] Zhu X, Liu X, Zhang Y, et al. Robust 3-D Plane Segmentation from Airborne Point Clouds Based on Quasia-Contrario Theory[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14:7133-7147
    [17] Breiman L. Random Forests[J]. Machine Learning, 2001, 45(1):5-32
    [18] Qi C R, Yi L, Su H, et al. Pointnet++:Deep Hierarchical Feature Learning on Point Sets in a Metric Space[J]. arXiv preprint arXiv:1706.02413, 2017
    [19] Hu Q, Yang B, Xie L, et al. Randla-net:Efficient Semantic Segmentation of Large-Scale Point Clouds[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition,2020:11108-11117
    [20] Canny J. A Computational Approach to Edge Detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986, 8(6):679-698
    [21] Von Gioi R G, Jakubowicz J, Morel J M, et al. LSD:A Line Segment Detector[J]. Image Processing on Line, 2012, 2:35-55
    [22] Bentley J L. Multidimensional Binary Search Trees Used for Associative Searching[J]. Communications of the ACM, 1975, 18(9):509-517
    [23] Fabbri R, Costa L D F, Torelli J C, et al. 2D Euclidean Distance Transform Algorithms:A Comparative Survey[J]. ACM Computing Surveys (CSUR), 2008, 40(1):1-44
    [24] Li J, Zhang Y, Hu Q. Robust Estimation in Robot Vision and Photogrammetry:A New Model and Its Applications[J]. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2021, 1:137-144
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Registration of the HRSIs and the LiDAR point clouds based on the distance transformation model

doi: 10.13203/j.whugis20220028

Abstract: High resolution satellite images (HRSIs) can provide spectral characteristics observation information of ground objects at low cost and high frequency,while Light Detection and Ranging (LiDAR) point clouds can provide fine geometric structure.The fusion of two kinds of data can realize complementary advantages,and further improve the accuracy and automation of ground object classification and information extraction.The realization of geometric registration with sub-pixel accuracy is the premise of two kinds of data fusion.Methods:In this paper,a fast registration method based on line element distance transformation model is proposed.The point clouds are used as the control source,and the typical line elements such as building edges in the point clouds are projected into the image space through the initial RPC parameters of the satellite image,and the iterative closest point registration is carried out with the line elements in the satellite image,so as to achieve geometric registration by means of refining RPC parameters.In this method,the distance transformation model is used as the search table of the iterative closest point,which greatly improves the operation efficiency.Furthermore,the latest progressive robust solution strategy is adopted at the same time of the closest point iteration,so as to ensure the robustness of registration in the case of too much noise.In this paper,registration experiments are carried out on GeoEye-2 data,Gaofen-7 data,WorldView-3 data with LiDAR point clouds data.Results:The results proves that the proposed method can achieve a registration accuracy of 0.4-0.7 m on three kinds of images by using the GCPs which are accurately measured and the operator's internal control points as checkpoints.Conclusions:It is significantly better than the strategy of mapping point clouds to 2D image and then registering through multi-mode matching.

ZHANG Yongjun, HONG Weichen, WAN Yi. Registration of the HRSIs and the LiDAR point clouds based on the distance transformation model[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20220028
Citation: ZHANG Yongjun, HONG Weichen, WAN Yi. Registration of the HRSIs and the LiDAR point clouds based on the distance transformation model[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20220028
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