Advances in fusion of optical imagery and LiDAR point cloud applied to photogrammetry and remote sensing

被引:77
|
作者
Zhang J. [1 ]
Lin X. [1 ]
机构
[1] Chinese Academy of Surveying and Mapping, Beijing
来源
Zhang, Jixian (zhangjx@casm.ac.cn) | 1600年 / Taylor and Francis Ltd.卷 / 08期
基金
中国国家自然科学基金;
关键词
change detection; classification; Data fusion; optical image; point cloud; target recognition;
D O I
10.1080/19479832.2016.1160960
中图分类号
学科分类号
摘要
Optical imagery and Light Detection And Ranging (LiDAR) point cloud are two major data sources in the community of photogrammetry and remote sensing. Optical images and LiDAR data have unique characteristics that make them preferable in certain applications. On the other hand, the disadvantage of one type of data source may be compensated by an advantage of the other. Hence, data fusion is a prerequisite to utilising the complementary characteristics of both data sources. Numerous methods haven been proposed to perform the fusion in various applications. This article makes a systematic review of the state-of-the-art fusion methodology used in various applications, such as registration, generation of true orthophotographs, pan-sharpening, classification, recognition of some key targets, three-dimensional reconstruction, change detection and forest inventory. Moreover, the future developing trends are introduced. In the coming few years, we expect that fusion of optical images and LiDAR point cloud will promote the development of both photogrammetry and laser scanning in both industry and scientific research. © 2016 Informa UK Limited, trading as Taylor & Francis Group.
引用
收藏
页码:1 / 31
页数:30
相关论文
共 50 条
  • [31] Fuzzy AutoEncode Based Cloud Detection for Remote Sensing Imagery
    Shao, Zhenfeng
    Deng, Juan
    Wang, Lei
    Fan, Yewen
    Sumari, Neema S.
    Cheng, Qimin
    [J]. REMOTE SENSING, 2017, 9 (04):
  • [32] Advances in mathematical morphology applied to geoscience and remote sensing
    Soille, P
    Pesaresi, M
    [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2002, 40 (09): : 2042 - 2055
  • [33] Evaluation of thresholding techniques applied to oceanograpic remote sensing imagery
    Marcello, J
    Marqués, F
    Eugenio, F
    [J]. IMAGE AND SIGNAL PROCESSING FOR REMOTE SENSING X, 2004, 5573 : 96 - 103
  • [34] LiDAR Point Cloud Correction and Location Based on Multisensor Fusion
    Pu Wenhao
    Liu Xixiang
    Chen Hao
    Xu Hao
    Liu Ye
    [J]. LASER & OPTOELECTRONICS PROGRESS, 2023, 60 (24)
  • [35] SPATIAL-FILTERING APPLIED TO REMOTE-SENSING IMAGERY
    HARNETT, PR
    MOUNTAIN, GD
    BARNETT, ME
    [J]. OPTICA ACTA, 1978, 25 (08): : 801 - 809
  • [36] ADVANCES AND CHALLENGES OF UAV SFM MVS PHOTOGRAMMETRY AND REMOTE SENSING: SHORT REVIEW
    Berra, E. F.
    Peppa, M., V
    [J]. 2020 IEEE LATIN AMERICAN GRSS & ISPRS REMOTE SENSING CONFERENCE (LAGIRS), 2020, : 533 - 538
  • [37] LIDAR Point Cloud Registration for Sensing and Reconstruction of Unstructured Terrain
    Zhu, Qingyuan
    Wu, Jinjin
    Hu, Huosheng
    Xiao, Chunsheng
    Chen, Wei
    [J]. APPLIED SCIENCES-BASEL, 2018, 8 (11):
  • [38] Empirical radiometric correction of optical remote sensing imagery
    Palubinskas, G
    Müller, R
    Reinartz, P
    [J]. ALGORITHMS AND TECHNOLOGIES FOR MULTISPECTRAL, HYPERSPECTRAL, AND ULTRASPECTRAL IMAGERY VIII, 2002, 4725 : 104 - 115
  • [39] Fusion of Multispectral Imagery and Spectrometer Data in UAV Remote Sensing
    Zeng, Chuiqing
    King, Douglas J.
    Richardson, Murray
    Shan, Bo
    [J]. REMOTE SENSING, 2017, 9 (07):
  • [40] Modern Methods Applied in Remote Sensing Image Fusion
    Hugianu, R.
    Dima, M.
    Telisca, M.
    Hraniciuc, T.
    [J]. MODERN TECHNOLOGIES FOR THE 3RD MILLENNIUM, 2019, : 31 - 36