• Laser & Optoelectronics Progress
  • Vol. 58, Issue 5, 0528001 (2021)
Hui Bai and Fengbao Yang*
Author Affiliations
  • College of Information and Communication Engineering, North University of China, Taiyuan , Shanxi 030051, China
  • show less
    DOI: 10.3788/LOP202158.0528001 Cite this Article Set citation alerts
    Hui Bai, Fengbao Yang. LiDAR Data Classification Method Based on High Recognition Compound Derivative Feature[J]. Laser & Optoelectronics Progress, 2021, 58(5): 0528001 Copy Citation Text show less
    References

    [1] Chen S Y, Cheng X W. The principle and application of airborne LIDAR. Engineering of Surveying and Mapping, 16, 27-31(2007).

    [2] Maltezos E, Doulamis A, Doulamis N et al. Building extraction from LiDAR data applying deep convolutional neural networks. IEEE Geoscience and Remote Sensing Letters, 16, 155-159(2019).

    [3] Li K. Visualization of full waveform data of airborne lidar(2012).

    [4] Guo Y D, Wang X K, Su D P et al. Building orthogonal boundary extraction for airborne LiDAR based on directional prediction regularization. Laser & Optoelectronics Progress, 57, 062801(2020).

    [5] Shi X S, Cheng Y L, Xue D D et al. Object classification method for multi-source fusion point clouds based on point-net. Laser & Optoelectronics Progress, 57, 081019(2020).

    [6] Yang S J, Zhang K S, Shao Y S. Classification of airborne LiDAR point cloud data based on multiscale adaptive features. Acta Optica Sinica, 39, 0228001(2019).

    [7] Wang H Z, Glennie C. Fusion of waveform LiDAR data and hyperspectral imagery for land cover classification. ISPRS Journal of Photogrammetry and Remote Sensing, 108, 1-11(2015).

    [8] Chen Y H, Han Z G. Classification of a weighted combination of remote sensing image based on D-S evidence theory. Science Technology and Engineering, 13, 5970-5973,5977(2013).

    [9] Yang F B, Wang X X. Combination method of conflictive evidences in D-S evidence theory(2010).

    [10] Feng P P. Research of improving the accuracy of land-cover fast classification method based on LIDAR data(2016).

    [11] Gitelson A A, Merzlyak M N. Remote sensing of chlorophyll concentration in higher plant leaves. Advances in Space Research, 22, 689-692(1998).

    [12] Hu B Q. The basis of fuzzy theory, 291-300(2004).

    [13] Yang F B, Ji L N, Wang X X. Possibility theory and application(2019).

    Hui Bai, Fengbao Yang. LiDAR Data Classification Method Based on High Recognition Compound Derivative Feature[J]. Laser & Optoelectronics Progress, 2021, 58(5): 0528001
    Download Citation