Infrared and Laser Engineering, Volume. 50, Issue 10, 20200518(2021)

Classification of coal/rock based on Hyperspectral LiDAR calibration-free signals

Zixin He... Hui Shao*, Hang Guo and Jie Chen |Show fewer author(s)
Author Affiliations
  • School of Electronic and Information Engineering, Anhui Jianzhu University, Hefei 230601, China
  • show less
    References(33)

    [1] Feng Qun, Chen Hong. The safety-level gap between China and the US in view of the interaction between coal production and safety management. Safety Science, 54, 80-86(2013).

    [2] Qingjie Qi, Xiaoliang Zhao, Baichao Song. Pre-evaluation method of coal mine safety based on continental distance model with varying weight. Procedia Earth and Planetary Science, 1, 180-185(2009).

    [3] Weihua Song, Hongwei Zhang. Regional prediction of coal and gas outburst hazard based on multi-factor pattern recognition. Procedia Earth and Planetary Science, 1, 347-353(2009).

    [4] V M Khatik, A K Nandi. A generic method for rock mass classification. Journal of Rock Mechanics and Geotechnical Engineering, 10, 106-120(2018).

    [5] L Weidner, G Walton, R A Kromer. Classification methods for point clouds in rock slope monitoring: A novel machine learning approach and comparative analysis. Engineering Geology, 263, 105326(2019).

    [6] A K Ryan, Jean H D, M J Lato, et al. Identifying rock slope failure precursors using LiDAR for transportation corridor hazard management. Engineering Geology, 195, 93-103(2015).

    [7] P Hartzell, C Glennie, K Biber, et al. Application of multispectral LiDAR to automated virtual outcrop geology. ISPRS Journal of Photogrammetry and Remote Sensing, 88, 147-155(2014).

    [8] Yuwei Chen, Changhui Jiang, Juha Hyyppa, et al. Feasibility study of ore classification using active hyperspectral LiDAR. IEEE Geoscience and Remote Sensing Letters, 15, 1785-1789(2018).

    [9] Bingyi Liu, Quanfeng Zhuang, Shengguang Qin, et al. Aerosol classification method based on high spectral resolution lidar. Infrared and Laser Engineering, 46, 0411001(2017).

    [10] Junfa Dong, Jiqiao Liu, Xiaolei Zhu, et al. Splitting ratio optimization of spaceborne high spectral resolution lidar. Infrared and Laser Engineering, 48, S205001(2019).

    [11] Junjie Xu, Lingbing Bu, Jiqiao Liu, et al. Airborne high-spectral-resolution lidar for atmospheric aerosol detection. Chinese Journal of Lasers, 47, 0710003(2020).

    [12] Morsdorf, Nichol, Malthus, et al. Assessing forest structural and physiological information content of multi-spectral LiDAR waveforms by radiative transfer modelling. Remote Sensing of Environment, 113, 2152-2163(2009).

    [13] Yuwei Chen, E Räikkönen, S Kaasalainen, et al. Two-channel hyperspectral LiDAR with a supercontinuum laser source. Sensors, 10, 7057-7066(2010).

    [14] K Sanna, J Anttoni, K Mikko, et al. Analysis of incidence angle and distance effects on terrestrial laser scanner intensity: search for correction methods. Remote Sensing, 3, 2207-2221(2011).

    [15] M A F Balduzzi, V D Z Dimitry, J Stuckens, et al. The properties of terrestrial laser system intensity for measuring leaf geometries: A case study with conference pear trees (Pyrus Communis). Sensors, 11, 1657-1681(2011).

    [16] B Hoefle, N Pfeifer. Correction of laser scanning intensity data: Data and model-driven approaches. ISPRS Journal of Photogrammetry and Remote Sensing, 62, 415-433(2007).

    [17] W Y Yan, A Shaker, A Habib, et al. Improving classification accuracy of airborne LiDAR intensity data by geometric calibration and radiometric correction. ISPRS Journal of Photogrammetry and Remote Sensing, 67, 35-44(2012).

    [18] Hui Shao, Yuwei Chen, Zhirong Yang, et al. A 91-channel hyperspectral LiDAR for coal/rock classification. IEEE Geoscience and Remote Sensing Letters, 17, 1052-1056(2020).

    [19] Yuwei Chen, Wei Li, H Juha, et al. A 10-nm spectral resolution hyperspectral LiDAR system based on an acousto-optic tunable filter. Sensors, 19, 1620(2019).

    [20] R CoifmanR, M V Wickerhauser. Entropy-based algorithms for best basis selection. IEEE Transactions on Information Theory, 38, 713-718(1992).

    [21] D J Diner, F Xu, J GarayM, et al. The Airborne Multiangle Spectro Polarimetric Imager (AirMSPI): A new tool for aerosol and cloud remote sensing. Atmospheric Measurement Techniques, 6, 2007-2025(2013).

    [22] Jikai Chen, Guoqing Li. Tsallis wavelet entropy and its application in power signal analysis. Entropy, 16, 3009-3025(2014).

    [23] A Hovi, L Korhonen, J Vauhkonen, et al. LiDAR waveform features for tree species classification and their sensitivity to tree- and acquisition related parameters. Remote Sensing of Environment, 173, 224-237(2016).

    [24] Dianpeng Su, Fanlin Yang, Yue Ma, et al. Classification of coral reefs in the south China sea by combining airborne LiDAR bathymetry bottom waveforms and bathymetric features. IEEE Transactions on Geoscience and Remote Sensing, 57, 815-828(2019).

    [25] Zhujun Gu, Sen Cao, G A Sanchez-Azofeifa. Using LiDAR waveform metrics to describe and identify successional stages of tropical dry forests. ITC Journal, 73, 482-492(2018).

    [26] Lei Guo, Weiwei Chang, Chaoyang Fu. Band selection of optimal for hyperspectral image fusion. Journal of Astronautics, 32, 374-379(2011).

    [27] C W Chan, P Desiré. Evaluation of random forest and adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery. Remote Sensing of Environment, 112, 2999-3011(2008).

    [28] N Ghasemian, M Akhoondzadeh. Introducing two Random Forest based methods for cloud detection in remote sensing images. Advances in Space Research, 62, 288-303(2018).

    [29] Chunhui Zhao, Bing Gao, Lejun Zhang, et al. Classification of hyperspectral imagery based on spectral gradient, SVM and spatial random forest. Infrared Physics and Technology, 95, 61-69(2018).

    [30] I V Emma, Z M Raúl. An evaluation of guided regularized random forest for classification and regression tasks in remote sensing. International Journal of Applied Earth Observations and Geoinformation, 88, 102051(2020).

    [31] F Pedregosa, G Varoquaux, A Gramfort, et al. Scikit-learn: Machine learning in python. Journal of Machine Learning Research, 12, 2825-2830(2011).

    [32] Hui Shao, Yuwei Chen, Wei Li, et al. An investigation of spectral band selection for hyperspectral LiDAR technique. Electronics, 9, 148(2020).

    [33] Shouxun Yan, Bing Zhang, Yongchao Zhao, et al. Summarizing the VIS-NIR spectra of minerals and rocks. Remote Sensing Technology and Application, 18, 191-201(2003).

    CLP Journals

    [1] Hui Shao, Beining Sa, Wei Li, Yuwei Chen, Lu Liu, Jie Chen, Long Sun, Yuxia Hu. A design and implementation of full waveform hyperspectral LiDAR for ancient architecture modelling[J]. Infrared and Laser Engineering, 2022, 51(8): 20210786

    [2] Shilong Xu, Yuhao Xia, Jiajie Dong, Qishu Qian. Lidar point cloud expansion and identification method for masking targets based on time-spectra information[J]. Infrared and Laser Engineering, 2023, 52(6): 20230213

    Tools

    Get Citation

    Copy Citation Text

    Zixin He, Hui Shao, Hang Guo, Jie Chen. Classification of coal/rock based on Hyperspectral LiDAR calibration-free signals[J]. Infrared and Laser Engineering, 2021, 50(10): 20200518

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category: Lasers & Laser optics

    Received: Dec. 9, 2020

    Accepted: --

    Published Online: Dec. 7, 2021

    The Author Email: Shao Hui (shaohui@ahjzu.edu.cn)

    DOI:10.3788/IRLA20200518

    Topics