• Infrared and Laser Engineering
  • Vol. 48, Issue 11, 1113004 (2019)
Hu Shanjiang1、2、*, He Yan1, Tao Bangyi3, Yu Jiayong4, and Chen Weibiao1
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
  • 3[in Chinese]
  • 4[in Chinese]
  • show less
    DOI: 10.3788/irla201948.1113004 Cite this Article
    Hu Shanjiang, He Yan, Tao Bangyi, Yu Jiayong, Chen Weibiao. Classification of sea and land waveforms based on deep learning for airborne laser bathymetry[J]. Infrared and Laser Engineering, 2019, 48(11): 1113004 Copy Citation Text show less
    References

    [1] Tuell G, Barbor K, Wozencraft J. Overview of the coastal zone mapping and imaging lidar (CZMIL): A new multisensor airborne mapping system for the US army corps of engineers[C]//Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI. International Society for Optics and Photonics, 2010, 7695: 76950R.

    [2] Baltsavias E P. Airborne laser scanning: existing systems and firms and other resources[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 1999, 54(2-3): 164-198.

    [3] Pe′eri S, Morgan L V, Philpot W D, et al. Land-water interface resolved from airborne LiDAR bathymetry (ALB) waveforms[J]. Journal of Coastal Research, 2011,62: 75-85.

    [4] Collin A, Long B, Archambault P. Merging land-marine realms: Spatial patterns of seamless coastal habitats using a multispectral LiDAR[J]. Remote Sensing of Environment, 2012, 123: 390-399.

    [6] Huang Tiancheng, Tao Bangyi, Mao Zhihua, et al. Classification of sea and land waveform based on multi-channel ocean lidar[J]. Chinese Journal of Lasers, 2017, 44(6): 0610002. (in Chinese)

    [7] Ma Yue, Zhang Wenhao, Zhang Zhiyu, et al. Sea and sea-ice waveform classification for the laser altimeter based on semi-analytic model[J]. Infrared and Laser Engineering, 2018, 47(5): 0506005. (in Chinese)

    [8] Nahhas F H, Shafri H Z M, Sameen M I, et al. Deep learning approach for building detection using lidar-orthophoto fusion[J]. Journal of Sensors, 2018:7212307.

    [9] Hu X, Yuan Y. Deep-learning-based classification for DTM extraction from ALS point cloud[J]. Remote Sensing, 2016, 8(9): 730.

    [10] Arief H, Strand G H, Tveite H, et al. Land cover segmentation of airborne LiDAR data using stochastic atrous network[J]. Remote Sensing, 2018, 10(6): 973.

    [11] Maturana D, Scherer S. 3d convolutional neural networks for landing zone detection from lidar[C]//2015 IEEE International Conference on Robotics and Automation (ICRA), 2015: 3471-3478.

    [12] Velas M, Spanel M, Hradis M, et al. Cnn for very fast ground segmentation in velodyne lidar data[C]//2018 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), 2018: 97-103.

    [13] Matti D, Ekenel H K, Thiran J P. Combining lidar space clustering and convolutional neural networks for pedestrian detection[C]//2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 2017: 1-6.

    [14] Dewan A, Oliveira G L, Burgard W. Deep semantic classification for 3d lidar data[C]//2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2017: 3544-3549.

    [15] Wang A, He X, Ghamisi P, et al. Lidar data classification using morphological profiles and convolutional neural networks[J]. IEEE Geoscience and Remote Sensing Letters, 2018, 15(5): 774-778.

    [16] LeCun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324.

    [17] Dai W, Dai C, Qu S, et al. Very deep convolutional neural networks for raw waveforms[C]//2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017: 421-425.

    [18] Zhao Ming, Chen Shi, Yuen Dave. Waveform classification and seismic recognition by convolution neural network[J]. Chinese Journal of Geophysics, 2019, 62(1): 374-382. (in Chinese)

    CLP Journals

    [1] Yiqiang Zhao, Qi Zhang, Changlong Liu, Weikang Wu, Yao Li. Airborne LiDAR data classification method combining physical and geometric characteristics[J]. Infrared and Laser Engineering, 2023, 52(11): 20230212

    [2] Bo Liu, Yun Jiang, Rui Wang, Zhen Chen, Bin Zhao, Fengyun Huang, Yuqiang Yang. Technical progress and system evaluation of all-time single photon lidar[J]. Infrared and Laser Engineering, 2023, 52(1): 20220748

    [3] Jiachuan Sheng, Yaqi Chen, Jun Wang, Yahong Han. Image sentiment classification via deep learning structure optimization[J]. Infrared and Laser Engineering, 2020, 49(11): 20200269

    [4] Xu Zhang, Mingxin Yu, Lianqing Zhu, Yanlin He, Guangkai Sun. Raman mineral recognition method based on all-optical diffraction deep neural network[J]. Infrared and Laser Engineering, 2020, 49(10): 20200221

    Hu Shanjiang, He Yan, Tao Bangyi, Yu Jiayong, Chen Weibiao. Classification of sea and land waveforms based on deep learning for airborne laser bathymetry[J]. Infrared and Laser Engineering, 2019, 48(11): 1113004
    Download Citation