Message Board

Respected readers, authors and reviewers, you can add comments to this page on any questions about the contribution, review, editing and publication of this journal. We will give you an answer as soon as possible. Thank you for your support!

Full name
E-mail
Phone number
Title
Message
Verification Code
Volume 45 Issue 10
Oct.  2023
Turn off MathJax
Article Contents
Ji Xue,Dong Zhen,Zhang Jingyu, et al. Identification and correction of airborne laser bathymetry intensity data in AGC compensated abnormal zone[J]. Haiyang Xuebao,2023, 45(10):159–167 doi: 10.12284/hyxb2023133
Citation: Ji Xue,Dong Zhen,Zhang Jingyu, et al. Identification and correction of airborne laser bathymetry intensity data in AGC compensated abnormal zone[J]. Haiyang Xuebao,2023, 45(10):159–167 doi: 10.12284/hyxb2023133

Identification and correction of airborne laser bathymetry intensity data in AGC compensated abnormal zone

doi: 10.12284/hyxb2023133
  • Received Date: 2023-02-26
  • Rev Recd Date: 2023-06-01
  • Available Online: 2023-11-06
  • Publish Date: 2023-10-30
  • Airborne Laser Bathymetry (ALB) as an established surveying procedure is certainly operated in blue-green region (532 nm) for penetrating the water column to collect depth. Alongside geometric information accepted widely, ALB typically record the radiometric properties (backscattering intensity) about sensed targets and assist with accurate strips registration, fine ground (sediment) cover classification, and advanced geometric modelling. However, due to the design limitations of Automatic Gain Control (AGC), there is a delay in gain value adjustment, particularly prominent in island and coastal areas with more high-return and low-return targets such as exposed rocks and water, which throws the issue of intensity compensation abnormality into stark relief. In response to this issue, a local weighted intensity correction method based on bidirectional movement is designed. Firstly, effective intensity is extracted by index sharing and elevation information. Then, the emission angles are used to divide the scan line and serve as the judgment unit, and the Kolmogorov-Smirnov test is used to identify the abnormal area. Finally, intensity correction is performed by joint weighting of adjacent scan lines and neighborhood intensity, where the joint weighting can ensure the intensity details while eliminating the deviation from the neighborhood intensity. In addition, a unique bidirectional moving strategy is introduced to weaken the decline of strength correction accuracy caused by insufficient correction accumulation. Experiments have illustrated that this approach can effectively solve the AGC compensation anomaly problem, and compared to the pre-correction, the average absolute percentage error of the corrected intensity data decreased by about 0.27, the root mean square error decreased by about 693, and the intensity deviation of the abnormal area was controlled within 26 DN (Digital Number), thus obtaining a high-quality intensity image.
  • loading
  • [1]
    Su Dianpeng, Yang Fanlin, Ma Yue, et al. Classification of coral reefs in the South China Sea by combining airborne LiDAR bathymetry bottom waveforms and bathymetric features[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(2): 815−828. doi: 10.1109/TGRS.2018.2860931
    [2]
    Narayanan R, Kim H B, Sohn G. Classification of SHOALS 3000 bathymetric LiDAR signals using decision tree and ensemble techniques[C]//2009 IEEE Toronto International Conference Science and Technology for Humanity (TIC-STH). Toronto, ON, Canada: IEEE, 2009.
    [3]
    刘焱雄, 郭锴, 何秀凤, 等. 机载激光测深技术及其研究进展[J]. 武汉大学学报(信息科学版), 2017, 42(9): 1185−1194.

    Liu Yanxiong, Guo Kai, He Xiufeng, et al. Research progress of airborne laser bathymetry technology[J]. Geomatics and Information Science of Wuhan University, 2017, 42(9): 1185−1194.
    [4]
    Ji Xue, Yang Bisheng, Tang Qiuhua, et al. A coarse-to-fine strip mosaicing model for airborne bathymetric LiDAR data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(10): 8129−8142. doi: 10.1109/TGRS.2021.3050789
    [5]
    Ji Xue, Yang Bisheng, Wang Yuan, et al. Full-waveform classification and segmentation-based signal detection of single-wavelength bathymetric LiDAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 4208714.
    [6]
    Abdallah H, Baghdadi N, Bailly J S, et al. Wa-LiD: a new LiDAR simulator for waters[J]. IEEE Geoscience and Remote Sensing Letters, 2012, 9(4): 744−748. doi: 10.1109/LGRS.2011.2180506
    [7]
    Eren F, Pe’eri S, Rzhanov Y, et al. Bottom characterization by using airborne lidar bathymetry (ALB) waveform features obtained from bottom return residual analysis[J]. Remote Sensing of Environment, 2018, 206: 260−274. doi: 10.1016/j.rse.2017.12.035
    [8]
    Zavalas R, Ierodiaconou D, Ryan D, et al. Habitat classification of temperate marine macroalgal communities using bathymetric LiDAR[J]. Remote Sensing, 2014, 6(3): 2154−2175. doi: 10.3390/rs6032154
    [9]
    Long B F, Aucoin F, Montreuil S, et al. Airborne lidar bathymetry applied to coastal hydrodynamic processes[C]. Coastal Engineering Proceedings. 2011, 1(32): 1−12.
    [10]
    Ji Xue, Yang Bisheng, Tang Qiuhua, et al. Feature fusion-based registration of satellite images to airborne LiDAR bathymetry in island area[J]. International Journal of Applied Earth Observation and Geoinformation, 2022, 109: 102778. doi: 10.1016/j.jag.2022.102778
    [11]
    Höfle B, Pfeifer N. Correction of laser scanning intensity data: Data and model-driven approaches[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2007, 62(6): 415−433. doi: 10.1016/j.isprsjprs.2007.05.008
    [12]
    Oren M, Nayar S K. Generalization of the Lambertian model and implications for machine vision[J]. International Journal of Computer Vision, 1995, 14(3): 227−251. doi: 10.1007/BF01679684
    [13]
    Yoon J S, Shin J I, Lee K S. Land cover characteristics of airborne LiDAR intensity data: a case study[J]. IEEE Geoscience and Remote Sensing Letters, 2008, 5(4): 801−805. doi: 10.1109/LGRS.2008.2000754
    [14]
    Richter K, Maas H G. Radiometric enhancement of full-waveform airborne laser scanner data for volumetric representation in environmental applications[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2022, 183: 510−524. doi: 10.1016/j.isprsjprs.2021.10.021
    [15]
    Kashani A G, Olsen M J, Parrish C E, et al. A review of LIDAR radiometric processing: from Ad Hoc intensity correction to rigorous radiometric calibration[J]. Sensors, 2015, 15(11): 28099−28128. doi: 10.3390/s151128099
    [16]
    Lin Y C. Normalization of echo features derived from full-waveform airborne laser scanning data[J]. Remote Sensing, 2015, 7(3): 2731−2751. doi: 10.3390/rs70302731
    [17]
    Peeri S, Gardner J V, Ward L G, et al. The seafloor: a key factor in Lidar bottom detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(3): 1150−1157. doi: 10.1109/TGRS.2010.2070875
    [18]
    Wang C K, Philpot W D. Using airborne bathymetric Lidar to detect bottom type variation in shallow waters[J]. Remote Sensing of Environment, 2007, 106(1): 123−135. doi: 10.1016/j.rse.2006.08.003
    [19]
    Philips D M, Abbot R H, Penny M F. Remote sensing of sea water turbidity with an airborne laser system[J]. Journal of Physics D: Applied Physics, 1984, 17(8): 1749. doi: 10.1088/0022-3727/17/8/028
    [20]
    Vain A, Yu Xiaowei, Kaasalainen S, et al. Correcting airborne laser scanning intensity data for automatic gain control effect[J]. IEEE Geoscience and Remote Sensing Letters, 2010, 7(3): 511−514. doi: 10.1109/LGRS.2010.2040578
    [21]
    Korpela I, Ørka H O, Hyyppä J, et al. Range and AGC normalization in airborne discrete-return LiDAR intensity data for forest canopies[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2010, 65(4): 369−379. doi: 10.1016/j.isprsjprs.2010.04.003
    [22]
    Korpela I S. Mapping of understory lichens with airborne discrete-return LiDAR data[J]. Remote Sensing of Environment, 2008, 112(10): 3891−3897. doi: 10.1016/j.rse.2008.06.007
    [23]
    刘健, 陈亮, 王驹, 等. 二维K-S检验法在岩体统计均质区划分中的应用[J]. 岩土工程学报, 2019, 41(12): 2374−2380.

    Liu Jian, Chen Liang, Wang Ju, et al. Application of 2D K-S tests to evaluating statistical homogeneity of rock mass[J]. Chinese Journal of Geotechnical Engineering, 2019, 41(12): 2374−2380.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(6)  / Tables(2)

    Article views (202) PDF downloads(11) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return