浏览全部资源
扫码关注微信
纸质出版日期： 2020-12-07 ，
扫 描 看 全 文
秦磊，邢艳秋，黄佳鹏，马建明，安立华.2020.ICESat-2机载实验光子云数据自适应去噪及分类算法.遥感学报，24（12）： 1476-1487
Qin L，Xing Y Q，Huang J P，Ma J M and An L H. 2020. Adaptive denoising and classification algorithms for ICESat-2 airborne experimental photon cloud data of 2018. Journal of Remote Sensing（Chinese）， 24（12）：1476-1487
第二代星载激光雷达冰、云和陆地测高卫星ICESat-2（The Ice， Cloud， and Land Elevation Satellite-2）搭载了先进光子计数式激光雷达，使用了全新的微脉冲多波束光子计数式激光雷达。由于光子计数式激光雷达的自身特点，其光子云数据具有受噪声光子影响大、信噪比与扫描时间相关、光子分布密度不均匀等问题，目前开发的去噪算法并不能很好的应用于不同的光子云数据。基于以上问题，本文提出一种改进的去噪算法，首先分析光子云内部特征并自适应选择最优参数进行粗去噪，然后进行两次精去噪，最后对光子云进行分类并拟合出地面线及冠层顶线，为提取森林冠层高度提供基础。使用该算法对MABEL数据进行去噪实验，实验结果表明：该去噪算法的一次去噪对不同环境下MABEL数据在夜间的去噪平均精确度为94.5%，F1-score为96.3%，日间平均精确度为86.7%，F1-score为91.7%，且三次去噪算法完成后能够显著提升光子云去噪精度。实验证明该算法对MABEL光子云数据具有较好去噪效果和稳定性，可为ICESat-2数据处理提供参考。本文的光子分类算法能够从光子数据中提取冠层顶点、地面点及林内光子并在此算法中进一步精确去除剩余噪点，最终光子分类结果显示该算法能够从复杂光子云数据中提取森林剖面结构。
The ice
cloud and land elevation satellite-2 (ICESat-2) is equipped with advanced photon counting lidar. The system is a multi-beam micro pulse photon counting radar
which has the advantages of low energy consumption
high measurement sensitivity
high repetition rate and high space operation altitude. However
due to the characteristics of the lidar
the data returned is the elevation profile photon cloud data. Due to the nature of the instrument
the data is easily affected by noise photons
observation time
observation area and so on. The photon cloud data contains a lot of background light noise. Before using the photon cloud data for canopy extraction
the efficient and high-precision photon denoising and classification algorithm is as follows It's very necessary.
Based on the above problems
this paper proposes an improved triple denoising algorithm. Firstly
DBSCAN clustering algorithm is selected for the coarse denoising of photons. The eps parameters of clustering algorithm have a great influence on it. In this paper
by analyzing the correlation between the density of photon cloud and the parameters of the algorithm and the denoising results
it is proposed to select the optimal eps parameters adaptively for rough denoising according to the internal characteristics of the photon cloud The signal photons are not lost and the noise photons are removed effectively. Then
two fine denoising algorithms are carried out to remove the noise photons located at the top of the canopy and below the ground line. Finally
the optical cloud is classified and fitted to the ground line and the canopy top line. Finally
the remaining noise photons are removed according to the fitting ground line and canopy top line interval.
In this paper
the algorithm is applied to ICESat-2 airborne test data (MABEL). The experimental results show that the average de-noising accuracy of the algorithm is 94.5% for nighttime data
96.3% for F1-score
86.7% for daytime data and 91.7% for F1-score. The results show that the denoising parameters can be selected adaptively according to the photon density of the data However
the results also show that it can not achieve good results in areas where the density of signal photons and noise photons is excessively similar. However
the overall accuracy evaluation shows that the F1-score of all segments is 91%
92% and 95% respectively in the three times denoising algorithm. The results show that the following two denoising algorithms can accurately remove most of the remaining noise photons which are not completely removed
and significantly improve the denoising accuracy of photon cloud
which provides a guarantee for the accurate extraction of the subsequent photon categories. The overall experimental results show that the algorithm has good denoising effect and stability for MABEL photon cloud data. In the section data of denoising
the qualitative results show that the photon classification algorithm in this paper can select the canopy vertex
ground point and forest photons from the photon data based on the denoising results. The final photon classification results show that the algorithm can extract the forest profile structure from the complex photon cloud data
and retain most of the signal photons in the canopy
which can provide some reference for the subsequent tree height extraction and biomass calculation of ICESat-2 data.
遥感ICESat-2MABEL光子云DBSCAN去噪分类
remote sensingICESat-2MABELphoton cloudDBSCANdenoisingclassification
Bae S and Webb C E. 2017. Precision attitude determination with an extended Kalman filter to measure ice-sheet elevation. Journal of Guidance, Control, and Dynamics, 40(9): 2335-2340 [DOI: 10.2514/1.G002715http://dx.doi.org/10.2514/1.G002715]
Feng S R and Xiao W J. 2007. Research and application of DBSCAN clustering algorithm based on density. Computer Engineering and Applications, 43(20): 216-221
冯少荣, 肖文俊. 2007. 基于密度的DBSCAN聚类算法的研究及应用. 计算机工程与应用, 43(20): 216-221 [DOI: 10.3321/j.issn:1002-8331.2007.20.064http://dx.doi.org/10.3321/j.issn:1002-8331.2007.20.064]
Forfinski-Sarkozi N A and Parrish C E. 2016. Analysis of MABEL bathymetry in keweenaw bay and implications for ICESat-2 ATLAS. Remote Sensing, 8(9): 772 [DOI: 10.3390/rs8090772http://dx.doi.org/10.3390/rs8090772]
Gwenzi D, Lefsky M A, Suchdeo V P and Harding D J. 2016. Prospects of the ICESat-2 laser altimetry mission for savanna ecosystem structural studies based on airborne simulation data. ISPRS Journal of Photogrammetry and Remote Sensing, 118: 68-82 [DOI: 10.1016/j.isprsjprs.2016.04.009http://dx.doi.org/10.1016/j.isprsjprs.2016.04.009]
Jasinski M F, Stoll J D, Cook W B, Ondrusek M, Stengel E and Brunt K. 2016. Inland and near-Shore water profiles derived from the high-altitude multiple altimeter beam experimental lidar (MABEL). Journal of Coastal Research, 76(sp1): 44-55 [DOI: 10.2112/SI76-005http://dx.doi.org/10.2112/SI76-005]
Lefsky M A, Harding D J, Keller M, Cohen W B, Carabajal C C, Del Bom Espirito-Santo F, Hunter M O and de Oliveira Jr R. 2005. Estimates of forest canopy height and aboveground biomass using ICESat. Geophysical Research Letters, 32(22): L22S02 [DOI: 10.1029/2005GL023971http://dx.doi.org/10.1029/2005GL023971]
Magruder L A and Brunt K M. 2018. Performance analysis of airborne photon- counting lidar data in preparation for the ICESat-2 mission. IEEE Transactions on Geoscience and Remote Sensing, 56(5): 2911-2918 [DOI: 10.1109/TGRS.2017.2786659http://dx.doi.org/10.1109/TGRS.2017.2786659]
McGill M, Markus T, Scott V S and Neumann T. 2013. The Multiple Altimeter Beam Experimental Lidar (MABEL): an airborne simulator for the ICESat-2 mission. Journal of Atmospheric and Oceanic Technology, 30(2): 345-352 [DOI: 10.1175/JTECH-D-12-00076.1http://dx.doi.org/10.1175/JTECH-D-12-00076.1]
Nie S, Wang C, Xi X H, Luo S Z, Li G Y, Tian J Y and Wang H T T. 2018. Estimating the vegetation canopy height using micro-pulse photon-counting LiDAR data. Optics Express, 26(10): A520-A540 [DOI: 10.1364/OE.26.00A520http://dx.doi.org/10.1364/OE.26.00A520]
Popescu S C, Zhou T, Nelson R, Neuenschwander A, Sheridan R, Narine L and Walsh K M. 2018. Photon counting LiDAR: an adaptive ground and canopy height retrieval algorithm for ICESat-2 data. Remote Sensing of Environment, 208: 154-170 [DOI: 10.1016/j.rse.2018.02.019http://dx.doi.org/10.1016/j.rse.2018.02.019]
Tang H, Swatantran A, Barrett T, DeCola P and Dubayah R. 2016. Voxel-based spatial filtering method for canopy height retrieval from airborne single-photon lidar. Remote Sensing, 8(9): 771 [DOI: 10.3390/rs8090771http://dx.doi.org/10.3390/rs8090771]
Wang X, Pan Z G and Glennie C. 2016. A novel noise filtering model for photon-counting laser altimeter data. IEEE Geoscience and Remote Sensing Letters, 13(7): 947-951 [DOI: 10.1109/LGRS.2016.2555308http://dx.doi.org/10.1109/LGRS.2016.2555308]
Xia S B, Wang C, Xi X H, Luo S Z and Zeng H C. 2014. Point cloud filtering and tree height estimation using airborne experiment data of ICESat-2. Journal of Remote Sensing, 18(6): 1199-1207
夏少波, 王成, 习晓环, 骆社周, 曾鸿程. 2014. ICESat-2机载试验点云滤波及植被高度反演. 遥感学报, 18(6): 1199-1207 [DOI: 10.11834/jrs.20144029http://dx.doi.org/10.11834/jrs.20144029]
Xu Y T. 2017. Research on the Date Processing Technology of Single Photon Laser Altimetry—Taking MABEL Data as a Example. Xi’an: Xi’an University of Science and Technology
许艺腾. 2017. 单光子激光测高数据处理技术研究——以MABEL数据为例. 西安: 西安科技大学
Xue A R, Yao L, Ju S G, Chen W H and Ma H D. 2008. Survey of outlier mining. Computer Science, 35(11): 13-18, 27
薛安荣, 姚林, 鞠时光, 陈伟鹤, 马汉达. 2008. 离群点挖掘方法综述. 计算机科学, 35(11): 13-18, 27 [DOI: 10.3969/j.issn.1002-137X.2008.11.003http://dx.doi.org/10.3969/j.issn.1002-137X.2008.11.003]
Yang F, Wen J H and Wang W L. 2011. ICESat and ICESat-2 applications: progress and prospect. Chinese Journal of Polar Research, 23(2): 138-148
杨帆, 温家洪, Wang W L. 2011. ICESat与ICESat-2应用进展与展望. 极地研究, 23(2): 138-148
Zhang J S and Kerekes J. 2015. An adaptive density-based model for extracting surface returns from photon-counting laser altimeter data. IEEE Geoscience and Remote Sensing Letters, 12(4): 726-730 [DOI: 10.1109/LGRS.2014.2360367http://dx.doi.org/10.1109/LGRS.2014.2360367]
相关作者
相关机构
京公网安备11010802024621