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基于地面激光强度校正数据的单木枝叶分离

Separation of Single Wood Branches and Leaves Based on Corrected TLS Intensity Data

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摘要

由于受到角度、距离等因素影响,同种地物反射的激光强度数据存在较大的偏差,需要建立校正模型使强度数据能准确反映地物信息,并快速提取。从激光雷达测距方程出发,设计校正实验,运用多项式模型拟合强度数据校正方程,通过定义其标准值,改正距离和高度影响下的反射强度。采用阈值法和随机森林法进行立木枝叶分离。利用二次多项式对七类材质分别进行校正,校正后各材质强度数据级差均小于0.1,不受距离、高度影响,可以区分主干和叶片;活立木强度数据经过校正可以得到枝叶分离的阈值;随机森林法的最终分类效果较好,两树种叶模型校正分类精度分别为91.5%和84.3%。本研究建立的校正模型方法,对自然漫反射目标物的激光强度数据能够进行较为精确的校正,选用的自然立木验证实验校正成功,为进一步进行立木的枝叶分离提供了可能性。

Abstract

Objective Lidar scanning obtain point cloud date can not only directly measure the three-dimensional (3D) model of the object, but also reveal the intensity of the object. The laser intensity data reflects a variety of the characteristics of the target surface, which can be applied for registration of different measuring stations and filtering of point cloud data. It can also be used to extract and classify the target object by using the intensity data or combining the intensity data with the point cloud RGB data, so as to provide a basis information for feature extraction and leaf area calculation and biomass estimation. However, the influence factors such as the angle and distance will impact the laser intensity data of the same features. These deviations reduce the accuracy of point cloud registration, classification, and extraction, which is not conducive to the full use of point cloud information. Therefore, it is needed to establish a correction model to make the intensity data accurately reflects the feature information for rapid extraction.

Methods Based on lidar ranging equation, first, a pre experiment processing is designed to predict the influence of scanning background, illumination change, and leaf inclination on the intensity data of the research objects. Second, the polynomial models are analyzed to fit the intensity data corrected equation, and the standard value of each material is defined through indoor experiment. Through the model, the angle, which is not easy to measure, is converted into height. After that, the intensity correction models of seven different materials (ginkgo leaves front and back, ginkgo branch, white paper, soapberry leaves front and back, and soapberry branch), with six different distances (2 m, 3 m, 4 m, 5 m, 6 m, and 7 m) and six different heights (0 m, 0.5 m, 1 m, 1.5 m, 2 m, and 2.5 m) is going to be established. Third, the coordinate transformation method of outdoor standing trees (two species: ginkgo and soapberry) is designed, and the correction model is used to obtain the corrected data of the reflectance of branches and leaves for each species. Finally, a threshold method and a random forest method are selected for intensity classification, and the classification result is analyzed to achieve the purpose of using point cloud intensity data for ground feature classification.

Results and discussions 1) In the comprehensive analysis of various correction models, quadratic polynomial usually has the characteristics of simple calculation way and good simulation effect. For the seven kinds of materials, the data range after correction is less than 0.1, and the reflection intensity of each material is more stable than raw data, and is not affected by the distance and height. 2) Before correction, the peak value of reflected intensity of point cloud with leaves is smaller than that of yellow leaves and leaves off. The reflected intensity of leaves is less than that of branches. The reflected intensity of ginkgo ranges from 0.163 to 0.506 and that of soapberry ranges from 0.182 to 0.505. After correction, the range of intensity data for all materials decreases by an order of magnitude and fluctuates within a very small range of value. Among the correction results, the minimum range appears only 0.005 in the data of front of gingko leaves. While in the data of back of gingko leaves the range is also smaller, only 0.007. 3) For standing tree experiment, reflectance for single target and all return point cloud are all drawn. Experimental results show that the curves of gingko reflected intensity are smoother. The reflected intensity interval of ginkgo point clouds is 35, that is, the reflected intensity is 0.175 with a largest number of clouds. After the correction of the leaf model and the branch model, the reflection intensity of the most standing point clouds is 0.035 and 0.065. For soapberry, the intensity interval is 18, and the reflected intensity is 0.09 with a largest number of clouds. After the correction of the leaf model and the branch model, the reflection intensity of the most standing point clouds are 0.05 and 0.065. 4) Different classification methods are used to separate leaves and branches from the corrected intensity of the two tree species, the threshold classification is more applicable. The highest accuracy of the threshold classification with row data is 37.406%. After correction, the classification accuracy of ginkgo leaf model could reach 75.780% which increases by 83%. With the addition of RGB information, the classification accuracy of the random forest model with corrected data is improved from 85.645% to 91.504% which increases by 6.8%. The accuracy of leaf model correction for both species are 91.504% and 84.323%, respectively.

Conclusions The calibration model method established in this paper can accurately correct the laser intensity data of the natural diffuse reflection target object. The selected natural standing-tree verifies the experiment successfully. Branches and leaves can be distinguished by the reflection intensity after calibration, which provides the possibility to further use point cloud data for branch and leaf separation and tree species identification.

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中图分类号:S771

DOI:10.3788/CJL202148.0104001

所属栏目:测量与计量

基金项目:江苏省自然科学基金面上项目(BK20191388)、江苏省普通高校学术学位研究生科研创新计划项目(KYCX20_0900)、南京林业大学大创项目(2020NFUSPITP0235)

收稿日期:2020-07-10

修改稿日期:2020-08-24

网络出版日期:2021-01-01

作者单位    点击查看

孙圆:南京林业大学林学院, 江苏 南京 210037南京林业大学南方现代林业协同创新中心, 江苏 南京 210037
林秀云:南京林业大学林学院, 江苏 南京 210037
熊金鑫:南京六合平山林场发展有限公司, 江苏 南京 211500
任国婧:南京林业大学林学院, 江苏 南京 210037

联系人作者:熊金鑫(sunyuan1123@126.com)

【1】Hua X H, Zhao B F, Chen X J, et al. Research and prospect of terrestrial 3D laser scanning point cloud quality evaluation technology [J]. Geospatial Information. 2018, 16(8): 1-7, 9.
花向红, 赵不钒, 陈西江, 等. 地面三维激光扫描点云质量评价技术研究与展望 [J]. 地理空间信息. 2018, 16(8): 1-7, 9.
Hua X H, Zhao B F, Chen X J, et al. Research and prospect of terrestrial 3D laser scanning point cloud quality evaluation technology [J]. Geospatial Information. 2018, 16(8): 1-7, 9.
花向红, 赵不钒, 陈西江, 等. 地面三维激光扫描点云质量评价技术研究与展望 [J]. 地理空间信息. 2018, 16(8): 1-7, 9.

【2】Junttila S, Vastaranta M, Liang X L, et al. Measuring leaf water content with dual-wavelength intensity data from terrestrial laser scanners [J]. Remote Sensing. 2016, 9(1): 8-27.

【3】Kang Z, Li J, Zhang L, et al. Automatic registration of terrestrial laser scanning point clouds using panoramic reflectance images [J]. Sensors. 2009, 9(4): 2621-2646.

【4】Tong Y, Xia M, Yang K C, et al. Target reflection feature extraction based on lidar intensity value [J]. Laser & Optoelectronics Progress. 2018, 55(10): 102802.
童祎, 夏珉, 杨克成, 等. 基于激光雷达强度值的目标反射特征提取 [J]. 激光与光电子学进展. 2018, 55(10): 102802.

【5】Li Z, Jupp D L, Strahler A H, et al. Radiometric calibration of a dual-wavelength, full-waveform terrestrial lidar [J]. Sensors. 2016, 16(3): 313-336.

【6】Zeng J J, Lu X S, Wang J, et al. Road extraction based on the echo information of LiDAR [J]. Science of Surveying and Mapping. 2011, 36(2): 142-143, 174.
曾静静, 卢秀山, 王健, 等. 基于LIDAR回波信息的道路提取 [J]. 测绘科学. 2011, 36(2): 142-143, 174.
Zeng J J, Lu X S, Wang J, et al. Road extraction based on the echo information of LiDAR [J]. Science of Surveying and Mapping. 2011, 36(2): 142-143, 174.
曾静静, 卢秀山, 王健, 等. 基于LIDAR回波信息的道路提取 [J]. 测绘科学. 2011, 36(2): 142-143, 174.

【7】Huang X M, Sun Y, Liu H Q, et al. Resolving leaf area index of individual trees based on multi-return terrestrial laser point cloud data [J]. Journal of Remote Sensing. 2018, 22(6): 1042-1050.
黄星旻, 孙圆, 刘慧倩, 等. 多回波点云数据解算单株木叶面积指数 [J]. 遥感学报. 2018, 22(6): 1042-1050.

【8】Cao L, She G H, Dai J S, et al. Status and prospects of the LiDAR-based forest biomass estimation [J]. Journal of Nanjing Forestry University (Natural Sciences Edition). 2013, 37(3): 163-169.
曹林, 佘光辉, 代劲松, 等. 激光雷达技术估测森林生物量的研究现状及展望 [J]. 南京林业大学学报(自然科学版). 2013, 37(3): 163-169.

【9】Fang W, Huang X F, Zhang F, et al. Intensity correction of terrestrial laser scanning data by estimating laser transmission function [J]. IEEE Transactions on Geoscience and Remote Sensing. 2015, 53(2): 942-951.

【10】Tan K, Cheng X J, Ding X L, et al. Intensity data correction for the distance effect in terrestrial laser scanners [J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2016, 9(1): 304-312.

【11】Xia G F, Hu C M, Cao B Z, et al. Study on the influence of laser incident angle on the reflection intensity of the point cloud [J]. Laser Journal. 2016, 37(4): 11-13.
夏国芳, 胡春梅, 曹毕铮, 等. 激光入射角度对点云反射强度的影响研究 [J]. 激光杂志. 2016, 37(4): 11-13.

【12】Carrea D, Abellan A, Humair F, et al. Correction of terrestrial LiDAR intensity channel using Oren-Nayar reflectance model: an application to lithological differentiation [J]. ISPRS Journal of Photogrammetry and Remote Sensing. 2016, 113: 17-29.Carrea D, Abellan A, Humair F, et al. Correction of terrestrial LiDAR intensity channel using Oren-Nayar reflectance model: an application to lithological differentiation [J]. ISPRS Journal of Photogrammetry and Remote Sensing. 2016, 113: 17-29.

【13】Cheng X L, Cheng X J, Li Q, et al. Laser intensity correction of terrestrial 3D laser scanning based on sectional polynomial model [J]. Laser & Optoelectronics Progress. 2017, 54(11): 112802.
程小龙, 程效军, 李泉, 等. 基于分段多项式模型的地面三维激光扫描激光强度改正 [J]. 激光与光电子学进展. 2017, 54(11): 112802.

【14】H?fle 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.

【15】Calders K, Disney M I, Armston J, et al. Evaluation of the range accuracy and the radiometric calibration of multiple terrestrial laser scanning instruments for data interoperability [J]. IEEE Transactions on Geoscience and Remote Sensing. 2017, 55(5): 2716-2724.

【16】Pfeifer N, Dorninger P, Haring A, et al. Investigating terrestrial laser scanning intensity data: quality and functional relations . [C]// International Conference on in Gruen. 2006.

【17】Niu Y C. On the generalization of weierstrass approximation theorem [J]. Journal of Inner Mongolia University for Nationalities (Natural Sciences). 2010, 25(6): 614-616.
牛英春. 关于Weierstrass逼近定理的推广 [J]. 内蒙古民族大学学报(自然科学版). 2010, 25(6): 614-616.

【18】Tan K, Cheng X J. Correction of incidence angle and distance effects on TLS intensity data based on reference targets [J]. Remote Sensing. 2016, 8(3): 251-270.Tan K, Cheng X J. Correction of incidence angle and distance effects on TLS intensity data based on reference targets [J]. Remote Sensing. 2016, 8(3): 251-270.

【19】Kaasalainen S, Pyysalo U, Krooks A, et al. Absolute radiometric calibration of ALS intensity data: effects on accuracy and target classification [J]. Sensors. 2011, 11(11): 10586-10602.

【20】Béland M, Baldocchi D D, Widlowski J L, et al. On seeing the wood from the leaves and the role of voxel size in determining leaf area distribution of forests with terrestrial LiDAR [J]. Agricultural and Forest Meteorology. 2014, 184: 82-97.

【21】Lu X Y, Yun T, Xue L F, et al. Effective feature extraction and identification method based on tree laser point cloud [J]. Chinese Journal of Lasers. 2019, 46(5): 0510002.
卢晓艺, 云挺, 薛联凤, 等. 基于树木激光点云的有效特征抽取与识别方法 [J]. 中国激光. 2019, 46(5): 0510002.

【22】Junttila S, Sugano J, Vastaranta M, et al. Can leaf water content be estimated using multispectral terrestrial laser scanning? A case study with Norway spruce seedlings [J]. Frontiers in Plant Science. 2018, 9: 299.

引用该论文

Sun Yuan,Lin Xiuyun,Xiong Jinxin,Ren Guojing. Separation of Single Wood Branches and Leaves Based on Corrected TLS Intensity Data[J]. Chinese Journal of Lasers, 2021, 48(1): 0104001

孙圆,林秀云,熊金鑫,任国婧. 基于地面激光强度校正数据的单木枝叶分离[J]. 中国激光, 2021, 48(1): 0104001

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