Point Cloud Recognition of Street Tree Target Based on Variable-scale Grid Index and Machine Learning
Abstract
Street tree continuous spray methods cause serious environmental pollution, however, the existing target spray technologies for tree are difficult to extend to complex urban environment. Aiming at the above problems, recognition method of street tree target was studied, which obtained the information of crown position and distance in real time and provided an accurate spraying basis for street tree toward-target spraying. The research results would improve the intelligent level of medical equipment for prevention and control of street tree and provide theoretical and technical support for street tree pest control, which had low injection, fine spraying, less pollution and high efficiency. Vehicle-borne 2D LiDAR was used to capture 3D point cloud data of street, and variable-scale grid index of point cloud was constructed to process point cloud data online and search neighborhood fast. Height, depth, density and covariance matrix features were extracted from spherical neighborhood of point cloud data, and an 11-dimensional feature vector was constructed. Distribution characteristics of features were analyzed and support vector machine algorithm based on the radial basis kernel function was used to fuse features and learn a point cloud classifier of crown. FIFO buffer was used to save point cloud frame sequences, and then street tree target can be recognized on-line. The classification error rate on the test set was less than 0.8%, with a detection rate more than 99.4% and a false alarm rate less than 0.9%. Four most discriminative features were selected, which were height mean, depth mean, height range and height variance.
Keywords: target spraying, street tree recognition, 2D LiDAR, point cloud index, point cloud classification
Download Full Text:
PDFReferences
JANG II S, LEE S C, JEON J Y, et al. Evaluation of road traffic noise abatement by vegetation treatment in a 1-10 urban scale model[J]. Journal of the Acoustical Society of America, 2015, 138(6) : 3884 -3895.
GROMKE C, RUCK B. Influence of trees on the dispersion of pollutants in an urban street canyon-experimental investigation of the flow and concentration field [ J ] - Atmospheric Environment, 2007, 41 ( 16) ; 3287 -3302.
GILLNER S, VOGT J, THARANG A, et al. Role of street trees in mitigating effects of heat and drought at highly sealed urban sites[J]. Landscape & Urban Planning, 2015, 143; 33 -42.
BERLAND A, HOPTON M E. Comparing street tree assemblages and associated stormwater benefits among communities in metropolitan Cincinnati, Ohio, USA [J]. Urban Forestry & Urban Greening, 2014, 13(4) ; 734 -741.
PANG Kaihui. Analysis on major diseases and pests for the trees of green belt [J]. Protection Forest Science and Technology, 2016 ( 8 ) ; 34 - 35.( in Chinese)
http://www.j-csam.org/jcsam/ch/reader/view_abstract. aspx? flle_no = 20150309&flag = l&journal_id = jcsam. DOI: 10. 6041/j. issn. 1000- 1298.2015.03.009. QIU Baijing, YAN Run, MA Jing, et al. Research progress analysis of variable rate sprayer technology[ J/OL]. Transactions of the Chinese Society for Agricultural Machinery, 2015, 46(3) ; 59 -72. ( in Chinese)
LI Hanzhe, ZHAI Changyuan, ZHANG Bo, et al. Status analysis of orchard spray target detection technology [ J ] . Journal of Agricultural Mechanization Research, 2016, 38(2); 1 -5. (in Chinese)
CHEN Y, OZKAN H E, ZHU H, et al. Spray deposition inside tree canopies from a newly developed variable-rate air-assisted Sprayer [J]. Transactions of the ASA BE, 2013, 56(6) : 1263 - 1272.
LIU II, ZHU II, SHEN Y, et al. Development of digital flow control system for multi-channel variable-rate sprayers [ J ]. Transactions of the ASABE, 2014, 57( 1 ) : 273 - 281.
LI Zengyuan, LIU Qingwang, PANG Yong. Review on forest parameters inversion using LiDAR J]. Journal of Remote Sensing, 2016 , 20(5 ) : 1138 - 1150. (in Chinese)
YANG Bisheng, WEI Zheng, LI Qingquan, et al. A classification-oriented method of feature image generation for vehicle-borne laser scanning point clouds[J]. Acta Geodaetica et Cartographica Sinica, 2010, 39(5) ; 540 -545. (in Chinese)
YANG Shasha, LI Yongqiang, LI Kuangyu, et al. Tree extraction from vehicle-borne LiDAR data[J]. Engineering of Surveying and Mapping, 2014 , 23( 8 ) ; 23 - 27. (in Chinese)
ZHONG R , WEI J, SU W, et al. A method for extracting trees from vehicle-borne laser scanning data [ J ]. Mathematical & Computer Modelling, 2013 , 58 ( 3 - 4) : 727 -736.
WU B, YU B, YUE W. A voxel-based method for automated identification and morphological parameters estimation of individual street trees from mobile laser scanning data [J]. Remote Sensing, 2013, 5(2) : 584 -611.
ZHANG lleng, XU Junyi, LIU Rufei, et al. An extraction method of trees in vehicle-borne laser point cloud based on the improved region growing method[J]. Journal of Geomatics Science and Technology, 2015, 32(2) : 178 - 182. (in Chinese)
YUE G, LIU R, ZHANG II, et al. A method for extracting street trees from mobile LiDAR point clouds [J ] . Open Cybernetics & Systemics Journal, 2015, 9( 1 ) ; 204 -209.
VAUGHN N R, MOSKAL L M, TURNBLOM E C. Tree species detection accuracies using discrete point lidar and airborne waveform LiDAR [J]. Remote Sensing, 2012,4(2) : 377 -403.
DONG Zhen, YANG Bisheng. Hierarchical extraction of multiple objects from mobile laser scanning data[J]. Acta Geodaetica et Cartographica Sinica, 2015, 44(9) : 980 -987. (in Chinese)
YOKOYAMA H, DATE II, KANAI S, et al. Detection and classification of pole-like objects from mobile laser scanning data of urban environments [ J ]. International Journal of CAD/CAM, 2013, 13(1): 1 - 10.
LIANG X L, LITKEY P, HYYPPA J, et al. Automatic stem mapping using single-scan terrestrial laser scanning [J ]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(2) ; 661 -670.
LI Haiting, XIAO Jianhua, LI Yanhong, et al. Application of machine learning in the vehicle-borne laser point cloud extraction [ J]. Journal of Central China Normal University: Natural Science, 2015, 49(3) : 460 -464. ( in Chinese)
GUO Bo, HUANG Xianfeng, ZHANG Fan, et al. Points cloud classification using JointBoost combined with contextual information for feature reduction [ J ]. Acta Geodaetica et Cartographica Sinica, 2013, 42(5) : 715 -721. (in Chinese)
DEMANTKE J, MALLET C, DAVID N, et al. Dimensionality based scale selection in 3D LiDAR point clouds [ J ]. ISPRS- International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2012, 3812(5) :97 - 102.
MARTIN R, BERNHARD II, MARKUS II, et al. Object-based point cloud analysis of full-waveform airborne laser scanning data for urban vegetation classification[ J]. Sensors, 2008, 8(8) ;4505 -4528.
VAPNIK V. The nature of statistical learning theory[M]. New York: Springer-Verlag, 1995.
Refbacks
- There are currently no refbacks.