Point Clouds Classification Algorithm of Vegetation Based on Area and Pointing Features

Zhouyang Hua; Sheng Xu; Ying’an Liu

doi:10.3788/LOP202259.1828001

[1] Yang Q L, Su Y J, Jin S C et al. The influence of vegetation characteristics on individual tree segmentation methods with airborne LiDAR data[J]. Remote Sensing, 11, 2880(2019).

[2] Hassanat A B A, Alkasassbeh M, Al-awadi M et al. Color-based object segmentation method using artificial neural network[J]. Simulation Modelling Practice and Theory, 64, 3-17(2016).

[3] Chen Y Y, Hou C J, Tang Y et al. Citrus tree segmentation from UAV images based on monocular machine vision in a natural orchard environment[J]. Sensors, 19, 5558(2019).

[4] Zhao Liang, Hu Jie, Liu Han, An Yongpeng, Xiong Zongquan, Wang Yu. Deep Learning Based on Semantic Segmentation for Three-Dimensional Object Detection from Point Clouds[J]. Chinese Journal of Lasers, 48, 1710004(2021).

[5] Xue D D, Cheng Y L, Shi X S et al. Point clouds classification algorithm based on cloth filtering algorithm and improved random forest[J]. Laser & Optoelectronics Progress, 57, 221017(2020).

[6] Wang G, Wang Q, Zhang Z X et al. Classification of airborne LiDAR vegetation point clouds assisted by aerial images[J]. Laser & Optoelectronics Progress, 57, 201005(2020).

[7] Lu D D, Zou J G. Multi-classifier combined method for LiDAR point cloud classification[J/OL]. Surveying and Mapping Geographic Information, 1-7. https://doi.org/10.14188/j.2095-6045.2020445

[8] Wang X Y, Xing Y Q, You H T et al. TLS point cloud classification of forest based on nearby geometric features[J]. Journal of Beijing Forestry University, 41, 138-146(2019).

[9] Wang D, Hollaus M, Pfeifer N. Feasibility of machine learning methods for separating wood and leaf points from terrestrial laser scanning data[J]. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, IV-2/W4, 157-164(2017).

[10] Tian Y, Luo S, Li B B et al. Optimal energy features of partial discharge signals in GIS extracted by fisher linear discriminant[J]. Electric Power, 52, 93-101(2019).

[11] Guo Y F, Shu T T, Yang J Y et al. Feature extraction method based on the generalised fisher discriminant criterion and facial recognition[J]. Pattern Analysis & Applications, 4, 61-66(2001).

[12] Lin X G, Zhang J X. Segmentation-based filtering of airborne LiDAR point clouds by progressive densification of terrain segments[J]. Remote Sensing, 6, 1294-1326(2014).

[13] Li C R. Filtering algorithm for LiDAR point clouds in urban areas based on improved iterative rough classification[J]. Modern Surveying and Mapping, 43, 53-56(2020).

[14] Zhang G X, Tang W, Wan T R et al. Design of anti-noise point cloud recognition network based on deep learning[J]. Basic Sciences Journal of Textile Universities, 33, 113-120(2020).

[15] He T, Xiong F G, Han X et al. A feature curve extraction algorithm for point cloud based on covariance matrix[J]. Computer Engineering, 44, 275-280, 286(2018).

[16] Hu H Y, Hui Z Y, Li N. Airborne LiDAR point cloud classification based on multiple-entity eigenvector fusion[J]. Chinese Journal of Lasers, 47, 0810002(2020).

[17] Zhang J L, Xu D, Hao K J et al. FS-GBDT: identification multicancer-risk module via a feature selection algorithm by integrating Fisher score and GBDT[J]. Briefings in Bioinformatics, 22, bbaa189(2020).

[18] Zhu W H, Yao J. Point cloud classification method based on point cloud convolution research[J/OL]. Surveying and mapping geographic information, 1-7. https://doi.org/10.14188/j.2095-6045.2020332