Please wait a minute...
 
REMOTE SENSING FOR LAND & RESOURCES    2008, Vol. 20 Issue (4) : 6-8     DOI: 10.6046/gtzyyg.2008.04.02
Technology and Methodology |
LIDAR POINT CLOUD DATA FILTERING BASED ON REGIONAL GROWING
CHENG Xiao-qian1, ZHAO Hong-qiang2
1. State Key Laboratory of Information Engineering for Surveying Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China; 2. School of Geodesy and Geomatics, Wuhan University, Wuhan 430079; China
Download: PDF(546 KB)  
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  

A new filtering algorithm named regional growing filer is proposed in this paper. The principle of the regional growing filer is similar to the regional growing used to process digital images. First, reliable seeds are selected, then the growing rule is formulated. If the height difference between the seeds and the selected points is lower than the threshold, the selected point is regarded as the ground point, otherwise the point is removed as a feature point. When there are no laser points that can meet the rule, the growing ends. The processed point clouds use regional growing filter with no need of original data interpolation and iteration, and hence the filtering speed is fast. Experiments show that the effects of the regional growing filer is better than the results of such traditional algorithms as the maximum local slope filer and the expansion of window height threshold filer.

Keywords Thermal infrared      Potash anomaly     
Issue Date: 23 June 2009
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
Cite this article:   
CHENG Xiao-Qian, ZHAO Hong-Qiang. LIDAR POINT CLOUD DATA FILTERING BASED ON REGIONAL GROWING[J]. REMOTE SENSING FOR LAND & RESOURCES,2008, 20(4): 6-8.
URL:  
https://www.gtzyyg.com/EN/10.6046/gtzyyg.2008.04.02     OR     https://www.gtzyyg.com/EN/Y2008/V20/I4/6
[1] LI Teya, SONG Yan, YU Xinli, ZHOU Yuanxiu. Monthly production estimation model for steel companies based on inversion of satellite thermal infrared temperature[J]. Remote Sensing for Natural Resources, 2021, 33(4): 121-129.
[2] SHU Huiqin, FANG Junyong, LU Peng, GU Wanfa, WANG Xiao, ZHANG Xiaohong, LIU Xue, DING Lanpo. Research on fine recognition of site spatial archaeology based on multisource high-resolution data[J]. Remote Sensing for Land & Resources, 2021, 33(2): 162-171.
[3] Jing LI, Qiangqiang SUN, Ping ZHANG, Danfeng SUN, Li WEN, Xianwen LI. A study of auxiliary monitoring in iron and steel plant based on multi-temporal thermal infrared remote sensing[J]. Remote Sensing for Land & Resources, 2019, 31(1): 220-228.
[4] Jian YU, Yunjun YAO, Shaohua ZHAO, Kun JIA, Xiaotong ZHANG, Xiang ZHAO, Liang SUN. Estimating latent heat flux over farmland from Landsat images using the improved METRIC model[J]. Remote Sensing for Land & Resources, 2018, 30(3): 83-88.
[5] Hanyue CHEN, Li ZHU, Jiaguo LI, Xieyu FAN. A comparison of two mono-window algorithms for retrieving sea surface temperature from Landsat8 data in coastal water of Hongyan River nuclear power station[J]. Remote Sensing for Land & Resources, 2018, 30(1): 45-53.
[6] LI Feng, LIANG Handong, ZHAO Xiaoping, BAI Jiangwei, CUI Yukun. Remote sensing monitoring and assessment of fire-fighting effects in Wuda coal field,Inner Mongolia[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(3): 217-223.
[7] GUAN Zhen, WU Hong, CAO Cui, HUANG Xiaojuan, GUO Lin, LIU Yan, HAO Min. Uranium ore prediction based on inversion of ETM+6-γ mineral information in Huashan granite area[J]. REMOTE SENSING FOR LAND & RESOURCES, 2014, 26(3): 92-98.
[8] WEN Shaoyan, QU Chunyan, SHAN Xinjian, YAN Lili, SONG Dongmei. Satellite thermal infrared background field variation characteristics of the Qilian Mountains and the Capital Zone[J]. REMOTE SENSING FOR LAND & RESOURCES, 2013, 25(3): 138-144.
[9] MENG Peng, HU Yong, GONG Cai-lan, LI Lin. Discussions on Using Channels of Split-window Algorithm to Retrieve Earth Surface Temperature[J]. REMOTE SENSING FOR LAND & RESOURCES, 2012, 24(4): 16-20.
[10] ZHANG Xue-hong, TIAN Qing-jiu. Application of the Temperature-Moisture Index to the Improvement of Remote Sensing Identification Accuracy of Mangrove[J]. REMOTE SENSING FOR LAND & RESOURCES, 2012, 24(3): 65-70.
[11] MA Hong-Zhang, LIU Qin-Huo, WEN Jian-Guang, SHI Jian. The Numerical Simulation and Difference Analysis of Soil Temperature on Thermal Infrared and L Bands[J]. REMOTE SENSING FOR LAND & RESOURCES, 2011, 23(2): 26-32.
[12] XU Yong-Meng, QIN Zhi-Hao, WAN Hong-Xiu.
Advances in the Study of Near Surface Air Temperature Retrieval from Thermal Infrared Remote Sensing
[J]. REMOTE SENSING FOR LAND & RESOURCES, 2011, 23(1): 9-14.
[13] HE Jia-Hui, LIANG Chun-Li, LI Ming-Song.   Temperature Field Airborne Thermal Remote Sensing Survey of the Alongshore Nuclear Power Station[J]. REMOTE SENSING FOR LAND & RESOURCES, 2010, 22(3): 51-53.
[14] YU Yan-Mei, GAN Fu-Ping, ZHOU Ping, YAN Bo-Kun. A PRELIMINARY STUDY AND APPLICATION OF THE MARTIAN MINERAL MAPPING METHODS BASED
ON THERMAL INFRARED REMOTE SENSING DATA
[J]. REMOTE SENSING FOR LAND & RESOURCES, 2009, 21(4): 35-39.
[15] CHEN Feng, HE Bao-Yin, LONG Zhan-Yong, YANG Xiao-Qin. A SPATIAL ANALYSIS OF URBAN HEAT ISLAND AND UNDERLYING SURFACE USING LANDSAT ETM+[J]. REMOTE SENSING FOR LAND & RESOURCES, 2008, 20(2): 56-61.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
京ICP备05055290号-2
Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech