A random forest classifier based on pixel comparison features for urban LiDAR data
Authors:
- Chisheng WangCWChisheng Wang
- Qiqi ShuQSQiqi Shu
- Xinyu WangXWXinyu Wang
- Bo GuoBGBo Guo
- Peng LiuPLPeng Liu
- Qingquan LiQLQingquan Li
- 1 February 2019
- journal article
- research article
- Published by Elsevier in ISPRS Journal of Photogrammetry and Remote Sensing
- Vol. 148, 75-86
Abstract
No abstract availableKeywords
Funding Information
- National Natural Science Foundation of China (41504004, 41504003)
- Natural Science Funding of Shenzhen University (2018073)
- Shenzhen Scientific Research and Development Funding Program (JCYJ20170302144002028)
- Open Fund of the State Key Laboratory of Earthquake Dynamics (LED2016B03)
This publication has 31 references indexed in Scilit:
- A review of regional science applications of satellite remote sensing in urban settingsComputers, Environment and Urban Systems, 2013
- Improving classification accuracy of airborne LiDAR intensity data by geometric calibration and radiometric correctionISPRS Journal of Photogrammetry and Remote Sensing, 2012
- Fusion of High Resolution Aerial Multispectral and LiDAR Data: Land Cover in the Context of Urban Mosquito HabitatRemote Sensing, 2011
- Support vector machines in remote sensing: A reviewISPRS Journal of Photogrammetry and Remote Sensing, 2011
- Relevance of airborne lidar and multispectral image data for urban scene classification using Random ForestsISPRS Journal of Photogrammetry and Remote Sensing, 2011
- Backscatter coefficient as an attribute for the classification of full-waveform airborne laser scanning data in urban areasISPRS Journal of Photogrammetry and Remote Sensing, 2010
- Conditional variable importance for random forestsBMC Bioinformatics, 2008
- Object-Based Land Cover Classification Using High-Posting-Density LiDAR DataGIScience & Remote Sensing, 2008
- Monitoring the composition of urban environments based on the vegetation-impervious surface-soil (VIS) model by subpixel analysis techniquesInternational Journal of Remote Sensing, 2002
- Integrating Contextual Information with per-Pixel Classification for Improved Land Cover ClassificationRemote Sensing of Environment, 2000