Evaluating the performance of pixel-based and object-based multidimensional clustering algorithms for automated surface water mappingOA
Evaluating the performance of pixel-based and object-based multidimensional clustering algorithms for automated surface water mapping
Bohao Li;Kai Liu;Ming Wang;Yanfang Wang;Linmei Zhuang;Weihua Zhu;Chenxia Li;Linhao Zhang;Yanan Chen
Joint International Research Laboratory of Catastrophe Simulation and Systemic Risk Governance,Beijing Normal University,Zhuhai,China||School of National Safety and Emergency Management,Beijing Normal University,Beijing,China||Faculty of Geographical Science,Beijing Normal University,Beijing,ChinaJoint International Research Laboratory of Catastrophe Simulation and Systemic Risk Governance,Beijing Normal University,Zhuhai,China||School of National Safety and Emergency Management,Beijing Normal University,Beijing,China||Collaborative Innovation Centre on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD),Nanjing University of Information Science&Technology,Nanjing,ChinaJoint International Research Laboratory of Catastrophe Simulation and Systemic Risk Governance,Beijing Normal University,Zhuhai,China||School of National Safety and Emergency Management,Beijing Normal University,Beijing,ChinaHebei International Joint Research Centre for Remote Sensing of Agricultural Drought Monitoring,Hebei GEO University,Shijiazhuang,ChinaJoint International Research Laboratory of Catastrophe Simulation and Systemic Risk Governance,Beijing Normal University,Zhuhai,China||School of National Safety and Emergency Management,Beijing Normal University,Beijing,China||Faculty of Geographical Science,Beijing Normal University,Beijing,ChinaTransport Planning and Research Institute,Ministry of Transport,Beijing,ChinaCollege of Resources,Environment and Tourism,Capital Normal University,Beijing,ChinaFaculty of Geographical Science,Beijing Normal University,Beijing,ChinaHebei Remote Sensing Centre,Hebei Hydrological Engineering Geological Survey Institute,Shijiazhuang,China
Water mappingSentinel-2unsupervised classificationmachine learningrandom forestobject-based classification
Water mappingSentinel-2unsupervised classificationmachine learningrandom forestobject-based classification
《地球空间信息科学学报（英文版）》 2026 (2)
中插6,878-898,22
This work is supported by the General Program of the National Natural Science Foundation of China[Grant num-ber 42377467],the National Natural Science Foundation of China[Grant number 42201034],and the Fundamental Research Funds for the Central Universities[Grant number 2243300007].
评论