Research on Building Extraction Based on Object-oriented CART Classification Algorithm and GF-2 Satellite Images  

作　　者：HUANG Wei CUI Zhimei HUANG Zhidu WU Rongrong 

机构地区：[1]Electric Power Research Institute of Guangxi Power Grid Co.,Ltd.,Nanning 530000,China

出　　处：《Journal of Geodesy and Geoinformation Science》2024年第4期5-18,共14页测绘学报(英文版)

基　　金：Research on Algorithm Model for Monitoring and Evaluating Typical Disaster Situations of Electric Power Equipment Based on Remote Sensing Imaging Technology of Heaven and Earth,South Grid Guangxi Power Grid Company Science and Technology Project(GXKJXM20222160).

摘　　要：As one of the main geographical elements in urban areas,buildings are closely related to the development of the city.Therefore,how to quickly and accurately extract building information from remote sensing images is of great significance for urban map updating,urban planning and construction,etc.Extracting building information around power facilities,especially obtaining this information from high-resolution images,has become one of the current hot topics in remote sensing technology research.This study made full use of the characteristics of GF-2 satellite remote sensing images,adopted an object-oriented classification method,combined with multi-scale segmentation technology and CART classification algorithm,and successfully extracted the buildings in the study area.The research results showed that the overall classification accuracy reached 89.5%and the Kappa coefficient was 0.86.Using the object-oriented CART classification algorithm for building extraction could be closer to actual ground objects and had higher accuracy.The extraction of buildings in the city contributed to urban development planning and provided decision support for management.

关 键 词：OBJECT-ORIENTED high-resolution image image segmentation CART decision tree building extraction 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]