Research on Building Extraction Based on Object-oriented CART Classification Algorithm and GF-2 Satellite Images
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...
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| Format: | Article |
| Language: | English |
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Surveying and Mapping Press
2024-12-01
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| Series: | Journal of Geodesy and Geoinformation Science |
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| Online Access: | http://jggs.chinasmp.com/fileup/2096-5990/PDF/1737100151274-910433691.pdf |
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| author | HUANG Wei, CUI Zhimei, HUANMG Zhidu, WU Rongrong |
| author_facet | HUANG Wei, CUI Zhimei, HUANMG Zhidu, WU Rongrong |
| author_sort | HUANG Wei, CUI Zhimei, HUANMG Zhidu, WU Rongrong |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-9eac55539adc4f6480ae92204ce880a6 |
| institution | Kabale University |
| issn | 2096-5990 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Surveying and Mapping Press |
| record_format | Article |
| series | Journal of Geodesy and Geoinformation Science |
| spelling | doaj-art-9eac55539adc4f6480ae92204ce880a62025-01-17T07:56:09ZengSurveying and Mapping PressJournal of Geodesy and Geoinformation Science2096-59902024-12-017451810.11947/j.JGGS.2024.0402Research on Building Extraction Based on Object-oriented CART Classification Algorithm and GF-2 Satellite ImagesHUANG Wei, CUI Zhimei, HUANMG Zhidu, WU Rongrong0Electric Power Research Institute of Guangxi Power Grid Co., Ltd., Nanning 530000, ChinaAs 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.http://jggs.chinasmp.com/fileup/2096-5990/PDF/1737100151274-910433691.pdf|object-oriented|high-resolution image|image segmentation|cart decision tree|building extraction |
| spellingShingle | HUANG Wei, CUI Zhimei, HUANMG Zhidu, WU Rongrong Research on Building Extraction Based on Object-oriented CART Classification Algorithm and GF-2 Satellite Images Journal of Geodesy and Geoinformation Science |object-oriented|high-resolution image|image segmentation|cart decision tree|building extraction |
| title | Research on Building Extraction Based on Object-oriented CART Classification Algorithm and GF-2 Satellite Images |
| title_full | Research on Building Extraction Based on Object-oriented CART Classification Algorithm and GF-2 Satellite Images |
| title_fullStr | Research on Building Extraction Based on Object-oriented CART Classification Algorithm and GF-2 Satellite Images |
| title_full_unstemmed | Research on Building Extraction Based on Object-oriented CART Classification Algorithm and GF-2 Satellite Images |
| title_short | Research on Building Extraction Based on Object-oriented CART Classification Algorithm and GF-2 Satellite Images |
| title_sort | research on building extraction based on object oriented cart classification algorithm and gf 2 satellite images |
| topic | |object-oriented|high-resolution image|image segmentation|cart decision tree|building extraction |
| url | http://jggs.chinasmp.com/fileup/2096-5990/PDF/1737100151274-910433691.pdf |
| work_keys_str_mv | AT huangweicuizhimeihuanmgzhiduwurongrong researchonbuildingextractionbasedonobjectorientedcartclassificationalgorithmandgf2satelliteimages |