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

doi:10.11947/j.JGGS.2024.0402

Abstract

Abstract:

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.

Key words: object-oriented; high-resolution image; image segmentation; CART decision tree; building extraction

HUANG Wei, CUI Zhimei, HUANG Zhidu, WU Rongrong. Research on Building Extraction Based on Object-oriented CART Classification Algorithm and GF-2 Satellite Images[J]. Journal of Geodesy and Geoinformation Science, 2024, 7(4): 5-18.

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