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Proceedings Paper

Vegetation information extraction in urban area based on high resolution remote sensing images
Author(s): Yu Wang; Xiaoyong Wang; Hongyan He; Guoliang Tian
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Paper Abstract

The spatial information of high-resolution remote sensing images is more abundant, and the expression of ground object information in detail is clearer. Vegetation is a component of the environment and the most important component of terrestrial ecosystems. Therefore, vegetation information extraction from remote sensing images is particularly significant. This paper takes Shanghai Pudong New Area as the research area, adopts threshold classification method and membership function classification method to extract vegetation information, and introduces normalized vegetation index as feature to extract vegetation information from WorldView-3 satellite remote sensing image. The results show that the accuracy of vegetation information extraction based on membership function classification method is higher. The classification accuracy of typical vegetation area is higher than 90%, and the Kappa coefficient is higher than 0.86, which can significantly reduce the fragmentation caused by classification. At the same time, high-resolution remote sensing images show great potential for the extraction of vegetation information in urban areas.

Paper Details

Date Published: 18 December 2019
PDF: 7 pages
Proc. SPIE 11341, AOPC 2019: Space Optics, Telescopes, and Instrumentation, 113411Q (18 December 2019); doi: 10.1117/12.2547659
Show Author Affiliations
Yu Wang, Beijing Institute of Space Mechanics and Electricity (China)
Key Lab. for Advanced Optical Remote Sensing Technology of Beijing (China)
Xiaoyong Wang, Beijing Institute of Space Mechanics and Electricity (China)
Key Lab. for Advanced Optical Remote Sensing Technology of Beijing (China)
Hongyan He, Beijing Institute of Space Mechanics and Electricity (China)
Key Lab. for Advanced Optical Remote Sensing Technology of Beijing (China)
Guoliang Tian, Beijing Institute of Space Mechanics and Electricity (China)
Key Lab. for Advanced Optical Remote Sensing Technology of Beijing (China)


Published in SPIE Proceedings Vol. 11341:
AOPC 2019: Space Optics, Telescopes, and Instrumentation
Suijian Xue; Xuejun Zhang; Carl Anthony Nardell; Ziyang Zhang, Editor(s)

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