Abstract
In this paper, we develop a classification method of land cover based on support vector machines. As a case study, we choose five Landsat images to retrieve land cover maps in Shenzhen, China from 1979 to 2005. The classification method is based on support vector machines with assistance from visual interpretation. And then we take use of the complex network approach to analyze the character of land use-cover change from an overall perspective. The result shows that the main changes of land use-cover are different over time. The medium of bare land during the urban construction can hardly be witnessed, even though the time intervals are shorter than the two periods before. It reveals the transformation from vegetation to urban becomes faster. The transformation from vegetation to bare land is hard to be witnessed in the late stage. As bare land is the medium for transforming vegetation to urban land in Shenzhen during the past years from 1979 to 2005.
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Acknowledgments
This work was supported in part by the Basic and Applied Basic Research Funding Program of Guangdong Province of China (Grant No. 2019A1515110303), the Natural Science Foundation of Guangdong Province (Grant No. 2018A030313014), the research team project of Dongguan University of Technology (Grant No. TDY-B2019009), the Guangdong University Key Project (2019KZDXM012).
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Ding, K., Wang, C., Tao, M., Xiao, H., Yang, C., Huang, P. (2020). A Classification Method of Land Cover Based on Support Vector Machines. In: Chen, X., Yan, H., Yan, Q., Zhang, X. (eds) Machine Learning for Cyber Security. ML4CS 2020. Lecture Notes in Computer Science(), vol 12488. Springer, Cham. https://doi.org/10.1007/978-3-030-62463-7_5
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