Abstract
The urbanization process has changed urban land, which has affected the environmental quality of urban residents. It is very important to obtain urban land cover information. In this paper, Yangshuo, a small country of Guilin City, is used as the research area, and the object-oriented spatial feature extraction module (Feature Extraction, hereinafter referred to as FX) is used to carry out experiments and accuracy evaluation of land cover classification in the research area. Extracting land cover information from the GF-2 remote sensing image, establishing a classification system sample based on the characteristic information of six land cover classification objects such as urban land, waterbody, woodland, farmland, road and other lands, and finally execute Supervising the classification and verify its accuracy. The results show that this method can recognize the land cover accurately and the total accuracy verified is as high as 97.41%.
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Change history
01 May 2021
The original version of this chapter was revised: The name of Yuanfa Li has been corrected to Yuanfa JI.
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Acknowledgment
The authors thanks national key R & D program funding (2018YFB0505103), Major Special Plan of Guilin Science and Technology Plan Project in 2019 (20190219-1), Innovation Project of GUET Graduate Education(2020YCXS026, 2020YCXS028) and Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing 2019 Director Fund Project (GXKL06190111) for providing the necessary support and funds. The authors also thank the satellite navigation team for all the validation data provided during the experiment.
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Chen, X., Li, J., Ji, Y. (2021). Land Cover Classification and Accuracy Evaluation Based on Object-Oriented Spatial Features of GF-2. In: Gao, H., Fan, P., Wun, J., Xiaoping, X., Yu, J., Wang, Y. (eds) Communications and Networking. ChinaCom 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 352. Springer, Cham. https://doi.org/10.1007/978-3-030-67720-6_21
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DOI: https://doi.org/10.1007/978-3-030-67720-6_21
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