1.School of Earth Sciences and Resources, China University of Geosciences(Beijing), Beijing 100083, China 2.Land Consolidation and Rehabilitation Center, Ministry of Natural Resources, Beijing 100035, China
At present, the area of high standard farmland has reached a certain scale in China. In the remote sensing monitoring for the utilization of high standard farmland, illegal utilization has appeared frequently. How to realize real-time and accurate remote sensing monitoring for high standard farmland has become an urgent problem for the land regulation department of the government. The national high standard farmland monitoring area is large, and the monitoring precision requirements are high. It is urgent for the government to study a set of high standard farmland automatic monitoring methods adapted to the nationwide extension. In this paper, two automatic remote sensing classification monitoring methods, i.e., object oriented and maximum likelihood, are compared. The overall precision of the object-oriented method is 98.684 7%, and the Kappa coefficient is 0.983 3. The overall accuracy of the maximum likelihood classification method is 78.587 1%, and the Kappa coefficient is 0.718 0. The research shows that the object-oriented classification method can better meet the requirements of the high standard farmland. By popularizing the method, it is the way to provide efficient and accurate decision-making information for real time supervision of high standard farmland, and can provide technical support for the national protection of cultivated land and food security.
陈震,张耘实,章远钰,桑玲玲. 高标准农田建后遥感监测方法[J]. 国土资源遥感, 2019, 31(2): 125-130.
Zhen CHEN,Yunshi ZHANG,Yuanyu ZHANG,Lingling SANG. A study of remote sensing monitoring methods for the high standard farmland. Remote Sensing for Land & Resources, 2019, 31(2): 125-130.
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