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Remote Sensing for Land & Resources    2019, Vol. 31 Issue (2) : 125-130     DOI: 10.6046/gtzyyg.2019.02.18
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A study of remote sensing monitoring methods for the high standard farmland
Zhen CHEN1, Yunshi ZHANG1, Yuanyu ZHANG2, Lingling SANG2
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
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Abstract  

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.

Keywords farmland use remote sensing monitoring      object-oriented classification      maximum likelihood classification     
:  TP79  
Issue Date: 23 May 2019
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Zhen CHEN
Yunshi ZHANG
Yuanyu ZHANG
Lingling SANG
Cite this article:   
Zhen CHEN,Yunshi ZHANG,Yuanyu ZHANG, et al. A study of remote sensing monitoring methods for the high standard farmland[J]. Remote Sensing for Land & Resources, 2019, 31(2): 125-130.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2019.02.18     OR     https://www.gtzyyg.com/EN/Y2019/V31/I2/125
参数 全色/多光谱相机参数值
光谱范围/μm 全色 0.450.90
多光谱 0.450.52
0.520.59
0.630.69
0.770.89
空间分辨率/m 全色 1
多光谱 4
幅宽/km 45(2台相机组合)
重访周期(侧摆时)/d 5
覆盖周期(不侧摆)/d 69
Tab.1  Indexes of GF-2 satellite
波段 B1 B2 B3 B4
B1 1.00 0.95 0.90 0.34
B2 0.95 1.00 0.97 0.43
B3 0.90 0.96 1.00 0.41
B4 0.34 0.43 0.41 1.00
Tab.2  Correlation coefficient matrix between each multispectral band of GF-2
R,G,B B1,B2,B4 B1,B3,B4 B2,B3,B4
OIF 290.52 366.17 352.60
Tab.3  Band combination of GF-2 data and OIF value
Fig.1  Work flow chart
Tab.4  Remote sensing interpretation sign of the high standard farmland utilization monitoring after construction
Fig.2  Schematic diagram of KNN classification
Fig.3  Classification of object-oriented method
Fig.4  Classification of maximum likelihood method
类别 农田 水体 公路 建设
占用
调整
用途
荒地 合计
农田 522 0 0 0 0 0 522
水体 0 412 0 0 13 0 425
公路 0 0 840 0 0 19 859
建设占用 0 0 0 609 0 0 609
调整用途 0 0 0 0 180 0 180
荒地 0 0 4 0 0 138 142
合计 522 412 844 609 193 157 2 737
Tab.5  Confusion matrix of the object-oriented method
类别 农田 水体 公路 建设
占用
调整
用途
荒地 合计
农田 521 0 0 1 0 0 522
水体 0 0 0 0 165 0 165
公路 0 411 843 0 0 0 1 254
建设占用 0 0 0 607 3 0 610
调整用途 0 0 0 0 25 5 30
荒地 0 0 0 0 0 151 151
合计 521 411 843 608 193 156 2 732
Tab.6  Confusion matrix of the maximum likelihood method
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