Land use classification of farming areas based on time series Sentinel-2A/B data and random forest algorithm
WANG Dejun1(), JIANG Qigang2, LI Yuanhua2, GUAN Haitao1, ZHAO Pengfei1, XI Jing2
1. The Fifth Surveying Mapping and Geographic Information Engineering Institute of Heilongjiang Province, Harbin 150081, China 2. College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China
Land cover information in farming areas is the basis of land resource management and planning, which plays an important role in the rational development of land resources, adjustment of land use structure, and dynamic monitoring of land. Due to the complex land types and high heterogeneity in farming areas, the accuracy of land cover information extraction has been facing challenges. Therefore, this study used Sentinel-2A/B remote sensing data as the data source. Firstly, a normalized difference vegetation index (NDVI) time series data set and tasseled cap wetness (TCW) time series data set were constructed; Secondly, the J-M (Jeffries-Matusita) distance was used to analyze the separability of the surface features and select the best time series data combination of NDVI and TCW; Finally, combined with random forest (RF), support vector machine (SVM), maximum likelihood classification (MLC) and single phase remote sensing data, the classification of typical features in farming areas was studied, and the accuracy of classification results was evaluated and compared. The research results show that the classification accuracy of the time series data combined with the random forest classification algorithm is relatively high. The overall classification accuracy reaches 88.87%, and the Kappa coefficient reaches 0.855 7, which improves the classification accuracy by 10.05 percentage points and 0.209 3 respectively compared with that of the single remote sensing data. This fully demonstrates that the combination of time series data and random forest classification algorithm can effectively improve the classification accuracy of typical features in farming areas.
王德军, 姜琦刚, 李远华, 关海涛, 赵鹏飞, 习靖. 基于Sentinel-2A/B时序数据与随机森林算法的农耕区土地利用分类[J]. 国土资源遥感, 2020, 32(4): 236-243.
WANG Dejun, JIANG Qigang, LI Yuanhua, GUAN Haitao, ZHAO Pengfei, XI Jing. Land use classification of farming areas based on time series Sentinel-2A/B data and random forest algorithm. Remote Sensing for Land & Resources, 2020, 32(4): 236-243.
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