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  

Land use classification of farming areas based on time series Sentinel-2A/B data and random forest algorithm

作　　者：王德军 姜琦刚[2] 李远华[2] 关海涛 赵鹏飞 习靖 WANG Dejun;JIANG Qigang;LI Yuanhua;GUAN Haitao;ZHAO Pengfei;XI Jing(The Fifth Surveying Mapping and Geographic Information Engineering Institute of Heilongjiang Province, Harbin 150081, China;College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China)

机构地区：[1]黑龙江省第五测绘地理信息工程院,哈尔滨150081 [2]吉林大学地球探测科学与技术学院,长春130026

出　　处：《国土资源遥感》2020年第4期236-243,共8页Remote Sensing for Land & Resources

基　　金：中国地质调查局项目“辽吉黑区自然资源更新调查”(编号:3S2170124423);中国地质调查局资金资助项目(编号:GFZX0404130302)共同资助。

摘　　要：农耕区土地覆被信息是土地资源管理与规划的基础,在合理开发土地资源,调整土地利用结构以及土地动态监测等方面起着重要作用。由于农耕区土地类型复杂并且具有高异质性的特点,土地覆被信息提取的精度一直面临着挑战。因此,以Sentinel-2A/B多光谱遥感数据作为数据源,首先构建归一化植被指数(normalized difference vegetation index,NDVI)时序数据集和缨帽-湿度分量(tasseled cap wetness,TCW)时序数据集;其次,利用J-M(Jeffries-Matusita)距离对地物进行可分离性分析和挑选出NDVI和TCW最佳时序数据组合;最后,结合随机森林(random forest,RF)、支持向量机(support vector machine,SVM)、最大似然分类(maximum likelihood classification,MLC)3种分类算法以及利用单时相遥感数据对农耕区典型地物进行分类研究。研究结果表明:基于时间序列数据结合随机森林分类算法取得了较高的分类精度,其总体分类精度达到88.87%,Kappa系数达到0.8557,与利用单时相影像数据分类结果的精度相比分别提高了10.05百分点和0.2093,这充分说明利用时序数据结合RF分类算法在农耕地区能够有效提高典型地物的分类精度。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 0.8557,which improves the classification accuracy by 10.05 percentage points and 0.2093 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.

关 键 词：时间序列 随机森林 土地利用分类 农耕区 Sentinel-2A/B 

分 类 号：TP79[自动化与计算机技术—检测技术与自动化装置]