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博士生李桂娥的论文在 International Journal of Remote Sensing 刊出
发布时间:2019-04-22 11:04:32     发布者:易真     浏览次数:

标题:A comparison of machine learning approaches for identifying high-poverty counties: robust features of DMSP/OLS night-time light imagery

作者: Guie Li, Zhongliang Cai, Xiaojian Liu, Ji Liu, Shiliang Su

来源出版物:International Journal of Remote Sensing  DOI10.1080/01431161.2019.1580820出版时间:21 Feb 2019

摘要: The goal of the present study is to demonstrate that high-poverty counties and robust classification features can be identified by machine learning approaches using only DMSP/OLS night-time light imagery. To accomplish this goal, a total of 96 high-poverty and 96 non-poverty counties were classified using 15 statistical and spatial features extracted from night-time light imagery in China in 2010 formed a training set for identifying high-poverty counties. Seven machine learning approaches were adopted to classify high-poverty counties, and five feature importance measures were used to select robust features. The resulting metrics, including the user’s (>63%), producer’s (>66%) and overall (>82%) accuracies of the poor county identification (probability of poverty greater than 0.6), show that the seven machine learning approaches used in this paper exhibit good performance, although some differences exist among the approaches. The order of feature importance reveals that the relative importance of each feature differs among the models; however, the important features remain consistent. The nine most important features ranked in each approach are relatively robust for poverty identification at the county level. Both spatial feature and statistical features calculated in part from the central tendency, degree of dispersion, and the distribution of the night-time light data were identified as indispensable robust features in all the approaches, indicating that the complex social phenomenon of poverty requires analysis from different aspects. Previous studies that utilized primarily night-time light imagery applied single features related to the central tendency or the distribution features of the imagery; this study provides a new method and can act as a reference for feature selection and identification of high-poverty counties using night-time light imagery and has potential applications across several scientific domains.

文献类型:Article

语种:English

通讯作者地址:Zhongliang Cai , School of Resource and Environmental Science, Wuhan University, Wuhan, China; Key Laboratory of Geographical Information Systems, Ministry of Education, Wuhan University, Wuhan, China

电子邮件地址:zlcai@whu.edu.cn

地址:

[Guie Li, Xiaojian Liu]School of Resource and Environmental Science, Wuhan University, Wuhan, China

[Zhongliang Cai, Shiliang Su]School of Resource and Environmental Science, Wuhan University, Wuhan, China; Key Laboratory of Geographical Information Systems, Ministry of Education, Wuhan University, Wuhan, China

[Ji Liu] The Second Survey and Mapping Institute of Guizhou Province, Guiyang, China

影响因子:1.782


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