一种应用兴趣点数据进行地表覆盖分类的方法
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摘要
针对传统基于遥感影像的地表覆盖分类方法普遍存在的生产周期长、成本高、自动化程度低等问题,提出了一种完全利用兴趣点(point of interest,POI)数据进行地表覆盖自动化分类的方法。首先应用潜在狄利克雷分布主题计算模型,从POI数据的文本信息中挖掘出与地表覆盖类型相关的主题类型和分布概率;然后基于POI文本的主题分布,运用支持向量机分类算法构建地表覆盖分类模型;最后以遥感影像地表覆盖分类结果为依据,采用随机抽样的方式对所提方法进行验证。结果表明,该方法能够较好地区分人造地表和非人造地表,且整体分类精度超过80%,可作为传统遥感影像分类的辅助手段,满足地表覆盖快速分类的制图需求。
Traditional land cover classification process is very complicated, time-consuming and labor-intensive, which requires huge amount of imagery data and involves many people. Recently, crowd-sourcing data have been used for land cover classification with lower costs, but they are still time-consuming due to the process of interpreting data. We examine the potential of textual information in point of interest(POI) as a new reference source. Firstly, POI textual data is analyzed to calculate the word distributions and topic distributions of POI using latent Dirichlet allocation(LDA) topic model. Secondly, support vector machine(SVM) algorithm is applied with topic distributions of POI to build a land cover classification model. Finally, we evaluate the land cover classification result by taking a random sample of remote sensing images. In the experiments, 1.9 million POIs from Weibo, Baidu and Gaode are used to test the proposed method,and result shows that a classification accuracy of over 80% is achieved.
Traditional land cover classification process is very complicated, time-consuming and labor-intensive, which requires huge amount of imagery data and involves many people. Recently, crowd-sourcing data have been used for land cover classification with lower costs, but they are still time-consuming due to the process of interpreting data. We examine the potential of textual information in point of interest(POI) as a new reference source. Firstly, POI textual data is analyzed to calculate the word distributions and topic distributions of POI using latent Dirichlet allocation(LDA) topic model. Secondly, support vector machine(SVM) algorithm is applied with topic distributions of POI to build a land cover classification model. Finally, we evaluate the land cover classification result by taking a random sample of remote sensing images. In the experiments, 1.9 million POIs from Weibo, Baidu and Gaode are used to test the proposed method,and result shows that a classification accuracy of over 80% is achieved.
引文
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[13]Lansley G,Longley P A.The Geography of Twitter Topics in London[J].Computers Environment and Urban Systems,2016,58:85-96
[14]Griffiths T L,Steyvers M.Finding Scientific Topics[J].Proceedings of the National Academy of Sciences of the United States of America,2004,101(S1):5 228-5 235
[2]Chen Jun,Chen Jin,Liao Anping,et al.Concept Sand Key Techniques for 30 m Global Land Cover Mapping[J].Acta Geodaetica et Cartographica Sinica,2014,43(6):551-557(陈军,陈晋,廖安平,等.全球30 m地表覆盖遥感制图的总体技术[J].测绘学报,2014,43(6):551-557)
[3]Sitthi A,Nagai M,Dailey M,et al.Exploring Land Use and Land Cover of Geotagged Social-Sensing Images Using Naive Bayes Classifier[J].Sustainability,2016,8(9):921-942
[4]See L,Schepaschenko D,Lesiv M,et al.Building a Hybrid Land Cover Map with Crowdsourcing and Geographically Weighted Regression[J].ISPRSJournal of Photogrammetry and Remote Sensing,2015,103:48-56
[5]Lu Guozhen,Chang Xiaomeng,Li Qingquan,et al.Land Use Classification Based on Massive HumanActivity Spatio-temporal Data[J].Journal of Geo-information Science,2015,17(12):1 497-1 505(鲁国珍,常晓猛,李清泉,等.基于人类时空活动的城市土地利用分类研究[J].地球信息科学学报,2015,17(12):1 497-1 505)
[6]Hu T,Yang J,Li X,et al.Mapping Urban Land Use by Using Landsat Images and Open Social Data[J].Remote Sensing,2016,8(2):151-168
[7]Xing H,Meng Y,Hou D,et al.Exploring Point-ofInterest Data from Social Media for Artificial Surface Validation with Decision Trees[J].International Journal of Remote Sensing,2017,38(23):6 945-6 969
[8]Xing H,Meng Y,Hou D,et al.Employing Crowdsourced Geographic Information to Classify Land Cover with Spatial Clustering and Topic Model[J].Remote Sensing,2017,9(6):602-621
[9]Meng Y,Hou D,Xing H.Rapid Detection of Land Cover Changes Using Crowdsourced Geographic Information:A Case Study of Beijing,China[J].Sustainability,2017,9(9):1 547-1 562
[10]Blei D M,Ng A Y,Jordan M I.Latent Dirichlet Allocation[J].Journal of Machine Learning Research,2003,3:993-1 022
[11]Cortes C,Vapnik V.Support-Vector Networks[J].Machine Learning,1995,20(3):273-297
[12]Adams B,Janowicz K.Thematic Signatures for Cleansing and Enriching Place-related Linked Data[J].International Journal of Geographical Information Science,2015,29(4):556-579
[13]Lansley G,Longley P A.The Geography of Twitter Topics in London[J].Computers Environment and Urban Systems,2016,58:85-96
[14]Griffiths T L,Steyvers M.Finding Scientific Topics[J].Proceedings of the National Academy of Sciences of the United States of America,2004,101(S1):5 228-5 235