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Enhancing Land Cover Mapping through Integration of Pixel-Based and Object-Based Classifications from Remotely Sensed Imagery

文献类型：期刊论文

作者Chen, Yuehong1; Zhou, Ya'nan1; Ge, Yong2; An, Ru1; Chen, Yu3
刊名REMOTE SENSING
出版日期2018
卷号10期号:1页码:15
关键词land cover mapping mixed object uncertainty pixel-based classification object-based classification integration
ISSN号2072-4292
DOI10.3390/rs10010077
通讯作者Zhou, Ya'nan(zhouyn@hhu.edu.cn)
英文摘要Pixel-based and object-based classifications are two commonly used approaches in extracting land cover information from remote sensing images. However, they each have their own inherent merits and limitations. This study, therefore, proposes a new classification method through the integration of pixel-based and object-based classifications (IPOC). Firstly, it employs pixel-based soft classification to obtain the class proportions of pixels to characterize the land cover details from pixel-scale properties. Secondly, it adopts area-to-point kriging to explore the class spatial dependence between objects for each pixel from object-based soft classification results. Thirdly, the class proportions of pixels and the class spatial dependence of pixels are fused as the class occurrence of pixels. Last, a linear optimization model on objects is built to determine the optimal class label of pixels within each object. Two remote sensing images are used to evaluate the effectiveness of IPOC. The experimental results demonstrate that IPOC performs better than the traditional pixel-based hard classification and object-based hard classification methods. Specifically, the overall accuracy of IPOC is 7.64% higher than that of pixel-based hard classification and 4.64% greater than that of object-based hard classification in the first experiment, while the overall accuracy improvements in the second experiment are 3.59% and 3.42%, respectively. Meanwhile, IPOC produces less salt and pepper effect than the pixel-based hard classification method and generates more accurate land cover details and small patches than the object-based hard classification method.
WOS关键词SATELLITE IMAGES ; ORIENTED METHODS ; SENSING IMAGERY ; LARGE AREAS ; ALGORITHM ; ACCURACY ; STRATEGY ; MODEL
资助项目National Natural Science Foundation of China[41701376] ; National Natural Science Foundation of China[41501453] ; Natural Science Foundation of Jiangsu Province[BK20170866] ; Key Program of Chinese Academy of Sciences[ZDRW-ZS-2016-6-3-4] ; Fundamental Research Funds for the Central Universities[2017B11714] ; Fundamental Research Funds for the Central Universities[2016B11414] ; China Postdoctoral Science Foundation[2016M600356] ; State Key Laboratory of Resources and Environmental Information System, China
WOS研究方向Remote Sensing
语种英语
WOS记录号WOS:000424092300076
出版者MDPI AG
资助机构National Natural Science Foundation of China ; Natural Science Foundation of Jiangsu Province ; Key Program of Chinese Academy of Sciences ; Fundamental Research Funds for the Central Universities ; China Postdoctoral Science Foundation ; State Key Laboratory of Resources and Environmental Information System, China
源URL[http://ir.igsnrr.ac.cn/handle/311030/56945]  
专题中国科学院地理科学与资源研究所
通讯作者Zhou, Ya'nan
作者单位1.Hohai Univ, Sch Earth Sci & Engn, Nanjing 210098, Jiangsu, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
3.Nanjing Normal Univ, Sch Geog Sci, Nanjing 210023, Jiangsu, Peoples R China
推荐引用方式
GB/T 7714		 Chen, Yuehong,Zhou, Ya'nan,Ge, Yong,et al. Enhancing Land Cover Mapping through Integration of Pixel-Based and Object-Based Classifications from Remotely Sensed Imagery[J]. REMOTE SENSING,2018,10(1):15.
APA Chen, Yuehong,Zhou, Ya'nan,Ge, Yong,An, Ru,&Chen, Yu.(2018).Enhancing Land Cover Mapping through Integration of Pixel-Based and Object-Based Classifications from Remotely Sensed Imagery.REMOTE SENSING,10(1),15.
MLA Chen, Yuehong,et al."Enhancing Land Cover Mapping through Integration of Pixel-Based and Object-Based Classifications from Remotely Sensed Imagery".REMOTE SENSING 10.1(2018):15.

入库方式： OAI收割

来源：地理科学与资源研究所

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