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Remote Sensing Image Classification with a Graph-Based Pre-Trained Neighborhood Spatial Relationship

文献类型：期刊论文

作者Guan, Xudong2; Huang, Chong1; Yang, Juan3; Li, Ainong2
刊名SENSORS
出版日期2021-08-01
卷号21期号:16页码:28
关键词remote sensing image classification SVM (Support Vector Machine) knowledge graph object-based image analysis fuzzy classification graph theory
DOI10.3390/s21165602
通讯作者Li, Ainong(ainongli@imde.ac.cn)
英文摘要Previous knowledge of the possible spatial relationships between land cover types is one factor that makes remote sensing image classification "smarter". In recent years, knowledge graphs, which are based on a graph data structure, have been studied in the community of remote sensing for their ability to build extensible relationships between geographic entities. This paper implements a classification scheme considering the neighborhood relationship of land cover by extracting information from a graph. First, a graph representing the spatial relationships of land cover types was built based on an existing land cover map. Empirical probability distributions of the spatial relationships were then extracted using this graph. Second, an image was classified based on an object-based fuzzy classifier. Finally, the membership of objects and the attributes of their neighborhood objects were joined to decide the final classes. Two experiments were implemented. Overall accuracy of the two experiments increased by 5.2% and 0.6%, showing that this method has the ability to correct misclassified patches using the spatial relationship between geo-entities. However, two issues must be considered when applying spatial relationships to image classification. The first is the "siphonic effect" produced by neighborhood patches. Second, the use of global spatial relationships derived from a pre-trained graph loses local spatial relationship in-formation to some degree.
WOS关键词OBJECT-BASED ANALYSIS ; LAND-COVER ; EXPERT-SYSTEM ; SCENE CLASSIFICATION ; NEURAL-NETWORK ; KNOWLEDGE ; INFORMATION ; GIS ; SEGMENTATION ; ONTOLOGY
资助项目National Science Foundation of China[41901309] ; National Science Foundation of China[41701433] ; National Science Foundation of China[42090015] ; Youth Talent Team Program of the Institute of Mountain Hazards and Environment, CAS[SDSQB2020000032] ; Youth Talent Team Program of the Institute of Mountain Hazards and Environment, CAS[Y8R2230230] ; Sichuan Science and Technology Program[2020JDJQ0003] ; Second Tibetan Plateau Scientific Expedition and Research Program[2019QZKK0308] ; CAS Light of West China Program
WOS研究方向Chemistry ; Engineering ; Instruments & Instrumentation
语种英语
WOS记录号WOS:000689803200001
出版者MDPI
资助机构National Science Foundation of China ; Youth Talent Team Program of the Institute of Mountain Hazards and Environment, CAS ; Sichuan Science and Technology Program ; Second Tibetan Plateau Scientific Expedition and Research Program ; CAS Light of West China Program
源URL[http://ir.igsnrr.ac.cn/handle/311030/165109]  
专题中国科学院地理科学与资源研究所
通讯作者Li, Ainong
作者单位1.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
2.Chinese Acad Sci, Res Ctr Digital Mt & Remote Sensing Applicat, Inst Mt Hazards & Environm, Chengdu 610041, Peoples R China
3.Shaanxi Energy Inst, Xianyang 712000, Peoples R China
推荐引用方式
GB/T 7714		 Guan, Xudong,Huang, Chong,Yang, Juan,et al. Remote Sensing Image Classification with a Graph-Based Pre-Trained Neighborhood Spatial Relationship[J]. SENSORS,2021,21(16):28.
APA Guan, Xudong,Huang, Chong,Yang, Juan,&Li, Ainong.(2021).Remote Sensing Image Classification with a Graph-Based Pre-Trained Neighborhood Spatial Relationship.SENSORS,21(16),28.
MLA Guan, Xudong,et al."Remote Sensing Image Classification with a Graph-Based Pre-Trained Neighborhood Spatial Relationship".SENSORS 21.16(2021):28.

入库方式： OAI收割

来源：地理科学与资源研究所

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