地理本体驱动的遥感影像面向对象分析方法
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摘要
针对遥感影像面向对象分析技术存在的"分类过程中专家分析不同带来的分类结果不一致"问题,提出地理本体驱动的"地理实体描述-模型构建-影像对象分类"解译框架。首先,利用地理本体建立影像对象客观特征与地理专家知识的联系,实现对地理实体的描述与表达;其次,利用知识工程方法以及计算机可操作的形式化本体语言构建影像对象特征、分类器的本体模型,形成语义网络模型;最后,联合语义网络模型与专家规则实现影像对象的语义分类。地表覆盖分类实验结果表明,该方法不仅能够得到反映真实地理对象的遥感影像分类结果,而且能够掌握地理实体的语义信息,实现地表覆盖分类知识的共享与语义网络模型的复用,为遥感影像面向对象分析提供了一种全局性的解译分析框架及其方法。
One of the unsolved problems of geographic object-based image analysis(GEOBIA)is"the classification results may be inconsistent by different expert in the process of image analysis".Based on geo-ontology theory,this paper presents a novel framework "geo-graphical entity descriptionmodel building-object classification"to improve the interpretation of GEOBIA results.A geographical entity is expressed formally from the perspective of geo-ontology based on the characteristics of remote sensing image and expert knowledge.The semantic network model is built by using knowledge engineering methods and computer-actionable formal ontology languages.The image objects are classified based on semantic network model and expert rule.In the case of Land-cover classification,results show that,this method can not only obtain the classification results which reflect the geographical objects,but also grasp the semantic information of the geographical entities,and realize the sharing of land-cover classification knowledge and the reusing of the semantic network model.This new approach provides a holistic framework and method for GEOBIA.
One of the unsolved problems of geographic object-based image analysis(GEOBIA)is"the classification results may be inconsistent by different expert in the process of image analysis".Based on geo-ontology theory,this paper presents a novel framework "geo-graphical entity descriptionmodel building-object classification"to improve the interpretation of GEOBIA results.A geographical entity is expressed formally from the perspective of geo-ontology based on the characteristics of remote sensing image and expert knowledge.The semantic network model is built by using knowledge engineering methods and computer-actionable formal ontology languages.The image objects are classified based on semantic network model and expert rule.In the case of Land-cover classification,results show that,this method can not only obtain the classification results which reflect the geographical objects,but also grasp the semantic information of the geographical entities,and realize the sharing of land-cover classification knowledge and the reusing of the semantic network model.This new approach provides a holistic framework and method for GEOBIA.
引文
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[14]Zhang Ying.Study on Geo-ontology Progress and Application[J].Standardization of Surveying and Mapping,2014,30(2):24-27(张莹.地理本体的研究—研究进展与应用[J].测绘标准化,2014,30(2):24-27)
[15]Wei Yuanyuan.Research of Ontology-based Agricultural Knowledge Modeling and Reasoning[D].Hefei:University of Science and Technology of China,2011.(魏圆圆.基于本体论的农业知识建模及推理研究[D].合肥:中国科学技术大学,2011)
[16]Tonjrs R,Glowe S,Buckner J,et al.Knowledgebased Interpretation of RS Images Using Semantic Nets[J].Photogrammetric Engineering&Remote Sensing,1999,65(7):811-822
[17]Yang Y,Li H T,Han Y S,et al.High Resolution Remote Sensing Image Segmentation Based on Graph Theory and Fractal Net Evolution Approach[J].ISPRS-International Archives of the Photogrammetry,Remote Sensing and Spatial Information Sciences,2015,XL-7/W4:197-201
[2]Blaschke T,Hay G J,Kelly M,et al.Geographic Object-based Image Analysis:a New Paradigm in Remote Sensing and Geographic Information Science[J].ISPRS Journal of Photogrammetry and Remote Sensing,2014,87(1):180-191
[3]Belgiu M,Tomljenovic I,Lampoltshammer T J,et al.Ontology-based Classification of Building Types Detected From Airborne Laser Scanning Data[J].Remote Sensing,2014,6(2):1 347-1 366
[4]Belgiu M,Hofer B,Hofmann P.Coupling Formalized Knowledge Bases with Object-based Image Analysis[J].Remote Sensing Letters,2014,5(6):530-538
[5]Arvor D,Durieux L,Andres S,et al.Advances in Geographic Object-Based Image Analysis with Ontologies:A Review of main Contributions and Limitations from a Remote Sensing Perspective[J].ISPRS Journal of Photogrammetry and Remote Sensing,2013,82(8):125-137
[6]Lüscher P,Weibel R,Burghardt D.Integrating Ontological Modelling and Bayesian Inference for Pattern Classification in Topographic Vector Data[J].Computers Environment and Urban Systems,2009,33(5):363-374
[7]Gruber T R.A Translation Approach to Portable Ontology Specifications[J].Knowledge Acquisition,1993,5(2):199-220
[8]Andres S,Arvor D,Pierkot C.Towards an Ontological Approach for Classifying Remote Sensing Images[C].The Signal Image Technology and Internet Based Systems(SITIS),2012Eighth International Conference,Naples,Italy,2012
[9]Jesús M A J,Luis D,JoséA P F.A Framework for Ocean Satellite Image Classification Based on Ontologies[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2013,6(2):1 048-1 063
[10]Dejrriri K,Malki M.Object-based Image Analysis and Data Mining for Building Ontology of Informal Urban Settlements[C].The SPIE Remote Sensing,International Society for Optics and Photonics,Edinburgh,UK,2012
[11]Forestier G,Puissant A,Wemmert C,et al.Knowledge-based Region Labeling for Remote Sensing Image Interpretation[J].Computers Environment and Urban Systems,2012,36(5):470-480
[12]Kyzirakos K,Karpathiotakis M,Garbis G,et al.Wildfire Monitoring Using Satellite Images,Ontologies and Linked Geospatial Data[J].Web Semantics Science Services and Agents on the World Wide Web,2014,24(4):18-26
[13]Cui Wei,Tang Shiming,Li Rong,et al.A Method of Identifying Remote Sensing Objects by Using Geo-ontology and Relative Elevation[J].Journal of Wuhan University of technology(Transportation Science&Engineering),2013,37(4):695-698(崔巍,汤世明,李荣,等.用地理本体和相对高程识别遥感对象的方法研究[J].武汉理工大学学报·交通科学与工程版,2013,37(4):695-698)
[14]Zhang Ying.Study on Geo-ontology Progress and Application[J].Standardization of Surveying and Mapping,2014,30(2):24-27(张莹.地理本体的研究—研究进展与应用[J].测绘标准化,2014,30(2):24-27)
[15]Wei Yuanyuan.Research of Ontology-based Agricultural Knowledge Modeling and Reasoning[D].Hefei:University of Science and Technology of China,2011.(魏圆圆.基于本体论的农业知识建模及推理研究[D].合肥:中国科学技术大学,2011)
[16]Tonjrs R,Glowe S,Buckner J,et al.Knowledgebased Interpretation of RS Images Using Semantic Nets[J].Photogrammetric Engineering&Remote Sensing,1999,65(7):811-822
[17]Yang Y,Li H T,Han Y S,et al.High Resolution Remote Sensing Image Segmentation Based on Graph Theory and Fractal Net Evolution Approach[J].ISPRS-International Archives of the Photogrammetry,Remote Sensing and Spatial Information Sciences,2015,XL-7/W4:197-201
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