{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T07:36:33Z","timestamp":1772868993796,"version":"3.50.1"},"reference-count":57,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2021,8,20]],"date-time":"2021-08-20T00:00:00Z","timestamp":1629417600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41901309"],"award-info":[{"award-number":["41901309"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41701433"],"award-info":[{"award-number":["41701433"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42090015"],"award-info":[{"award-number":["42090015"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Chinese Academy of Sciences, CAS \u201cLight of West China\u201d Program","award":["SDSQB-2020000032"],"award-info":[{"award-number":["SDSQB-2020000032"]}]},{"name":"the Youth Talent Team Program of the Institute of Mountain Hazards and Environment","award":["Y8R2230230"],"award-info":[{"award-number":["Y8R2230230"]}]},{"name":"Sichuan Science and Technology Program","award":["2020JDJQ0003"],"award-info":[{"award-number":["2020JDJQ0003"]}]},{"name":"the Second Tibetan Plateau Scienti\ufb01c Expedition and Research Program","award":["2019QZKK0308"],"award-info":[{"award-number":["2019QZKK0308"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Previous knowledge of the possible spatial relationships between land cover types is one factor that makes remote sensing image classification \u201csmarter\u201d. 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 \u201csiphonic effect\u201d 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.<\/jats:p>","DOI":"10.3390\/s21165602","type":"journal-article","created":{"date-parts":[[2021,8,22]],"date-time":"2021-08-22T22:59:27Z","timestamp":1629673167000},"page":"5602","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Remote Sensing Image Classification with a Graph-Based Pre-Trained Neighborhood Spatial Relationship"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0030-2335","authenticated-orcid":false,"given":"Xudong","family":"Guan","sequence":"first","affiliation":[{"name":"Research Center for Digital Mountain and Remote Sensing Application, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China"}]},{"given":"Chong","family":"Huang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"}]},{"given":"Juan","family":"Yang","sequence":"additional","affiliation":[{"name":"Shaanxi Energy Institute, Xianyang 712000, China"}]},{"given":"Ainong","family":"Li","sequence":"additional","affiliation":[{"name":"Research Center for Digital Mountain and Remote Sensing Application, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4443","DOI":"10.3934\/mbe.2020245","article-title":"Multi-spectral remote sensing images feature coverage classification based on improved convolutional neural network","volume":"17","author":"Li","year":"2020","journal-title":"Math. Biosci. Eng."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1451","DOI":"10.1109\/TGRS.2013.2251468","article-title":"Robust Autodual Morphological Profiles for the Classification of High-Resolution Satellite Images","volume":"52","author":"Luo","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3048","DOI":"10.1080\/01431161.2011.625055","article-title":"A modified object-oriented classification algorithm and its application in high-resolution remote-sensing imagery","volume":"33","author":"Chen","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1109\/LGRS.2017.2657778","article-title":"Training Deep Convolutional Neural Networks for Land-Cover Classification of High-Resolution Imagery","volume":"14","author":"Scott","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3030","DOI":"10.1109\/JSTARS.2018.2846178","article-title":"Deep Convolutional Neural Network for Complex Wetland Classification Using Optical Remote Sensing Imagery","volume":"11","author":"Rezaee","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"778","DOI":"10.1109\/LGRS.2017.2681128","article-title":"Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data","volume":"14","author":"Kussul","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3735","DOI":"10.1109\/JSTARS.2020.3005403","article-title":"Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities","volume":"13","author":"Cheng","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Gu, Y.T., Wang, Y.T., and Li, Y.S. (2019). A Survey on Deep Learning-Driven Remote Sensing Image Scene Understanding: Scene Classification, Scene Retrieval and Scene-Guided Object Detection. Appl. Sci., 9.","DOI":"10.3390\/app9102110"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1604","DOI":"10.1109\/TGRS.2020.2999962","article-title":"Assessing the Threat of Adversarial Examples on Deep Neural Networks for Remote Sensing Scene Classification: Attacks and Defenses","volume":"59","author":"Xu","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"9667","DOI":"10.1007\/s11042-018-6548-6","article-title":"Analysis of the inter-dataset representation ability of deep features for high spatial resolution remote sensing image scene classification","volume":"78","author":"Zhao","year":"2019","journal-title":"Multimed. Tools Appl."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1276","DOI":"10.1016\/j.rse.2009.02.014","article-title":"A neural network approach using multi-scale textural metrics from very high-resolution panchromatic imagery for urban land-use classification","volume":"113","author":"Pacifici","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/MGRS.2016.2540798","article-title":"Deep Learning for Remote Sensing Data A technical tutorial on the state of the art","volume":"4","author":"Zhang","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"24","DOI":"10.2747\/1548-1603.44.1.24","article-title":"An Object-Based Classification Approach in Mapping Tree Mortality Using High Spatial Resolution Imagery","volume":"44","author":"Guo","year":"2007","journal-title":"Giscience Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1109\/TGRS.1987.289805","article-title":"An expert system for remote sensing","volume":"25","author":"Goodenough","year":"1987","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/S0304-3800(01)00371-4","article-title":"Assessing temporal and spatial changes of salinity using fuzzy logic, remote sensing and gis. Foundations of an expert system","volume":"144","author":"Metternicht","year":"2001","journal-title":"Ecol. Model."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1109\/36.368222","article-title":"An expert system for land cover classification","volume":"33","author":"Kartikeyan","year":"1995","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"811","DOI":"10.1080\/014311697218773","article-title":"Remote sensing image analysis using a neural network and knowledge-based processing","volume":"18","author":"Murai","year":"1997","journal-title":"Int. J. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1007\/BF02760392","article-title":"A prototype expert system for interpretation of remote sensing image data","volume":"19","author":"Sarma","year":"1994","journal-title":"Sadhana"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1109\/36.481896","article-title":"Knowledge-based land-cover classification using ERS-1\/JERS-1 SAR composites","volume":"34","author":"Dobson","year":"1996","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1080\/01431160903252327","article-title":"Contextual land-cover classification: Incorporating spatial dependence in land-cover classification models using random forests and the getis statistic","volume":"1","author":"Ghimire","year":"2010","journal-title":"Remote Sens. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/j.isprsjprs.2003.10.002","article-title":"Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information","volume":"58","author":"Benz","year":"2004","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"932","DOI":"10.1109\/TGRS.2015.2469691","article-title":"Classification of polarimetric sar images based on modeling contextual information and using texture features","volume":"54","author":"Masjedi","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1109\/LGRS.2009.2031686","article-title":"Change Detection in Optical Remote Sensing Images Using Difference-Based Methods and Spatial Information","volume":"7","author":"Dianat","year":"2010","journal-title":"IEEE Geosci. Remote. Sens. Lett."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Cui, W., Wang, F., He, X., Zhang, D.Y., Xu, X.X., Yao, M., Wang, Z.W., and Huang, J.J. (2019). Multi-Scale Semantic Segmentation and Spatial Relationship Recognition of Remote Sensing Images Based on an Attention Model. Remote Sens., 11.","DOI":"10.3390\/rs11091044"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"710","DOI":"10.1080\/17538947.2014.925517","article-title":"Spatial relationship-assisted classification from high-resolution remote sensing imagery","volume":"8","author":"Qiao","year":"2015","journal-title":"Int. J. Digital Earth"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3198","DOI":"10.1109\/TGRS.2010.2044508","article-title":"Rule-Based Classification of a Very High Resolution Image in an Urban Environment Using Multispectral Segmentation Guided by Cartographic Data","volume":"48","author":"Bouziani","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","unstructured":"Benz, U., and Pottier, E. (2001, January 9\u201313). Object based analysis of polarimetric SAR data in alpha-entropy-anisotropy decomposition using fuzzy classification by eCognition. Proceedings of the IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium, Sydney, Australia."},{"key":"ref_28","unstructured":"Guarino, N. (1998, January 6\u20138). Formal Ontology in Information Systems. Proceedings of the 1st International Conference on Formal Ontology in Information Systems, Trento, Italy."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"470","DOI":"10.1016\/j.compenvurbsys.2012.01.003","article-title":"Knowledge-based region labeling for remote sensing image interpretation","volume":"36","author":"Forestier","year":"2012","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2138","DOI":"10.1109\/JSTARS.2015.2433257","article-title":"Expert Knowledge-Based Method for Satellite Image Time Series Analysis and Interpretation","volume":"8","author":"Rejichi","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1347","DOI":"10.3390\/rs6021347","article-title":"Ontology-Based Classification of Building Types Detected from Airborne Laser Scanning Data","volume":"6","author":"Mariana","year":"2014","journal-title":"Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.cageo.2012.11.023","article-title":"Coastal image interpretation using background knowledge and semantics","volume":"54","author":"Forestier","year":"2013","journal-title":"Comput. Geosci."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"530","DOI":"10.1080\/2150704X.2014.930563","article-title":"Coupling formalized knowledge bases with object-based image analysis","volume":"5","author":"Belgiu","year":"2014","journal-title":"Remote Sens. Lett."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Witharana, C., Bhuiyan, M.A.E., Liljedahl, A.K., Kanevskiy, M., Jorgenson, T., Jones, B.M., Daanen, R., Epstein, H.E., Griffin, C.G., and Kent, K. (2021). An object-based approach for mapping tundra ice-wedge polygon troughs from very high spatial resolution optical satellite imagery. Remote Sens., 13.","DOI":"10.3390\/rs13040558"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1007\/s10750-016-2928-y","article-title":"Mapping of submerged aquatic vegetation in rivers from very high-resolution image data, using object-based image analysis combined with expert knowledge","volume":"812","author":"Visser","year":"2018","journal-title":"Hydrobiologia"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1146\/annurev-ecolsys-102209-144718","article-title":"From Graphs to Spatial Graphs","volume":"41","author":"Dale","year":"2010","journal-title":"Annu. Rev. Ecol. Evol. Syst."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"580","DOI":"10.1080\/13658816.2014.989856","article-title":"Graph-assisted landscape monitoring","volume":"29","author":"Cheung","year":"2015","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Xu, C., and Liu, W. (2021). Integrating a Three-Level GIS Framework and a Graph Model to Track, Represent, and Analyze the Dynamic Activities of Tidal Flats. ISPRS Int. J. Geoinf., 10.","DOI":"10.3390\/ijgi10020061"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Ouyang, S., and Li, Y. (2021). Combining Deep Semantic Segmentation Network and Graph Convolutional Neural Network for Semantic Segmentation of Remote Sensing Imagery. Remote Sens., 13.","DOI":"10.3390\/rs13010119"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Li, Y., Chen, R., Zhang, Y., Zhang, M., and Chen, L. (2020). Multi-Label Remote Sensing Image Scene Classification by Combining a Convolutional Neural Network and a Graph Neural Network. Remote Sens., 12.","DOI":"10.3390\/rs12234003"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.patrec.2016.01.022","article-title":"Graph-based semi-supervised learning for spectral-spatial hyperspectral image classification","volume":"83","author":"Ma","year":"2016","journal-title":"Pattern Recognit. Lett."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Pu, S., Wu, Y., Sun, X., and Sun, X. (2021). Hyperspectral Image Classification with Localized Graph Convolutional Filtering. Remote Sens., 13.","DOI":"10.3390\/rs13030526"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Jabari, S., and Zhang, Y. (2014, January 23\u201328). Building detection in very high resolution satellite image using HIS model. Proceedings of the ASPRS 2014 Annual Conference, Louisville, KY, USA.","DOI":"10.1109\/IGARSS.2014.6946930"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.isprsjprs.2013.02.006","article-title":"Automatic fuzzy object-based analysis of VHSR images for urban objects extraction","volume":"79","author":"Sebari","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"762","DOI":"10.3390\/a6040762","article-title":"Very high resolution satellite image classification using fuzzy rule-based systems","volume":"6","author":"Jabari","year":"2013","journal-title":"Algorithms"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"575","DOI":"10.1016\/j.isprsjprs.2009.04.001","article-title":"Knowledge based reconstruction of building models from terrestrial laser scanning data","volume":"64","author":"Pu","year":"2009","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"2731","DOI":"10.1109\/TIP.2018.2810516","article-title":"Automatic registration of images with inconsistent content through line-support region segmentation and geometrical outlier removal","volume":"27","author":"Zhao","year":"2018","journal-title":"IEEE Trans. Img. Proc."},{"key":"ref_48","first-page":"1179","article-title":"Finding Prototypes for Nearest Neighbor Classifiers","volume":"23","year":"1974","journal-title":"IEEE Trans. Comput."},{"key":"ref_49","first-page":"207","article-title":"Distance Metric Learning for Large Margin Nearest Neighbor Classification","volume":"10","author":"Weinberger","year":"2009","journal-title":"J. Mach. Learn. Res."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"558","DOI":"10.1109\/TFUZZ.2010.2042721","article-title":"FSVM-CIL: Fuzzy Support Vector Machines for Class Imbalance Learning","volume":"18","author":"Batuwita","year":"2010","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"ref_51","first-page":"1889","article-title":"Working set selection using second order information for training SVM","volume":"6","author":"Fan","year":"2005","journal-title":"J. Mach. Learn. Res."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/S0165-0114(02)00514-6","article-title":"Support vector fuzzy regression machines","volume":"138","author":"Hong","year":"2003","journal-title":"Fuzzy Sets Syst."},{"key":"ref_53","unstructured":"(2021, March 17). Chesapeake Bay. Available online: http:\/\/en.volupedia.org\/wiki\/Chesapeake_Bay."},{"key":"ref_54","unstructured":"(2021, March 17). Kent County, Delaware. Available online: https:\/\/en.wikipedia.org\/wiki\/Kent_County,_Delaware."},{"key":"ref_55","unstructured":"(2021, April 07). Libsvm. Available online: https:\/\/www.csie.ntu.edu.tw\/~cjlin\/libsvm\/."},{"key":"ref_56","first-page":"735","article-title":"Statistical significance and normalized confusion matrices","volume":"63","author":"Hardin","year":"1997","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1111\/j.1467-8306.2004.09402009.x","article-title":"On the First Law of Geography: A Reply","volume":"94","author":"Tobler","year":"2004","journal-title":"Ann Assoc Am Geogr."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/16\/5602\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:47:40Z","timestamp":1760165260000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/16\/5602"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,20]]},"references-count":57,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2021,8]]}},"alternative-id":["s21165602"],"URL":"https:\/\/doi.org\/10.3390\/s21165602","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,20]]}}}