{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T16:26:27Z","timestamp":1775665587725,"version":"3.50.1"},"reference-count":43,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,5]],"date-time":"2023-01-05T00:00:00Z","timestamp":1672876800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100017054","name":"NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization","doi-asserted-by":"publisher","award":["U1609202"],"award-info":[{"award-number":["U1609202"]}],"id":[{"id":"10.13039\/100017054","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100017054","name":"NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization","doi-asserted-by":"publisher","award":["41376184"],"award-info":[{"award-number":["41376184"]}],"id":[{"id":"10.13039\/100017054","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100017054","name":"NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization","doi-asserted-by":"publisher","award":["40976109"],"award-info":[{"award-number":["40976109"]}],"id":[{"id":"10.13039\/100017054","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Natural Science Foundation of China","award":["U1609202"],"award-info":[{"award-number":["U1609202"]}]},{"name":"National Natural Science Foundation of China","award":["41376184"],"award-info":[{"award-number":["41376184"]}]},{"name":"National Natural Science Foundation of China","award":["40976109"],"award-info":[{"award-number":["40976109"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Although many machine learning methods have been successfully applied for the object-based classification of high resolution (HR) remote sensing imagery, current methods are highly dependent on the spectral similarity between segmented objects and have disappointingly poor performance when dealing with different segmented objects that have similar spectra. To overcome this limitation, this study exploited a knowledge graph (KG) that preserved the spatial relationships between segmented objects and has a reasoning capability that can assist in improving the probability of correctly classifying different segmented objects with similar spectra. In addition, to assist the knowledge graph classifications, an image segmentation method generating segmented objects that closely resemble real ground objects in size was used, which improves the integrity of the object classification results. Therefore, a novel HR remote sensing image classification scheme is proposed that involves a knowledge graph and an optimal segmentation algorithm, which takes full advantage of object-based classification and knowledge inference. This method effectively addresses the problems of object classification integrity and misclassification of objects with the same spectrum. In the evaluation experiments, three QuickBird-2 images and over 15 different land cover classes were utilized. The results showed that the classification accuracy of the proposed method is high, with overall accuracies exceeding 0.85. These accuracies are higher than the K Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF) methods. The evaluated results confirmed that the proposed method offers excellent performance in HR remote sensing image classification.<\/jats:p>","DOI":"10.3390\/rs15020321","type":"journal-article","created":{"date-parts":[[2023,1,5]],"date-time":"2023-01-05T05:29:48Z","timestamp":1672896588000},"page":"321","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Novel Knowledge Graph- and Knowledge Reasoning-Based Classification Prototype for OBIA Using High Resolution Remote Sensing Imagery"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7731-5287","authenticated-orcid":false,"given":"Zhao","family":"Gun","sequence":"first","affiliation":[{"name":"State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China"},{"name":"School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China"}]},{"given":"Jianyu","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhu, Q., Sun, X., Zhong, Y., and Zhang, L. (August, January 28). High-resolution remote sensing image scene understanding: A review. Proceedings of the IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8899293"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1479","DOI":"10.1109\/TIP.2005.852196","article-title":"Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization","volume":"14","author":"Chan","year":"2005","journal-title":"IEEE Trans. Image Process."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.isprsjprs.2017.06.001","article-title":"A review of supervised object-based land-cover image classification","volume":"130","author":"Ma","year":"2017","journal-title":"Isprs J. Photogramm. Remote Sens."},{"key":"ref_4","first-page":"4502817","article-title":"Exploration of glacial landforms by object-based image analysis and spectral parameters of digital elevation model","volume":"60","author":"Janowski","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1080\/15481603.2018.1426092","article-title":"Geographic object-based image analysis (GEOBIA): Emerging trends and future opportunities","volume":"55","author":"Chen","year":"2018","journal-title":"GIScience Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Kucharczyk, M., Hay, G.J., Ghaffarian, S., and Hugenholtz, C.H. (2020). Geographic object-based image analysis: A primer and future directions. Remote Sens., 12.","DOI":"10.3390\/rs12122012"},{"key":"ref_7","first-page":"725","article-title":"Advances in object-based image classification","volume":"37","author":"Aplin","year":"2008","journal-title":"Int. Arch.Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"6380","DOI":"10.3390\/rs70506380","article-title":"Object-based image analysis in wetland research: A review","volume":"7","author":"Dronova","year":"2015","journal-title":"Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.isprsjprs.2019.02.009","article-title":"Segmentation for Object-Based Image Analysis (OBIA): A review of algorithms and challenges from remote sensing perspective","volume":"150","author":"Hossain","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"389","DOI":"10.5721\/EuJRS20144723","article-title":"A review of remote sensing image classification techniques: The role of spatio-contextual information","volume":"47","author":"Li","year":"2014","journal-title":"Eur. J. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.isprsjprs.2020.08.004","article-title":"Exploring multiscale object-based convolutional neural network (multi-OCNN) for remote sensing image classification at high spatial resolution","volume":"168","author":"Martins","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1016\/j.proenv.2015.03.029","article-title":"Object-based image analysis for coral reef benthic habitat mapping with several classification algorithms","volume":"24","author":"Wahidin","year":"2015","journal-title":"Procedia Environ. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4032","DOI":"10.1109\/JSTARS.2018.2872969","article-title":"KNN-based representation of superpixels for hyperspectral image classification","volume":"11","author":"Tu","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"20","DOI":"10.38094\/jastt20165","article-title":"Classification based on decision tree algorithm for machine learning","volume":"2","author":"Charbuty","year":"2021","journal-title":"J. Appl. Sci. Technol. Trends"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random forest in remote sensing: A review of applications and future directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_16","first-page":"1","article-title":"Survey on SVM and their application in image classification","volume":"13","author":"Chandra","year":"2021","journal-title":"Int. J. Inf. Technol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"112948","DOI":"10.1016\/j.eswa.2019.112948","article-title":"A review: Knowledge reasoning over knowledge graph","volume":"141","author":"Chen","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.isprsjprs.2021.08.001","article-title":"Robust deep alignment network with remote sensing knowledge graph for zero-shot and generalized zero-shot remote sensing image scene classification","volume":"179","author":"Li","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_19","unstructured":"Pujara, J., Miao, H., Getoor, L., and Cohen, W. (2013, January 21\u201325). Knowledge graph identification. Proceedings of the International Semantic Web Conference, Sydney, NSW, Australia."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"57678","DOI":"10.1109\/ACCESS.2019.2912627","article-title":"Knowledge graph-based image classification refinement","volume":"7","author":"Zhang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"234","DOI":"10.2307\/143141","article-title":"A computer movie simulating urban growth in the Detroit region","volume":"46","author":"Tobler","year":"1970","journal-title":"Econ. Geogr."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"4544","DOI":"10.1109\/JSTARS.2022.3176612","article-title":"Remote Sensing Image Interpretation With Semantic Graph-Based Methods: A Survey","volume":"15","author":"Sun","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.isprsjprs.2019.08.014","article-title":"Optimizing multiscale segmentation with local spectral heterogeneity measure for high resolution remote sensing images","volume":"157","author":"Shen","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"7","DOI":"10.18778\/0208-6018.342.01","article-title":"Knowledge graph application in education: A literature review","volume":"3","author":"Rizun","year":"2019","journal-title":"Acta Univ. Lodz. Folia Oeconomica"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Tchechmedjiev, A., Fafalios, P., Boland, K., Gasquet, M., Zloch, M., Zapilko, B., Dietze, S., and Todorov, K. (2019, January 26\u201330). ClaimsKG: A knowledge graph of fact-checked claims. Proceedings of the International Semantic Web Conference, Auckland, New Zealand.","DOI":"10.1007\/978-3-030-30796-7_20"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"674428","DOI":"10.3389\/fnbot.2021.674428","article-title":"A Novel Encoder-Decoder Knowledge Graph Completion Model for Robot Brain","volume":"15","author":"Song","year":"2021","journal-title":"Front. Neurorobotics"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Hao, X., Ji, Z., Li, X., Yin, L., Liu, L., Sun, M., Liu, Q., and Yang, R. (2021). Construction and Application of a Knowledge Graph. Remote Sens., 13.","DOI":"10.3390\/rs13132511"},{"key":"ref_28","unstructured":"Tzotsos, A., and Argialas, D. (2006, January 1\u20135). MSEG: A generic region-based multi-scale image segmentation algorithm for remote sensing imagery. Proceedings of the ASPRS 2006 Annual Conference, Reno, Nevada."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1109\/TASLP.2013.2285474","article-title":"A Study of the Cosine Distance-Based Mean Shift for Telephone Speech Diarization","volume":"22","author":"Senoussaoui","year":"2014","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Choi, H., Som, A., and Turaga, P. (2020, January 14\u201319). AMC-loss: Angular margin contrastive loss for improved explainability in image classification. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00427"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1110","DOI":"10.1109\/LGRS.2018.2827845","article-title":"Joint euclidean and angular distance-based embeddings for multisource image analysis","volume":"15","author":"Yan","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"115","DOI":"10.31253\/te.v5i1.940","article-title":"Classification of Mint Leaf Types Based on the Image Using Euclidean Distance and K-Means Clustering with Shape and Texture Feature Extraction","volume":"5","author":"Harjanti","year":"2022","journal-title":"Tech-E"},{"key":"ref_33","unstructured":"Nair, B.B., and Sakthivel, N. (2022). A Deep Learning-Based Upper Limb Rehabilitation Exercise Status Identification System. Arabian Journal for Science and Engineering, 1\u201335."},{"key":"ref_34","first-page":"62","article-title":"Kappa coefficient: A popular measure of rater agreement","volume":"27","author":"Wan","year":"2015","journal-title":"Shanghai Arch. Psychiatry"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Phan, T.N., Kuch, V., and Lehnert, L.W. (2020). Land Cover Classification using Google Earth Engine and Random Forest Classifier\u2014The Role of Image Composition. Remote Sens., 12.","DOI":"10.3390\/rs12152411"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.patrec.2020.07.042","article-title":"Comparative analysis of image classification algorithms based on traditional machine learning and deep learning","volume":"141","author":"Wang","year":"2021","journal-title":"Pattern Recognit. Lett."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.neucom.2019.10.118","article-title":"A comprehensive survey on support vector machine classification: Applications, challenges and trends","volume":"408","author":"Cervantes","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1109\/MGRS.2016.2641240","article-title":"Remote Sensing Image Classification: A survey of support-vector-machine-based advanced techniques","volume":"5","author":"Maulik","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1082","DOI":"10.1109\/JSTARS.2018.2809781","article-title":"Cascaded random forest for hyperspectral image classification","volume":"11","author":"Zhang","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_40","unstructured":"Jin, X. (2009). A segmentation-based image processing system. (U.S. Patent WO2009065021 A1[P])."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"3585","DOI":"10.1080\/01431160802585348","article-title":"Image-object detectable in multiscale analysis on high-resolution remotely sensed imagery","volume":"30","author":"Chen","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1016\/j.patcog.2015.10.004","article-title":"A multiscale image segmentation method","volume":"52","author":"Li","year":"2016","journal-title":"Pattern Recognit."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Du, S., Du, S., Liu, B., and Zhang, X. (2019). Context-enabled extraction of large-scale urban functional zones from very-high-resolution images: A multiscale segmentation approach. Remote Sens., 11.","DOI":"10.3390\/rs11161902"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/2\/321\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T17:59:59Z","timestamp":1760119199000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/2\/321"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,5]]},"references-count":43,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["rs15020321"],"URL":"https:\/\/doi.org\/10.3390\/rs15020321","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,5]]}}}