{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T18:15:36Z","timestamp":1775672136491,"version":"3.50.1"},"reference-count":61,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2019,7,19]],"date-time":"2019-07-19T00:00:00Z","timestamp":1563494400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","award":["250400"],"award-info":[{"award-number":["250400"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>With the advent of high-spatial resolution (HSR) satellite imagery, urban land use\/land cover (LULC) mapping has become one of the most popular applications in remote sensing. Due to the importance of context information (e.g., size\/shape\/texture) for classifying urban LULC features, Geographic Object-Based Image Analysis (GEOBIA) techniques are commonly employed for mapping urban areas. Regardless of adopting a pixel- or object-based framework, the selection of a suitable classifier is of critical importance for urban mapping. The popularity of deep learning (DL) (or deep neural networks (DNNs)) for image classification has recently skyrocketed, but it is still arguable if, or to what extent, DL methods can outperform other state-of-the art ensemble and\/or Support Vector Machines (SVM) algorithms in the context of urban LULC classification using GEOBIA. In this study, we carried out an experimental comparison among different architectures of DNNs (i.e., regular deep multilayer perceptron (MLP), regular autoencoder (RAE), sparse, autoencoder (SAE), variational autoencoder (AE), convolutional neural networks (CNN)), common ensemble algorithms (Random Forests (RF), Bagging Trees (BT), Gradient Boosting Trees (GB), and Extreme Gradient Boosting (XGB)), and SVM to investigate their potential for urban mapping using a GEOBIA approach. We tested the classifiers on two RS images (with spatial resolutions of 30 cm and 50 cm). Based on our experiments, we drew three main conclusions: First, we found that the MLP model was the most accurate classifier. Second, unsupervised pretraining with the use of autoencoders led to no improvement in the classification result. In addition, the small difference in the classification accuracies of MLP from those of other models like SVM, GB, and XGB classifiers demonstrated that other state-of-the-art machine learning classifiers are still versatile enough to handle mapping of complex landscapes. Finally, the experiments showed that the integration of CNN and GEOBIA could not lead to more accurate results than the other classifiers applied.<\/jats:p>","DOI":"10.3390\/rs11141713","type":"journal-article","created":{"date-parts":[[2019,7,22]],"date-time":"2019-07-22T02:55:37Z","timestamp":1563764137000},"page":"1713","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":204,"title":["Comparing Deep Neural Networks, Ensemble Classifiers, and Support Vector Machine Algorithms for Object-Based Urban Land Use\/Land Cover Classification"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3260-3952","authenticated-orcid":false,"given":"Shahab Eddin","family":"Jozdani","sequence":"first","affiliation":[{"name":"Department of Geography and Planning, Queen\u2019s University, Kingston, ON K7L 3N6, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1911-3585","authenticated-orcid":false,"given":"Brian Alan","family":"Johnson","sequence":"additional","affiliation":[{"name":"Natural Resources and Ecosystem Services Area, Institute for Global Environmental Strategies, 2108-1 Kamiyamaguchi, Hayama, Kanagawa 240-0115, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5419-8735","authenticated-orcid":false,"given":"Dongmei","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Geography and Planning, Queen\u2019s University, Kingston, ON K7L 3N6, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2019,7,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.isprsjprs.2009.06.004","article-title":"Object based image analysis for remote sensing","volume":"65","author":"Blaschke","year":"2010","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1016\/j.rse.2010.12.017","article-title":"Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery","volume":"115","author":"Myint","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.isprsjprs.2013.05.008","article-title":"Classifying a high resolution image of an urban area using super-object information","volume":"83","author":"Johnson","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.isprsjprs.2013.09.014","article-title":"Geographic Object-Based Image Analysis\u2014Towards a new paradigm","volume":"87","author":"Blaschke","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"859","DOI":"10.1080\/13658810903174803","article-title":"ESP: A tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data","volume":"24","author":"Tiede","year":"2010","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"684","DOI":"10.1080\/01431161.2017.1390273","article-title":"A regression modelling approach for optimizing segmentation scale parameters to extract buildings of different sizes","volume":"39","author":"Jozdani","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"4573","DOI":"10.1080\/01431161.2014.930206","article-title":"Meta-discoveries from a synthesis of satellite-based land-cover mapping research","volume":"35","author":"Yu","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1023\/A:1007607513941","article-title":"An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization","volume":"40","author":"Dietterich","year":"2000","journal-title":"Mach. Learn."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_11","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_12","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","article-title":"A fast learning algorithm for deep belief nets","volume":"18","author":"Hinton","year":"2006","journal-title":"Neural Comput."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2017.2762307","article-title":"Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources","volume":"5","author":"Zhu","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Boualleg, Y., Farah, M., and Farah, I.R. (2019). Remote Sensing Scene Classification Using Convolutional Features and Deep Forest Classifier. IEEE Geosci. Remote Sens. Lett., 1\u20135.","DOI":"10.1109\/LGRS.2019.2911855"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"506","DOI":"10.1080\/01431161.2018.1513666","article-title":"Very high resolution remote sensing image classification with SEEDS-CNN and scale effect analysis for superpixel CNN classification","volume":"40","author":"Lv","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"4544","DOI":"10.1080\/01431161.2019.1569281","article-title":"Exploring the addition of Landsat 8 thermal band in land-cover mapping","volume":"40","author":"Zhao","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1117\/1.JRS.12.025010","article-title":"Using convolutional neural network to identify irregular segmentation objects from very high-resolution remote sensing imagery","volume":"12","author":"Fu","year":"2018","journal-title":"J. Appl. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, Z., Li, X., and Yeh, G.A. (2019). Integration of Convolutional Neural Networks and Object-Based Post-Classification Refinement for Land Use and Land Cover Mapping with Optical and SAR Data. Remote Sens., 11.","DOI":"10.3390\/rs11060690"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"7053","DOI":"10.1007\/s00500-016-2247-2","article-title":"SVM or deep learning? A comparative study on remote sensing image classification","volume":"21","author":"Liu","year":"2017","journal-title":"Soft Comput."},{"key":"ref_20","unstructured":"Ng, A. (2017, August 10). Sparse Autoencoder. Available online: https:\/\/web.Stanf.Edu\/Cl.\/Cs294a\/Sparseautoencoder.pdf."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3373","DOI":"10.1109\/JSTARS.2017.2672736","article-title":"Object-Based Land-Cover Supervised Classification for Very-High-Resolution UAV Images Using Stacked Denoising Autoencoders","volume":"10","author":"Zhang","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_22","first-page":"3371","article-title":"Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion","volume":"11","author":"Vincent","year":"2010","journal-title":"J. Mach. Learn. Res."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.isprsjprs.2018.03.006","article-title":"Deep convolutional neural network training enrichment using multi-view object-based analysis of Unmanned Aerial systems imagery for wetlands classification","volume":"139","author":"Liu","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_24","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_25","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1080\/15481603.2018.1426091","article-title":"Comparing fully convolutional networks, random forest, support vector machine, and patch-based deep convolutional neural networks for object-based wetland mapping using images from small unmanned aircraft system","volume":"55","author":"Liu","year":"2018","journal-title":"GIScience Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2784","DOI":"10.1080\/01431161.2018.1433343","article-title":"Implementation of machine-learning classification in remote sensing: An applied review","volume":"39","author":"Maxwell","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1080\/2150704X.2017.1422873","article-title":"An object-based supervised classification framework for very-high-resolution remote sensing images using convolutional neural networks","volume":"9","author":"Zhang","year":"2018","journal-title":"Remote Sens. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.isprsjprs.2013.11.018","article-title":"Automated parameterisation for multi-scale image segmentation on multiple layers","volume":"88","author":"Csillik","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.rse.2018.06.034","article-title":"An object-based convolutional neural network (OCNN) for urban land use classification","volume":"216","author":"Zhang","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_30","unstructured":"Mason, L., Baxter, J., Bartlett, P., and Frean, M. (December, January 29). Boosting algorithms as gradient descent. Proceedings of the 12th International Conference on Neural Information, Denver, CO, USA."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016). XGBoost: Reliable Large-scale Tree Boosting System. arXiv.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_32","unstructured":"Breiman, L. (1984). Classification and Regression Trees, Chapman and Hall."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/BF00058655","article-title":"Bagging Predictors","volume":"24","author":"Breiman","year":"1996","journal-title":"Mach. Learn."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1016\/j.rse.2005.10.014","article-title":"Mapping invasive plants using hyperspectral imagery and Breiman Cutler classifications (randomForest)","volume":"100","author":"Lawrence","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1006\/jcss.1997.1504","article-title":"A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting","volume":"55","author":"Freund","year":"1997","journal-title":"J. Comput. Syst. Sci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy function approximation: A gradient boosting machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Stat."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1080\/15481603.2017.1408892","article-title":"Less is more: Optimizing classification performance through feature selection in a very-high-resolution remote sensing object-based urban application","volume":"55","author":"Georganos","year":"2018","journal-title":"GIScience Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1109\/LGRS.2018.2803259","article-title":"Very High Resolution Object-Based Land Use\u2013Land Cover Urban Classification Using Extreme Gradient Boosting","volume":"15","author":"Georganos","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1778","DOI":"10.1109\/TGRS.2004.831865","article-title":"Classification of hyperspectral remote sensing images with support vector machines","volume":"42","author":"Melgani","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.isprsjprs.2010.11.001","article-title":"Support vector machines in remote sensing: A review","volume":"66","author":"Mountrakis","year":"2011","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_41","unstructured":"Glorot, X., and Bengio, Y. (2010, January 13\u201315). Understanding the difficulty of training deep feedforward neural networks. Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, Sardinia, Italy."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_43","unstructured":"Kingma, D.P., and Welling, M. (2013). Auto-Encoding Variational Bayes. arXiv."},{"key":"ref_44","unstructured":"Doersch, C. (2016). Tutorial on Variational Autoencoders. arXiv."},{"key":"ref_45","unstructured":"Baatz, M., and Sch\u00e4pe, A. (2000). Multiresolution Segmentation: An optimization approach for high quality multi-scale image segmentation. Angewandte Geographische Informationsverarbeitung XII. Beitr\u00e4ge zum AGIT-Symposium Salzburg 2000, Herbert Wichmann Verlag."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2292","DOI":"10.3390\/ijgi4042292","article-title":"Image Segmentation Parameter Optimization Considering Within- and Between-Segment Heterogeneity at Multiple Scale Levels: Test Case for Mapping Residential Areas Using Landsat Imagery","volume":"4","author":"Johnson","year":"2015","journal-title":"ISPRS Int. J. Geo-Inf."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1080\/15481603.2017.1287238","article-title":"A comparison of unsupervised segmentation parameter optimization approaches using moderate- and high-resolution imagery","volume":"54","author":"Grybas","year":"2017","journal-title":"GIScience Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Georganos, S., Lennert, M., Grippa, T., Vanhuysse, S., Johnson, B., and Wolff, E. (2018). Normalization in Unsupervised Segmentation Parameter Optimization: A Solution Based on Local Regression Trend Analysis. Remote Sens., 10.","DOI":"10.3390\/rs10020222"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Johnson, A.B., and Jozdani, E.S. (2018). Identifying Generalizable Image Segmentation Parameters for Urban Land Cover Mapping through Meta-Analysis and Regression Tree Modeling. Remote Sens., 10.","DOI":"10.3390\/rs10010073"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Gholoobi, M., and Kumar, L. (2015). Using object-based hierarchical classification to extract land use land cover classes from high-resolution satellite imagery in a complex urban area. J. Appl. Remote Sens., 9.","DOI":"10.1117\/1.JRS.9.096052"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.isprsjprs.2014.12.026","article-title":"Training set size, scale, and features in Geographic Object-Based Image Analysis of very high resolution unmanned aerial vehicle imagery","volume":"102","author":"Ma","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"8368","DOI":"10.3390\/rs71013436","article-title":"Scale Issues Related to the Accuracy Assessment of Land Use\/Land Cover Maps Produced Using Multi-Resolution Data: Comments on \u201cThe Improvement of Land Cover Classification by Thermal Remote Sensing\u201d","volume":"7","author":"Johnson","year":"2015","journal-title":"Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Chawla, N.V., Bowyer, K.W., Hall, L.O., and Kegelmeyer, W.P. (2002). SMOTE: Synthetic minority over-sampling technique. J. Artif. Intell. Res., 16.","DOI":"10.1613\/jair.953"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Bogner, C., Seo, B., Rohner, D., and Reineking, B. (2018). Classification of rare land cover types: Distinguishing annual and perennial crops in an agricultural catchment in South Korea. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0190476"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/j.neunet.2018.07.011","article-title":"A systematic study of the class imbalance problem in convolutional neural networks","volume":"106","author":"Buda","year":"2018","journal-title":"Neural Netw."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"6969","DOI":"10.1080\/01431161.2013.810825","article-title":"A hybrid pansharpening approach and multiscale object-based image analysis for mapping diseased pine and oak trees","volume":"34","author":"Johnson","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_58","unstructured":"Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., and Devin, M. (2016). TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems. arXiv."},{"key":"ref_59","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.isprsjprs.2019.04.015","article-title":"Deep learning in remote sensing applications: A meta-analysis and review","volume":"152","author":"Ma","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Novelli, A., Aguilar, A.M., Aguilar, J.F., Nemmaoui, A., and Tarantino, E. (2017). AssesSeg\u2014A Command Line Tool to Quantify Image Segmentation Quality: A Test Carried Out in Southern Spain from Satellite Imagery. Remote Sens., 9.","DOI":"10.3390\/rs9010040"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/14\/1713\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:07:27Z","timestamp":1760188047000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/14\/1713"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,19]]},"references-count":61,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2019,7]]}},"alternative-id":["rs11141713"],"URL":"https:\/\/doi.org\/10.3390\/rs11141713","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,7,19]]}}}