{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T11:16:03Z","timestamp":1767611763710,"version":"build-2065373602"},"reference-count":66,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2018,3,14]],"date-time":"2018-03-14T00:00:00Z","timestamp":1520985600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Fully Convolutional Networks (FCN) has shown better performance than other classifiers like Random Forest (RF), Support Vector Machine (SVM) and patch-based Deep Convolutional Neural Network (DCNN), for object-based classification using orthoimage only in previous studies; however, for further improving deep learning algorithm performance, multi-view data should be considered for training data enrichment, which has not been investigated for FCN. The present study developed a novel OBIA classification using FCN and multi-view data extracted from small Unmanned Aerial System (UAS) for mapping landcovers. Specifically, this study proposed three methods to automatically generate multi-view training samples from orthoimage training datasets to conduct multi-view object-based classification using FCN, and compared their performances with each other and also with RF, SVM, and DCNN classifiers. The first method does not consider the object surrounding information, while the other two utilized object context information. We demonstrated that all the three versions of FCN multi-view object-based classification outperformed their counterparts utilizing orthoimage data only. Furthermore, the results also showed that when multi-view training samples were prepared with consideration of object surroundings, FCN trained with these samples gave much better accuracy than FCN classification trained without context information. Similar accuracies were achieved from the two methods utilizing object surrounding information, although sample preparation was conducted using two different ways. When comparing FCN with RF, SVM, DCNN implies that FCN generally produced better accuracy than the other classifiers, regardless of using orthoimage or multi-view data.<\/jats:p>","DOI":"10.3390\/rs10030457","type":"journal-article","created":{"date-parts":[[2018,3,15]],"date-time":"2018-03-15T05:06:43Z","timestamp":1521090403000},"page":"457","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["An Object-Based Image Analysis Method for Enhancing Classification of Land Covers Using Fully Convolutional Networks and Multi-View Images of Small Unmanned Aerial System"],"prefix":"10.3390","volume":"10","author":[{"given":"Tao","family":"Liu","sequence":"first","affiliation":[{"name":"School of Forest Resources and Conservation, University of Florida, Gainesville, FL 32611, USA"},{"name":"Gulf Coast Research Center, University of Florida, Plant City, FL 33563, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6182-4017","authenticated-orcid":false,"given":"Amr","family":"Abd-Elrahman","sequence":"additional","affiliation":[{"name":"School of Forest Resources and Conservation, University of Florida, Gainesville, FL 32611, USA"},{"name":"Gulf Coast Research Center, University of Florida, Plant City, FL 33563, USA"}]}],"member":"1968","published-online":{"date-parts":[[2018,3,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1017\/S1466046606060224","article-title":"Using unmanned aerial vehicles for rangelands: Current applications and future potentials","volume":"8","author":"Rango","year":"2006","journal-title":"Environ. Pract."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.isprsjprs.2014.02.013","article-title":"Unmanned aerial systems for photogrammetry and remote sensing: A review","volume":"92","author":"Colomina","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1080\/01431160601075582","article-title":"Object-based change detection using correlation image analysis and image segmentation","volume":"29","author":"Im","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1141","DOI":"10.1016\/j.rse.2010.01.002","article-title":"Synergistic use of QuickBird multispectral imagery and LIDAR data for object-based forest species classification","volume":"114","author":"Ke","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_5","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_6","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":"GISci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"564","DOI":"10.1080\/22797254.2017.1373602","article-title":"Object-based classification of wetland vegetation using very high-resolution unmanned air system imagery","volume":"50","author":"Liu","year":"2017","journal-title":"Eur. J. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1080\/15481603.2016.1148229","article-title":"Channel bar feature extraction for a mining-contaminated river using high-spatial multispectral remote-sensing imagery","volume":"53","author":"Wang","year":"2016","journal-title":"GISci. Remote Sens."},{"key":"ref_9","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_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":"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_12","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20136). Imagenet classification with deep convolutional neural networks. Proceedings of the Advances in Neural Information Processing Systems, Lake Tahoe, NV, USA."},{"key":"ref_13","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_14","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1109\/MSP.2012.2205597","article-title":"Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups","volume":"29","author":"Hinton","year":"2012","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1016\/j.neuroimage.2014.06.077","article-title":"Alzheimer\u2019s Disease Neuroimaging Initiative. Hierarchical feature representation and multimodal fusion with deep learning for AD\/MCI diagnosis","volume":"101","author":"Suk","year":"2014","journal-title":"NeuroImage"},{"key":"ref_16","unstructured":"Huval, B., Wang, T., Tandon, S., Kiske, J., Song, W., Pazhayampallil, J., Andriluka, M., Rajpurkar, P., Migimatsu, T., and Cheng-Yue, R. (arXiv, 2015). An empirical evaluation of deep learning on highway driving, arXiv."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1038\/nature24270","article-title":"Mastering the game of go without human knowledge","volume":"550","author":"Silver","year":"2017","journal-title":"Nature"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1038\/nature16961","article-title":"Mastering the game of Go with deep neural networks and tree search","volume":"529","author":"Silver","year":"2016","journal-title":"Nature"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.isprsjprs.2017.05.002","article-title":"Simultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks","volume":"130","author":"Alshehhi","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_20","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_21","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.isprsjprs.2016.09.001","article-title":"Semisupervised classification for hyperspectral image based on multi-decision labeling and deep feature learning","volume":"120","author":"Ma","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Makantasis, K., Karantzalos, K., Doulamis, A., and Doulamis, N. (2015, January 26\u201331). Deep supervised learning for hyperspectral data classification through convolutional neural networks. Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7326945"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Vetrivel, A., Gerke, M., Kerle, N., Nex, F., and Vosselman, G. (2017). Disaster damage detection through synergistic use of deep learning and 3D point cloud features derived from very high resolution oblique aerial images, and multiple-kernel-learning. ISPRS J. Photogramm. Remote Sens.","DOI":"10.1016\/j.isprsjprs.2017.03.001"},{"key":"ref_24","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":"GISci. 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":"GISci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Marcos, D., Volpi, M., and Tuia, D. (arXiv, 2016). Learning rotation invariant convolutional filters for texture classification, arXiv.","DOI":"10.1109\/ICPR.2016.7899932"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.isprsjprs.2016.01.004","article-title":"Learning multiscale and deep representations for classifying remotely sensed imagery","volume":"113","author":"Zhao","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Celikyilmaz, A., Sarikaya, R., Hakkani-Tur, D., Liu, X., Ramesh, N., and Tur, G. (2016, January 8\u201312). A New Pre-training Method for Training Deep Learning Models with Application to Spoken Language Understanding. Proceedings of the Interspeech 2016, San Francisco, CA, USA.","DOI":"10.21437\/Interspeech.2016-512"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","article-title":"A survey on transfer learning","volume":"22","author":"Pan","year":"2010","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Xie, M., Jean, N., Burke, M., Lobell, D., and Ermon, S. (arXiv, 2015). Transfer learning from deep features for remote sensing and poverty mapping, arXiv.","DOI":"10.1609\/aaai.v30i1.9906"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.rse.2013.12.014","article-title":"Evaluation of semi-empirical BRDF models inverted against multi-angle data from a digital airborne frame camera for enhancing forest type classification","volume":"151","author":"Koukal","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1016\/j.rse.2006.05.023","article-title":"Support vector machines for recognition of semi-arid vegetation types using MISR multi-angle imagery","volume":"107","author":"Su","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/0034-4257(95)00197-2","article-title":"Classification of ASAS multiangle and multispectral measurements using artificial neural networks","volume":"57","author":"Abuelgasim","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1155","DOI":"10.1109\/TGRS.2011.2165548","article-title":"Very high resolution multiangle urban classification analysis","volume":"50","author":"Longbotham","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.compbiomed.2017.03.017","article-title":"Joint Multiple Fully Connected Convolutional Neural Network with Extreme Learning Machine for Hepatocellular Carcinoma Nuclei Grading","volume":"84","author":"Li","year":"2017","journal-title":"Comput. Biol. Med."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.knosys.2017.01.023","article-title":"Small bowel motility assessment based on fully convolutional networks and long short-term memory","volume":"121","author":"Pei","year":"2017","journal-title":"Knowl.-Based Syst."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.cmpb.2017.02.013","article-title":"MSFCN-multiple supervised fully convolutional networks for the osteosarcoma segmentation of CT images","volume":"143","author":"Huang","year":"2017","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"758","DOI":"10.1016\/j.neucom.2016.07.009","article-title":"Traffic sign detection and recognition using fully convolutional network guided proposals","volume":"214","author":"Zhu","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_40","unstructured":"ISPRS (2017, October 08). 2D Semantic Labeling\u2014Vaihingen Data. Available online: http:\/\/www2.isprs.org\/commissions\/comm3\/wg4\/2d-sem-label-vaihingen.html."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Piramanayagam, S., Schwartzkopf, W., Koehler, F., and Saber, E. (2016, January 26\u201328). Classification of remote sensed images using random forests and deep learning framework. Proceedings of the SPIE Remote Sensing, Edinburgh, UK.","DOI":"10.1117\/12.2243169"},{"key":"ref_42","unstructured":"Sherrah, J. (arXiv, 2016). Fully convolutional networks for dense semantic labelling of high-resolution aerial imagery, arXiv."},{"key":"ref_43","unstructured":"Marmanis, D., Schindler, K., Wegner, J.D., Galliani, S., Datcu, M., and Stilla, U. (arXiv, 2016). Classification with an edge: Improving semantic image segmentation with boundary detection, arXiv."},{"key":"ref_44","unstructured":"Grasslands, L. (2017, November 01). Blue Head Ranch. Available online: https:\/\/www.grasslands-llc.com\/blue-head-florida."},{"key":"ref_45","unstructured":"Holm, L.G., Plucknett, D.L., Pancho, J.V., and Herberger, J.P. (1977). The World\u2019s Worst Weeds, University Press."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Rutchey, K., Schall, T., Doren, R., Atkinson, A., Ross, M., Jones, D., Madden, M., Vilchek, L., Bradley, K., and Snyder, J. (2006). Vegetation Classification for South Florida Natural Areas, US Geological Survey.","DOI":"10.3133\/ofr20061240"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1109\/JSTARS.2012.2184527","article-title":"Potential of multi-angular data derived from a digital aerial frame camera for forest classification","volume":"5","author":"Koukal","year":"2012","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Im, J., Quackenbush, L.J., Li, M., and Fang, F. (2014). Optimum Scale in Object-Based Image Analysis. Scale Issues Remote Sens., 197\u2013214.","DOI":"10.1002\/9781118801628.ch10"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"889","DOI":"10.1137\/S1052623400378742","article-title":"Analysis of generalized pattern searches","volume":"13","author":"Audet","year":"2002","journal-title":"SIAM J. Optim."},{"key":"ref_50","unstructured":"Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R.R. (arXiv, 2012). Improving neural networks by preventing co-adaptation of feature detectors, arXiv."},{"key":"ref_51","unstructured":"Nair, V., and Hinton, G.E. (2010, January 21\u201324). Rectified linear units improve restricted boltzmann machines. Proceedings of the 27th International Conference on Machine Learning (ICML-10), Haifa, Israel."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Bottou, L. (2010). Large-scale machine learning with stochastic gradient descent. Proceedings of COMPSTAT\u20192010, Springer.","DOI":"10.1007\/978-3-7908-2604-3_16"},{"key":"ref_53","unstructured":"(2012). eCognition\u00ae Developer 8.8 User Guide, Trimble Documentation."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Scholkopf, B., and Smola, A.J. (2001). Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, MIT Press.","DOI":"10.7551\/mitpress\/4175.001.0001"},{"key":"ref_55","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_56","unstructured":"Yegnanarayana, B. (2009). Artificial Neural Networks, PHI Learning Pvt. Ltd."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Kuusk, A. (1991). The hot spot effect in plant canopy reflectance. Photon-Vegetation Interactions, Springer.","DOI":"10.1007\/978-3-642-75389-3_5"},{"key":"ref_58","first-page":"139","article-title":"Object based Information Extraction from High Resolution Satellite Imagery using eCognition","volume":"11","author":"Gupta","year":"2014","journal-title":"Int. J. Comput. Sci. Issues (IJCSI)"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"799","DOI":"10.14358\/PERS.72.7.799","article-title":"Object-based detailed vegetation classification with airborne high spatial resolution remote sensing imagery","volume":"72","author":"Yu","year":"2006","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1109\/72.991427","article-title":"A comparison of methods for multiclass support vector machines","volume":"13","author":"Hsu","year":"2002","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Mahdavi, S., Salehi, B., Granger, J., Amani, M., Brisco, B., and Huang, W. (2017). Remote sensing for wetland classification: A comprehensive review. GISci. Remote Sens.","DOI":"10.1080\/15481603.2017.1419602"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"779","DOI":"10.1080\/15481603.2017.1331510","article-title":"Wetland classification in Newfoundland and Labrador using multi-source SAR and optical data integration","volume":"54","author":"Amani","year":"2017","journal-title":"GISci. Remote Sens."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1080\/07038992.2017.1346468","article-title":"Wetland Classification Using Multi-Source and Multi-Temporal Optical Remote Sensing Data in Newfoundland and Labrador, Canada","volume":"43","author":"Amani","year":"2017","journal-title":"Can. J. Remote Sens."},{"key":"ref_64","first-page":"56","article-title":"Combined use of LiDAR data and multispectral earth observation imagery for wetland habitat mapping","volume":"37","author":"Rapinel","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1016\/j.rse.2017.11.005","article-title":"Fisher Linear Discriminant Analysis of coherency matrix for wetland classification using PolSAR imagery","volume":"206","author":"Mahdianpari","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.rse.2017.03.005","article-title":"Monthly flooded area classification using low resolution SAR imagery in the Sudd wetland from 2007 to 2011","volume":"194","author":"Wilusz","year":"2017","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/3\/457\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T14:57:01Z","timestamp":1760194621000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/3\/457"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,3,14]]},"references-count":66,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2018,3]]}},"alternative-id":["rs10030457"],"URL":"https:\/\/doi.org\/10.3390\/rs10030457","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2018,3,14]]}}}