{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,26]],"date-time":"2026-04-26T08:31:57Z","timestamp":1777192317564,"version":"3.51.4"},"reference-count":63,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2019,5,24]],"date-time":"2019-05-24T00:00:00Z","timestamp":1558656000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["PTDC\/AAG-MAA\/3699\/2014"],"award-info":[{"award-number":["PTDC\/AAG-MAA\/3699\/2014"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This paper investigates the reliability of free and open-source algorithms used in the geographical object-based image classification (GEOBIA) of very high resolution (VHR) imagery surveyed by unmanned aerial vehicles (UAVs). UAV surveys were carried out in a cork oak woodland located in central Portugal at two different periods of the year (spring and summer). Segmentation and classification algorithms were implemented in the Orfeo ToolBox (OTB) configured in the QGIS environment for the GEOBIA process. Image segmentation was carried out using the Large-Scale Mean-Shift (LSMS) algorithm, while classification was performed by the means of two supervised classifiers, random forest (RF) and support vector machines (SVM), both of which are based on a machine learning approach. The original, informative content of the surveyed imagery, consisting of three radiometric bands (red, green, and NIR), was combined to obtain the normalized difference vegetation index (NDVI) and the digital surface model (DSM). The adopted methodology resulted in a classification with higher accuracy that is suitable for a structurally complex Mediterranean forest ecosystem such as cork oak woodlands, which are characterized by the presence of shrubs and herbs in the understory as well as tree shadows. To improve segmentation, which significantly affects the subsequent classification phase, several tests were performed using different values of the range radius and minimum region size parameters. Moreover, the consistent selection of training polygons proved to be critical to improving the results of both the RF and SVM classifiers. For both spring and summer imagery, the validation of the obtained results shows a very high accuracy level for both the SVM and RF classifiers, with kappa coefficient values ranging from 0.928 to 0.973 for RF and from 0.847 to 0.935 for SVM. Furthermore, the land cover class with the highest accuracy for both classifiers and for both flights was cork oak, which occupies the largest part of the study area. This study shows the reliability of fixed-wing UAV imagery for forest monitoring. The study also evidences the importance of planning UAV flights at solar noon to significantly reduce the shadows of trees in the obtained imagery, which is critical for classifying open forest ecosystems such as cork oak woodlands.<\/jats:p>","DOI":"10.3390\/rs11101238","type":"journal-article","created":{"date-parts":[[2019,5,24]],"date-time":"2019-05-24T11:20:46Z","timestamp":1558696846000},"page":"1238","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":118,"title":["Object-Based Land Cover Classification of Cork Oak Woodlands using UAV Imagery and Orfeo ToolBox"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4740-6468","authenticated-orcid":false,"given":"Giandomenico","family":"De Luca","sequence":"first","affiliation":[{"name":"Dipartimento di Agraria, Universit\u00e0 degli Studi Mediterranea di Reggio Calabria, Localit\u00e0 Feo di Vito, I-89122 Reggio Calabria, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5201-9836","authenticated-orcid":false,"given":"Jo\u00e3o M.","family":"N. Silva","sequence":"additional","affiliation":[{"name":"Forest Research Centre, School of Agriculture, University of Lisbon, Tapada da Ajuda, 1349-017 Lisbon, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9118-193X","authenticated-orcid":false,"given":"Sofia","family":"Cerasoli","sequence":"additional","affiliation":[{"name":"Forest Research Centre, School of Agriculture, University of Lisbon, Tapada da Ajuda, 1349-017 Lisbon, Portugal"}]},{"given":"Jo\u00e3o","family":"Ara\u00fajo","sequence":"additional","affiliation":[{"name":"Spin.Works S.A., Avenida da Igreja 42, 6\u00ba, 1700-239 Lisboa, Portugal"}]},{"given":"Jos\u00e9","family":"Campos","sequence":"additional","affiliation":[{"name":"Spin.Works S.A., Avenida da Igreja 42, 6\u00ba, 1700-239 Lisboa, Portugal"}]},{"given":"Salvatore","family":"Di Fazio","sequence":"additional","affiliation":[{"name":"Dipartimento di Agraria, Universit\u00e0 degli Studi Mediterranea di Reggio Calabria, Localit\u00e0 Feo di Vito, I-89122 Reggio Calabria, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0388-0256","authenticated-orcid":false,"given":"Giuseppe","family":"Modica","sequence":"additional","affiliation":[{"name":"Dipartimento di Agraria, Universit\u00e0 degli Studi Mediterranea di Reggio Calabria, Localit\u00e0 Feo di Vito, I-89122 Reggio Calabria, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2019,5,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"205","DOI":"10.4081\/jae.2016.571","article-title":"Using Landsat 8 imagery in detecting cork oak (Quercus suber L.) woodlands: A case study in Calabria (Italy)","volume":"47","author":"Modica","year":"2016","journal-title":"J. Agric. Eng."},{"key":"ref_2","unstructured":"San-Miguel-Ayanz, J., de Rigo, D., Caudullo, G., Houston Durrant, T., and Mauri, A. (2016). European Forest Tree Species, Publication Office of the European Union."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2349","DOI":"10.1080\/01431161.2017.1297548","article-title":"UAS, sensors, and data processing in agroforestry: A review towards practical applications","volume":"38","author":"Vanko","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10661-015-4996-2","article-title":"Classification of riparian forest species and health condition using multi-temporal and hyperspatial imagery from unmanned aerial system","volume":"188","author":"Michez","year":"2016","journal-title":"Environ. Monit. Assess."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"B\u00f6hler, J.E., Schaepman, M.E., and Kneub\u00fchler, M. (2018). Crop classification in a heterogeneous arable landscape using uncalibrated UAV data. Remote Sens., 10.","DOI":"10.3390\/rs10081282"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"De Castro, A.I., Torres-S\u00e1nchez, J., Pe\u00f1a, J.M., Jim\u00e9nez-Brenes, F.M., Csillik, O., and L\u00f3pez-Granados, F. (2018). An automatic random forest-OBIA algorithm for early weed mapping between and within crop rows using UAV imagery. Remote Sens., 10.","DOI":"10.3390\/rs10020285"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"783","DOI":"10.1139\/cjfr-2014-0347","article-title":"A survey on technologies for automatic forest fire monitoring, detection, and fighting using unmanned aerial vehicles and remote sensing techniques","volume":"45","author":"Yuan","year":"2015","journal-title":"Can. J. For. Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"594","DOI":"10.3390\/f6030594","article-title":"Analysis of unmanned aerial system-based CIR images in forestry-a new perspective to monitor pest infestation levels","volume":"6","author":"Lehmann","year":"2015","journal-title":"Forests"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"4026","DOI":"10.3390\/rs70404026","article-title":"Evaluating multispectral images and vegetation indices for precision farming applications from UAV images","volume":"7","author":"Candiago","year":"2015","journal-title":"Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"922","DOI":"10.3390\/f4040922","article-title":"A photogrammetric workflow for the creation of a forest canopy height model from small unmanned aerial system imagery","volume":"4","author":"Lisein","year":"2013","journal-title":"Forests"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Guerra-Hern\u00e1ndez, J., Gonz\u00e1lez-Ferreiro, E., Sarmento, A., Silva, J., Nunes, A., Correia, A.C., Fontes, L., Tom\u00e9, M., and D\u00edaz-Varela, R. (2016). Using high resolution UAV imagery to estimate tree variables in Pinus pinea plantation in Portugal. For. Syst., 25.","DOI":"10.5424\/fs\/2016252-08895"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"251","DOI":"10.5721\/EuJRS20144716","article-title":"Use of unmanned aerial systems for multispectral survey and tree classification: A test in a park area of northern Italy","volume":"47","author":"Gini","year":"2014","journal-title":"Eur. J. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Haest, B., Borre, J., Vanden Spanhove, T., Thoonen, G., Delalieux, S., Kooistra, L., M\u00fccher, C.A., Paelinckx, D., Scheunders, P., and Kempeneers, P. (2017). Habitat mapping and quality assessment of NATURA 2000 heathland using airborne imaging spectroscopy. Remote Sens., 9.","DOI":"10.3390\/rs9030266"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"5246","DOI":"10.1080\/01431161.2017.1402387","article-title":"Assessing the status of forest regeneration using digital aerial photogrammetry and unmanned aerial systems","volume":"39","author":"Goodbody","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Van Iersel, W., Straatsma, M., Middelkoop, H., Addink, E., van Iersel, W., Straatsma, M., Middelkoop, H., and Addink, E. (2018). Multitemporal Classification of River Floodplain Vegetation Using Time Series of UAV Images. Remote Sens., 10.","DOI":"10.3390\/rs10071144"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"791","DOI":"10.1007\/s11676-015-0088-y","article-title":"Drone remote sensing for forestry research and practices","volume":"26","author":"Tang","year":"2015","journal-title":"J. For. Res."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"P\u00e1dua, L., Hru\u0161ka, J., Bessa, J., Ad\u00e3o, T., Martins, L.M., Gon\u00e7alves, J.A., Peres, E., Sousa, A.M.R., Castro, J.P., and Sousa, J.J. (2018). Multi-temporal analysis of forestry and coastal environments using UASs. Remote Sens., 10.","DOI":"10.3390\/rs10010024"},{"key":"ref_18","first-page":"1","article-title":"Forestry applications of UAVs in Europe: A review","volume":"38","author":"Torresan","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.compag.2015.03.019","article-title":"An automatic object-based method for optimal thresholding in UAV images: Application for vegetation detection in herbaceous crops","volume":"114","year":"2015","journal-title":"Comput. Electron. Agric."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"016011","DOI":"10.1117\/1.JRS.10.016011","article-title":"Comparison of performance of object-based image analysis techniques available in open source software (Spring and Orfeo Toolbox\/Monteverdi) considering very high spatial resolution data","volume":"10","author":"Teodoro","year":"2016","journal-title":"J. Appl. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Torres-S\u00e1nchez, J., L\u00f3pez-Granados, F., de Castro, A.I., and Pe\u00f1a-Barrag\u00e1n, J.M. (2013). Configuration and Specifications of an Unmanned Aerial Vehicle (UAV) for Early Site Specific Weed Management. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0058210"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"71","DOI":"10.5558\/tfc2017-012","article-title":"Unmanned aerial systems for precision forest inventory purposes: A review and case study","volume":"93","author":"Goodbody","year":"2017","journal-title":"For. Chron."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1505","DOI":"10.1080\/01431160903571791","article-title":"Comparison of pixel-and object-based classification in land cover change mapping","volume":"32","author":"Robertson","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1080\/01431161003743173","article-title":"Assessing object-based classification: Advantages and limitations","volume":"1","author":"Liu","year":"2010","journal-title":"Remote Sens. Lett."},{"key":"ref_25","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_26","doi-asserted-by":"crossref","unstructured":"Phiri, D., and Morgenroth, J. (2017). Developments in Landsat Land Cover Classification Methods: A Review. Remote Sens., 9.","DOI":"10.3390\/rs9090967"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.isprsjprs.2013.09.014","article-title":"Geographic Object-Based Image Analysis\u2013Towards a new paradigm","volume":"87","author":"Blaschke","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_28","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_29","doi-asserted-by":"crossref","unstructured":"Blaschke, T., Lang, S., and Hay, G.J. (2008). Geographic Object-Based Image Analysis (GEOBIA): A new name for a new discipline. Object-Based Image Analysis. Spatial Concepts for Knowledge-Driven Remote Sensing Applications, Springer.","DOI":"10.1007\/978-3-540-77058-9"},{"key":"ref_30","first-page":"121","article-title":"Special issue: Geographic object-based image analysis (GEOBIA)","volume":"76","author":"Hay","year":"2010","journal-title":"Photogramm. Eng. Remote Sensing"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"5236","DOI":"10.1080\/01431161.2017.1363442","article-title":"Deciduous tree species classification using object-based analysis and machine learning with unmanned aerial vehicle multispectral data","volume":"39","author":"Franklin","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2037","DOI":"10.1080\/01431161.2017.1294781","article-title":"Hierarchical land cover and vegetation classification using multispectral data acquired from an unmanned aerial vehicle","volume":"38","author":"Ahmed","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_33","unstructured":"CNES OTB Development Team (2018). Software Guide, CNES."},{"key":"ref_34","unstructured":"OTB Development Team (2018). OTB CookBook Documentation, CNES."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"813","DOI":"10.1007\/s00484-015-1075-x","article-title":"Temporal dynamics of spectral bioindicators evidence biological and ecological differences among functional types in a cork oak open woodland","volume":"60","author":"Cerasoli","year":"2016","journal-title":"Int. J. Biometeorol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.agrformet.2015.01.017","article-title":"Effects of an extremely dry winter on net ecosystem carbon exchange and tree phenology at a cork oak woodland","volume":"204","author":"Correia","year":"2015","journal-title":"Agric. For. Meteorol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3695","DOI":"10.5194\/gmd-8-3695-2015","article-title":"A simple two-dimensional parameterisation for Flux Footprint Prediction (FFP)","volume":"8","author":"Kljun","year":"2015","journal-title":"Geosci. Model Dev."},{"key":"ref_38","unstructured":"Soares, P., Firmino, P., Tom\u00e9, M., Campagnolo, M., Oliveira, J., Oliveira, B., and Ara\u00fajo, J. (2015, January 29\u201330). A utiliza\u00e7\u00e3o de Ve\u00edculos A\u00e9reos N\u00e3o Tripulados no invent\u00e1rio florestal\u2013o caso do montado de sobro. Proceedings of the VII Confer\u00eancia Nac. Cartogr. e Geod., Lisbon, Portugal."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Cresson, R., Grizonnet, M., and Michel, J. (2018). Orfeo ToolBox Applications. QGIS and Generic Tools, John Wiley & Sons, Inc.","DOI":"10.1002\/9781119457091.ch5"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1186\/s40965-017-0031-6","article-title":"Orfeo ToolBox: Open source processing of remote sensing images","volume":"2","author":"Grizonnet","year":"2017","journal-title":"Open Geospatial Data Softw. Stand."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Immitzer, M., Vuolo, F., and Atzberger, C. (2016). First experience with Sentinel-2 data for crop and tree species classifications in central Europe. Remote Sens., 8.","DOI":"10.3390\/rs8030166"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"952","DOI":"10.1109\/TGRS.2014.2330857","article-title":"Stable Mean-Shift Algorithm and Its Application to the Segmentation of Arbitrarily Large Remote Sensing Images","volume":"53","author":"Michel","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_43","first-page":"87","article-title":"A systematic comparison of different object-based classification techniques using high spatial resolution imagery in agricultural environments","volume":"49","author":"Li","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1016\/j.rse.2017.11.024","article-title":"Supervised methods of image segmentation accuracy assessment in land cover mapping","volume":"205","author":"Costa","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.isprsjprs.2014.07.002","article-title":"Comparing supervised and unsupervised multiresolution segmentation approaches for extracting buildings from very high resolution imagery","volume":"96","author":"Belgiu","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Congalton, R.G., and Green, K. (2009). Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, Second CRC Press Taylor & Francis Group.","DOI":"10.1201\/9781420055139"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-Vector Networks Editor","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_48","unstructured":"Vapnik, V. (1998). Statistical Learning Theory, Wiley and sons."},{"key":"ref_49","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_50","doi-asserted-by":"crossref","first-page":"3440","DOI":"10.1080\/01431161.2014.903435","article-title":"Land-use\/cover classification in a heterogeneous coastal landscape using RapidEye imagery: Evaluating the performance of random forest and support vector machines classifiers","volume":"35","author":"Adam","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1080\/22797254.2017.1417745","article-title":"Estimating defoliation of Scots pine stands using machine learning methods and vegetation indices of Sentinel-2","volume":"51","author":"Bednarz","year":"2018","journal-title":"Eur. J. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Wessel, M., Brandmeier, M., Tiede, D., Wessel, M., Brandmeier, M., and Tiede, D. (2018). Evaluation of Different Machine Learning Algorithms for Scalable Classification of Tree Types and Tree Species Based on Sentinel-2 Data. Remote Sens., 10.","DOI":"10.3390\/rs10091419"},{"key":"ref_53","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_54","doi-asserted-by":"crossref","first-page":"2783","DOI":"10.1890\/07-0539.1","article-title":"Random Forests For Classification In Ecology","volume":"88","author":"Cutler","year":"2007","journal-title":"Ecology"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"3274","DOI":"10.1080\/01431161.2017.1292072","article-title":"Comparing six pixel-wise classifiers for tropical rural land cover mapping using four forms of fully polarimetric SAR data","volume":"38","author":"Trisasongko","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"672","DOI":"10.1109\/36.297984","article-title":"Detection of Forests Using Mid-IR Reflectance: An Application for Aerosol Studies","volume":"32","author":"Kaufman","year":"1994","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_57","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_58","doi-asserted-by":"crossref","first-page":"289","DOI":"10.14358\/PERS.76.3.289","article-title":"Accuracy Assessment Measures for Object-based Image Segmentation Goodness","volume":"76","author":"Clinton","year":"2010","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_59","first-page":"151","article-title":"UAV for mapping shrubland vegetation: Does fusion of spectral and vertical information derived from a single sensor increase the classification accuracy?","volume":"75","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/j.ecolind.2017.11.068","article-title":"Sub-metric analisis of vegetation structure in bog-heathland mosaics using very high resolution rpas imagery","volume":"89","year":"2018","journal-title":"Ecol. Indic."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1080\/14498596.2010.487850","article-title":"Enhanced evaluation of image segmentation results","volume":"55","author":"Marpu","year":"2010","journal-title":"J. Spat. Sci."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"3747","DOI":"10.1080\/01431161003777189","article-title":"Optimal region growing segmentation and its effect on classification accuracy","volume":"32","author":"Gao","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_63","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."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/10\/1238\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:54:46Z","timestamp":1760187286000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/10\/1238"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,5,24]]},"references-count":63,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2019,5]]}},"alternative-id":["rs11101238"],"URL":"https:\/\/doi.org\/10.3390\/rs11101238","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,5,24]]}}}