{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T15:13:47Z","timestamp":1777389227428,"version":"3.51.4"},"reference-count":64,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2018,10,31]],"date-time":"2018-10-31T00:00:00Z","timestamp":1540944000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Using object-based image analysis (OBIA) techniques for land use-land cover classification (LULC) has become an area of interest due to the availability of high-resolution data and segmentation methods. Multi-resolution segmentation in particular, statistically seen as the most used algorithm, is able to produce non-identical segmentations depending on the required parameters. The total effect of segmentation parameters on the classification accuracy of high-resolution imagery is still an open question, though some studies were implemented to define the optimum segmentation parameters. However, recent studies have not properly considered the parameters and their consequences on LULC accuracy. The main objective of this study is to assess OBIA segmentation and classification accuracy according to the segmentation parameters using different overlap ratios during image object sampling for a predetermined scale. With this aim, we analyzed and compared (a) high-resolution color-infrared aerial images of a newly-developed urban area including different land use types; (b) combinations of multi-resolution segmentation with different shape, color, compactness, bands, and band-weights; and (c) accuracies of classifications based on varied segmentations. The results of various parameters in the study showed an explicit correlation between segmentation accuracies and classification accuracies. The effect of changes in segmentation parameters using different sample selection methods for five main LULC types was studied. Specifically, moderate shape and compactness values provided more consistency than lower and higher values; also, band weighting demonstrated substantial results due to the chosen bands. Differences in the variable importance of the classifications and changes in LULC maps were also explained.<\/jats:p>","DOI":"10.3390\/ijgi7110424","type":"journal-article","created":{"date-parts":[[2018,10,31]],"date-time":"2018-10-31T11:55:41Z","timestamp":1540986941000},"page":"424","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Assessment of Segmentation Parameters for Object-Based Land Cover Classification Using Color-Infrared Imagery"],"prefix":"10.3390","volume":"7","author":[{"given":"Ozgun","family":"Akcay","sequence":"first","affiliation":[{"name":"Faculty of Engineering, Department of Geomatics Engineering, Canakkale Onsekiz Mart University, 17100 Canakkale, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Emin Ozgur","family":"Avsar","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Department of Geomatics Engineering, Canakkale Onsekiz Mart University, 17100 Canakkale, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Melis","family":"Inalpulat","sequence":"additional","affiliation":[{"name":"Faculty of Architecture and Design, Department of Urban and Regional Planning, Land Use and Climate Change Laboratory, Canakkale Onsekiz Mart University, 17100 Canakkale, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Levent","family":"Genc","sequence":"additional","affiliation":[{"name":"Faculty of Architecture and Design, Department of Urban and Regional Planning, Land Use and Climate Change Laboratory, Canakkale Onsekiz Mart University, 17100 Canakkale, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahmet","family":"Cam","sequence":"additional","affiliation":[{"name":"Photogrammetry Department, General Directorate of Mapping, 06100 Dikimevi Ankara, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,10,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"608","DOI":"10.1016\/j.isprsjprs.2011.04.001","article-title":"Land use land cover classification over a large area in Iran based on single date analysis of satellite imagery","volume":"66","author":"Saadat","year":"2011","journal-title":"ISPRS J. Photogramm."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1080\/10106049.2013.768300","article-title":"A comparative assessment between object and pixel-based classification approaches for land use\/land cover mapping using SPOT 5 imagery","volume":"29","author":"Tehrany","year":"2013","journal-title":"Geocarto Int."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.rse.2014.05.001","article-title":"Multi-temporal mapping of seagrass cover, species and biomass: A semi-automated object based image analysis approach","volume":"150","author":"Roelfsema","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2017.04.009","article-title":"Evaluating pixel and object based image classification techniques for mapping plant invasions from UAV derived aerial imagery: Harrisia pomanensis as a case study","volume":"129","author":"Mafanya","year":"2017","journal-title":"ISPRS J. Photogramm."},{"key":"ref_5","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_6","first-page":"49","article-title":"Enhanced change detection index for disaster response, recovery assessment and monitoring of accessibility and open spaces (camp sites)","volume":"57","author":"So","year":"2017","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_7","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."},{"key":"ref_8","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."},{"key":"ref_9","unstructured":"Strobl, J., Blaschke, T., and Griesbner, G. (2000). Multiresolution segmentation: An optimization approach for high quality multi-scale image segmentation. Angewandte Geographische Informations-Verarbeitung XII, Wichmann Verlag."},{"key":"ref_10","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."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1109\/34.1000236","article-title":"Mean shift: A robust approach toward feature space analysis","volume":"24","author":"Commaniciu","year":"2002","journal-title":"IEEE Trans. Pattern Anal."},{"key":"ref_12","first-page":"952","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. Pattern Anal."},{"key":"ref_13","unstructured":"Jin, X. (2007). Segmentation-Based Image Processing System. (8,260,048), U.S. Patent."},{"key":"ref_14","first-page":"187","article-title":"The watershed transform: Definitions, algorithms, and parallelization strategies","volume":"41","author":"Roerdink","year":"2000","journal-title":"Fund. Inform."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.isprsjprs.2017.02.008","article-title":"Hierarchical graph-based segmentation for extracting road networks from high-resolution satellite images","volume":"126","author":"Alshehhi","year":"2017","journal-title":"ISPRS J. Photogramm."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1109\/LGRS.2008.919622","article-title":"Improvement of image segmentation accuracy based on multiscale optimization procedure","volume":"5","author":"Esch","year":"2008","journal-title":"IEEE Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.isprsjprs.2014.04.008","article-title":"A multi-band approach to unsupervised scale parameter selection for multi-scale image segmentation","volume":"94","author":"Yang","year":"2014","journal-title":"ISPRS J. Photogramm."},{"key":"ref_18","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."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2976","DOI":"10.1016\/j.rse.2011.05.007","article-title":"Segmentation optimization and stratified object-based analysis for semi-automated geomorphological mapping","volume":"115","author":"Anders","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_20","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."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Momeni, R., Aplin, P., and Boyd, D.S. (2016). Mapping complex urban land cover from spaceborne imagery: The influence of spatial resolution, spectral band set and classification approach. Remote Sens., 8.","DOI":"10.3390\/rs8020088"},{"key":"ref_22","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_23","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_24","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.isprsjprs.2015.01.009","article-title":"Segmentation quality evaluation using region-based precision and recall measures for remote sensing images","volume":"102","author":"Zhang","year":"2015","journal-title":"ISPRS J. Photogramm."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/j.isprsjprs.2014.12.015","article-title":"A discrepancy measure for segmentation evaluation from the perspective of object recognition","volume":"101","author":"Yang","year":"2015","journal-title":"ISPRS J. Photogramm."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1016\/j.isprsjprs.2017.06.003","article-title":"Local and global evaluation for remote sensing image segmentation","volume":"130","author":"Su","year":"2017","journal-title":"ISPRS J. Photogramm."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Lindquist, E.J., and D\u2019Annunzio, R. (2016). Assessing global forest land-use change by object-based image analysis. Remote Sens., 8.","DOI":"10.3390\/rs8080678"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zou, X., Zhao, G., Li, J., Yang, Y., and Fang, Y. (2016, January 12\u201319). Object Based Image Analysis Combining High Spatial Resolution Imagery and Laser Point Clouds for Urban Land Cover. Proceedings of the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXIII ISPRS Congress, Prague, Czech Republic.","DOI":"10.5194\/isprsarchives-XLI-B3-733-2016"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"4702","DOI":"10.1080\/01431161.2015.1088674","article-title":"Mapping land cover and land use from object-based classification: An example from a complex agricultural landscape","volume":"36","author":"Goodin","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1016\/j.compenvurbsys.2007.10.001","article-title":"Classification of the wildland\u2014Urban interface: A comparison of pixel- and object-based classifications using high-resolution aerial photography","volume":"32","author":"Cleve","year":"2008","journal-title":"Comput. Environ. Urban"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1109\/TGE.1976.294460","article-title":"Classification of multispectral image data by extraction and classification of homogenous objects","volume":"14","author":"Kettig","year":"1976","journal-title":"IEEE Trans. Geosci. Remote"},{"key":"ref_32","first-page":"611","article-title":"Remote sensing of urban suburban infrastructure and socio-economic attributes","volume":"65","author":"Jensen","year":"1999","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_33","first-page":"12","article-title":"Per-parcel land use classification in urban areas using a rule-based technique","volume":"6","author":"Bauer","year":"2001","journal-title":"GeoBIT"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1940","DOI":"10.1109\/TGRS.2003.814625","article-title":"Classification and feature extraction for remote sensing images from urban areas base on morphological transformations","volume":"41","author":"Benediktsson","year":"2003","journal-title":"IEEE Trans. Geosci. Remote"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1016\/S0924-2716(02)00161-2","article-title":"Automated analysis of ultra high resolution remote sensing data for biotope type mapping: New possibilities and challenges","volume":"57","author":"Ehlers","year":"2003","journal-title":"ISPRS J. Photogramm."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1907","DOI":"10.1109\/TGRS.2003.815238","article-title":"Spectral resolution requirements for mapping urban areas","volume":"41","author":"Herold","year":"2003","journal-title":"IEEE Trans. Geosci. Remote"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1016\/j.rse.2010.12.017","article-title":"Per-pixel vs. object-based of urban land cover extraction using high resolution imagery","volume":"115","author":"Myint","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Georganos, S., Grippa, T., Lennert, M., Vanhuysse, S., Johnson, B., and Wolff, E. (2018). Scale Matters: Spatially Partitioned Unsupervised Segmentation Parameter Optimization for Large and Heterogeneous Satellite Images. Remote Sens., 10.","DOI":"10.3390\/rs10091440"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.rse.2011.11.020","article-title":"A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery","volume":"118","author":"Duro","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.isprsjprs.2013.03.006","article-title":"Change detection from remotely sensed images: From pixel-based to object-based approaches","volume":"80","author":"Hussain","year":"2013","journal-title":"ISPRS J. Photogramm."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Congalton, R.G., and Green, K. (2008). Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, CRC Press.","DOI":"10.1201\/9781420055139"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Lein, J.K. (2012). Object-based analysis. Environmental Sensing: Analytical Techniques for Earth Observation, Springer.","DOI":"10.1007\/978-1-4614-0143-8"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Congalton, R. (2009). Accuracy and error analysis of global and local maps: Lessons learned and future considerations. Remote Sensing of Global Croplands for Food Security, CRC Press.","DOI":"10.1201\/9781420090109.sec7"},{"key":"ref_44","unstructured":"Jensen, J.R. (2004). Digital Image Processing: A Remote Sensing Perspective, Prentice Hall."},{"key":"ref_45","unstructured":"(2018, September 17). Breiman and Cutler\u2019s Random Forests for Classification and Regression. Available online: https:\/\/cran.r-project.org\/web\/packages\/randomForest\/randomForest.pdf."},{"key":"ref_46","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_47","doi-asserted-by":"crossref","first-page":"1335","DOI":"10.1016\/0031-3203(95)00169-7","article-title":"A survey on evaluation methods for image segmentation","volume":"29","author":"Zhang","year":"1996","journal-title":"Pattern Recogn."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.isprsjprs.2012.01.007","article-title":"Discrepancy measures for selecting optimal combination of parameter values in object-based image analysis","volume":"68","author":"Liu","year":"2012","journal-title":"ISPRS J. Photogramm."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"228","DOI":"10.3390\/ijgi1030228","article-title":"Satellite image pansharpening using a hybrid approach for object-based image analysis","volume":"1","author":"Johnson","year":"2012","journal-title":"ISPRS Int. J. Geo-Inf."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2518","DOI":"10.1109\/TGRS.2002.805072","article-title":"Existential uncertainty of spatial objects segmented from satellite sensor imagery","volume":"40","author":"Lucieer","year":"2002","journal-title":"IEEE Trans. Geosci. Remote"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Blaschke, T., Lang, S., and Hay, G.J. (2008). Assessing image segmentation quality-concepts, methods and application. Object-Based Image Analysis, Springer.","DOI":"10.1007\/978-3-540-77058-9"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1080\/01431161.2010.532173","article-title":"Automated segmentation of vegetation structure units in a Mediterranean landscape","volume":"33","author":"Kent","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/S0924-2716(00)00019-8","article-title":"Location similarity of regions","volume":"55","author":"Winter","year":"2000","journal-title":"ISPRS J. Photogramm."},{"key":"ref_54","unstructured":"Weidner, U. (2008, January 3\u201311). Contribution to the assessment of segmentation quality for remote sensing applications. Proceedings of the International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXVII-B7, XXI ISPRS Congress, Beijing, China."},{"key":"ref_55","unstructured":"Whiteside, T.G., Maier, S.W., and Boggs, G.S. (2012, January 7\u20139). Site-specific area-based validation of classified objects. Proceedings of the 4th GEOBIA, Rio de Janeiro, Brazil."},{"key":"ref_56","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."},{"key":"ref_57","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_58","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."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Ma, L., Fu, T., Blaschke, T., Li, M., Tiede, D., Zhou, Z., Ma, X., and Chen, D. (2017). Evaluation of feature selection methods for object-based land cover mapping of unmanned aerial vehicle imagery using random forest and support vector machine classifiers. ISPRS Int. J. Geo-Inf., 6.","DOI":"10.3390\/ijgi6020051"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/S0034-4257(97)00083-7","article-title":"Selecting and interpreting measures of thematic classification accuracy","volume":"62","author":"Stehman","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1023\/A:1013964023376","article-title":"Estimating generalization error on two-class data sets using out-of-bag estimates","volume":"48","author":"Bylander","year":"2002","journal-title":"Mach. Learn."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"2249","DOI":"10.1016\/j.csda.2007.08.015","article-title":"Empirical characterization of random forest variable importance measures","volume":"52","author":"Archer","year":"2008","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1471-2105-9-307","article-title":"Conditional variable importance for random forests","volume":"9","author":"Strobl","year":"2008","journal-title":"BMC Bioinform."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"14","DOI":"10.32614\/RJ-2009-013","article-title":"Party on! A new, conditional variable-importance measure for Random Forests available in the party package","volume":"1\u20132","author":"Strobl","year":"2009","journal-title":"R J."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/7\/11\/424\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:27:11Z","timestamp":1760196431000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/7\/11\/424"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,10,31]]},"references-count":64,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2018,11]]}},"alternative-id":["ijgi7110424"],"URL":"https:\/\/doi.org\/10.3390\/ijgi7110424","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,10,31]]}}}