{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T10:52:51Z","timestamp":1770288771846,"version":"3.49.0"},"reference-count":48,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2018,2,1]],"date-time":"2018-02-01T00:00:00Z","timestamp":1517443200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"BELSPO (Belgian Science Policy Office)","award":["STEREO III programm\u2014project REACT [SR\/00\/337]"],"award-info":[{"award-number":["STEREO III programm\u2014project REACT [SR\/00\/337]"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In object-based image analysis (OBIA), the appropriate parametrization of segmentation algorithms is crucial for obtaining satisfactory image classification results. One of the ways this can be done is by unsupervised segmentation parameter optimization (USPO). A popular USPO method does this through the optimization of a \u201cglobal score\u201d (GS), which minimizes intrasegment heterogeneity and maximizes intersegment heterogeneity. However, the calculated GS values are sensitive to the minimum and maximum ranges of the candidate segmentations. Previous research proposed the use of fixed minimum\/maximum threshold values for the intrasegment\/intersegment heterogeneity measures to deal with the sensitivity of user-defined ranges, but the performance of this approach has not been investigated in detail. In the context of a remote sensing very-high-resolution urban application, we show the limitations of the fixed threshold approach, both in a theoretical and applied manner, and instead propose a novel solution to identify the range of candidate segmentations using local regression trend analysis. We found that the proposed approach showed significant improvements over the use of fixed minimum\/maximum values, is less subjective than user-defined threshold values and, thus, can be of merit for a fully automated procedure and big data applications.<\/jats:p>","DOI":"10.3390\/rs10020222","type":"journal-article","created":{"date-parts":[[2018,2,2]],"date-time":"2018-02-02T04:20:50Z","timestamp":1517545250000},"page":"222","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Normalization in Unsupervised Segmentation Parameter Optimization: A Solution Based on Local Regression Trend Analysis"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0001-2058","authenticated-orcid":false,"given":"Stefanos","family":"Georganos","sequence":"first","affiliation":[{"name":"Department of Geosciences, Environment &amp; Society, Universit\u00e9 libre de Bruxelles (ULB), 1050 Bruxelles, Belgium"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2870-4515","authenticated-orcid":false,"given":"Moritz","family":"Lennert","sequence":"additional","affiliation":[{"name":"Department of Geosciences, Environment &amp; Society, Universit\u00e9 libre de Bruxelles (ULB), 1050 Bruxelles, Belgium"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9837-1832","authenticated-orcid":false,"given":"Tais","family":"Grippa","sequence":"additional","affiliation":[{"name":"Department of Geosciences, Environment &amp; Society, Universit\u00e9 libre de Bruxelles (ULB), 1050 Bruxelles, Belgium"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6226-1968","authenticated-orcid":false,"given":"Sabine","family":"Vanhuysse","sequence":"additional","affiliation":[{"name":"Department of Geosciences, Environment &amp; Society, Universit\u00e9 libre de Bruxelles (ULB), 1050 Bruxelles, Belgium"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1911-3585","authenticated-orcid":false,"given":"Brian","family":"Johnson","sequence":"additional","affiliation":[{"name":"Natural Resources and Ecosystem Services Area, Institute for Global Environmental Strategies, 2108-11 Kamiyamaguchi, Hayama, Kanagawa 240-0115, Japan"}]},{"given":"El\u00e9onore","family":"Wolff","sequence":"additional","affiliation":[{"name":"Department of Geosciences, Environment &amp; Society, Universit\u00e9 libre de Bruxelles (ULB), 1050 Bruxelles, Belgium"}]}],"member":"1968","published-online":{"date-parts":[[2018,2,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.isprsjprs.2003.09.007","article-title":"Object-based classification of remote sensing data for change detection","volume":"58","author":"Walter","year":"2004","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.isprsjprs.2013.09.014","article-title":"Geographic Object-Based Image Analysis-Towards a new paradigm","volume":"87","author":"Blaschke","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1035","DOI":"10.1080\/01431160500297956","article-title":"Urban land cover multi-level region-based classification of VHR data by selecting relevant features","volume":"27","author":"Carleer","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_4","first-page":"884","article-title":"Comparing object-based and pixel-based classifications for mapping savannas","volume":"13","author":"Whiteside","year":"2011","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"6261","DOI":"10.1007\/s10661-012-3022-1","article-title":"Mapping trees outside forests using high-resolution aerial imagery: A comparison of pixel- and object-based classification approaches","volume":"185","author":"Meneguzzo","year":"2013","journal-title":"Environ. Monit. Assess."},{"key":"ref_6","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_7","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_8","doi-asserted-by":"crossref","first-page":"1720","DOI":"10.1109\/LGRS.2015.2421736","article-title":"A New Approach to Segmentation of Multispectral Remote Sensing Images Based on MRF","volume":"12","author":"Baumgartner","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_9","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_10","doi-asserted-by":"crossref","unstructured":"Kohli, D., Crommelinck, S., Bennett, R., Koeva, M., and Lemmen, C. (2017, January 4). Object-based image analysis for cadastral mapping using satellite images. Proceedings of the International Society for Optics and Photonics, Image Signal Processing Remote Sensing XXIII, Warsaw, Poland.","DOI":"10.1117\/12.2280254"},{"key":"ref_11","unstructured":"Li, F., Wong, A., and Clausi, D.A. (2014, January 13\u201318). Comparison of unsupervised segmentation methods for surficial materials mapping in Nunavut, Canada using RADARSAT-2 polarimetric, Landsat-7, and DEM data. Proceedings of the 2014 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Quebec City, QC, Canada."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1936","DOI":"10.1109\/JSTARS.2015.2424457","article-title":"Unsupervised quantification of under-and over-segmentation for object-based remote sensing image analysis","volume":"8","author":"Gancarski","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1109\/TGRS.2017.2745507","article-title":"Optimal Segmentation of High-Resolution Remote Sensing Image by Combining Superpixels With the Minimum Spanning Tree","volume":"56","author":"Wang","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","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_15","doi-asserted-by":"crossref","unstructured":"Aguilar, M.A., Novelli, A., Nemamoui, A., Aguilar, F.J., Lorca, A.G., and Gonz\u00e1lez-Yebra, \u00d3. (2017, January 21\u201323). Optimizing Multiresolution Segmentation for Extracting Plastic Greenhouses from WorldView-3 Imagery. Proceedings of the International Conference on Intelligent Interactive Multimedia Systems and Services, Vilamoura, Portugal.","DOI":"10.1007\/978-3-319-59480-4_4"},{"key":"ref_16","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_17","doi-asserted-by":"crossref","first-page":"8603","DOI":"10.1080\/01431161.2013.845318","article-title":"What makes segmentation good? A case study in boreal forest habitat mapping","volume":"34","author":"Rasanen","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_18","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_19","doi-asserted-by":"crossref","first-page":"3035","DOI":"10.1080\/01431160600617194","article-title":"Parameter selection for region-growing image segmentation algorithms using spatial autocorrelation","volume":"27","author":"Espindola","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_20","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_21","first-page":"217","article-title":"Unsupervised performance evaluation of image segmentation","volume":"2006","author":"Chabrier","year":"2006","journal-title":"EURASIP J. Appl. Signal Process."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Sublime, J., Troya-Galvis, A., and Puissant, A. (2017). Multi-scale analysis of very high resolution satellite images using unsupervised techniques. Remote Sens., 9.","DOI":"10.3390\/rs9050495"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Gao, H., Tang, Y., Jing, L., Li, H., and Ding, H. (2017). A Novel Unsupervised Segmentation Quality Evaluation Method for Remote Sensing Images. Sensors, 17.","DOI":"10.3390\/s17102427"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Georganos, S., Grippa, T., Vanhuysse, S., Lennert, M., Shimoni, M., Kalogirou, S., and Wolff, E. (2017). Less is more: optimizing classification performance through feature selection in a very-high-resolution remote sensing object-based urban application. GISci. Remote Sens.","DOI":"10.1080\/15481603.2017.1408892"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"35016","DOI":"10.1117\/1.JRS.11.035016","article-title":"Classification of semiurban landscapes from very high-resolution satellite images using a regionalized multiscale segmentation approach","volume":"11","author":"Kavzoglu","year":"2017","journal-title":"J. Appl. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Grippa, T., Lennert, M., Beaumont, B., Vanhuysse, S., Stephenne, N., and Wolff, E. (2016, January 14\u201316). An open-source semi-automated processing chain for uban OBIA classification. Proceedings of the GEOBIA 2016 Conference Mach-2 Machine Learning & Automation II, Enschede, The Netherlands.","DOI":"10.3990\/2.367"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"937","DOI":"10.1080\/10106049.2015.1004131","article-title":"A local approach to optimize the scale parameter in multiresolution segmentation for multispectral imagery","volume":"30","year":"2015","journal-title":"Geocarto Int."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1007\/s12524-017-0685-7","article-title":"A Tool Assessing Optimal Multi-Scale Image Segmentation","volume":"46","author":"Vamsee","year":"2017","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1016\/j.isprsjprs.2011.02.006","article-title":"Unsupervised image segmentation evaluation and refinement using a multi-scale approach","volume":"66","author":"Johnson","year":"2011","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1007\/s12517-016-2683-4","article-title":"An unsupervised multi-scale segmentation method based on automated parameterization","volume":"9","author":"Wang","year":"2016","journal-title":"Arab. J. Geosci."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"B\u00f6ck, S., Immitzer, M., and Atzberger, C. (2017). On the Objectivity of the Objective Function\u2014Problems with Unsupervised Segmentation Evaluation Based on Global Score and a Possible Remedy. Remote Sens., 9.","DOI":"10.3390\/rs9080769"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.envsoft.2011.11.014","article-title":"GRASS GIS: A multi-purpose open source GIS","volume":"31","author":"Neteler","year":"2012","journal-title":"Environ. Model. Softw."},{"key":"ref_33","unstructured":"Momsen, E., Metz, M., and GRASS Development TEAM (2017, August 16). Module i.segment 2015. Available online: https:\/\/grass.osgeo.org\/grass75\/manuals\/i.segment.html."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Grippa, T., Lennert, M., Beaumont, B., Vanhuysse, S., Stephenne, N., and Wolff, E. (2017). An Open-Source Semi-Automated Processing Chain for Urban Object-Based Classification. Remote Sens., 9.","DOI":"10.3390\/rs9040358"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.1538-4632.1972.tb00475.x","article-title":"Testing for spatial autocorrelation among regression residuals","volume":"4","author":"Cliff","year":"1972","journal-title":"Geogr. Anal."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1080\/17421770701576921","article-title":"A spatial modelling framework for income estimation","volume":"2","author":"Kalogirou","year":"2007","journal-title":"Spat. Econ. Anal."},{"key":"ref_37","first-page":"61801","article-title":"GeoDaTM 0.9 user\u2019s guide","volume":"51","author":"Anselin","year":"2003","journal-title":"Urbana"},{"key":"ref_38","unstructured":"Baatz, M., and Schape, A. (2017, December 20). Multiresolution Segmentation: An Optimization Approach for High Quality Multi-Scale Image Segmentation. 2000. Available online: https:\/\/www.semanticscholar.org\/paper\/Multiresolution-Segmentation-an-optimization-appro-Baatz-Sch%C3%A4pe\/364cc1ff514a2e11d21a101dc072575e5487d17e."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.rse.2009.08.014","article-title":"Detecting trend and seasonal changes in satellite image time series","volume":"114","author":"Verbesselt","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1232","DOI":"10.1109\/TGRS.2009.2029570","article-title":"A novel protocol for accuracy assessment in classification of very high resolution images","volume":"48","author":"Persello","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"8685","DOI":"10.1080\/01431161.2013.845319","article-title":"A framework for the geometric accuracy assessment of classified objects","volume":"34","author":"Birger","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1016\/j.rse.2017.11.024","article-title":"Remote Sensing of Environment Supervised methods of image segmentation accuracy assessment in land cover mapping","volume":"205","author":"Costa","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_43","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 Sens."},{"key":"ref_44","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_45","doi-asserted-by":"crossref","first-page":"14482","DOI":"10.3390\/rs71114482","article-title":"Self-guided segmentation and classification of multi-temporal Landsat 8 images for crop type mapping in Southeastern Brazil","volume":"7","author":"Schultz","year":"2015","journal-title":"Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Grybas, H., Melendy, L., and Congalton, R.G. (2017). A comparison of unsupervised segmentation parameter optimization approaches using moderate- and high-resolution imagery. GISci. Remote Sens., 54.","DOI":"10.1080\/15481603.2017.1287238"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Grippa, T., Georganos, S., Lennert, M., Vanhuysse, S., and Wolff, E. (2017, January 4). A local segmentation parameter optimization approach for mapping heterogeneous urban environments using VHR imagery. Proceedings of the SPIE Remote Sensing Technologies and Applications in Urban Environments II, Warsaw, Poland.","DOI":"10.1117\/12.2278422"},{"key":"ref_48","unstructured":"Georganos, S., Grippa, T., Lennert, M., Vanhuysse, S.G., and Wolff, E. (2017, January 28\u201330). SPUSPO: Spatially Partitioned Unsupervised Segmentation Parameter Optimization for Efficiently Segmenting Large Heteregeneous Areas. Proceedings of the 2017 Conference on Big Data from Space (BiDS\u201917), Toulouse, France."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/2\/222\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T14:53:28Z","timestamp":1760194408000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/2\/222"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,2,1]]},"references-count":48,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2018,2]]}},"alternative-id":["rs10020222"],"URL":"https:\/\/doi.org\/10.3390\/rs10020222","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,2,1]]}}}