{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T04:41:50Z","timestamp":1769575310846,"version":"3.49.0"},"reference-count":36,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2017,7,27]],"date-time":"2017-07-27T00:00:00Z","timestamp":1501113600000},"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>Image segmentation is a crucial stage at the very beginning of many geographic object-based image analysis (GEOBIA) workflows. While segmentation quality is generally deemed of great importance, selecting adequate tuning parameters for a segmentation algorithm can be tedious and subjective. Procedures to automatically choose parameters of a segmentation algorithm are meant to make the process objective and reproducible. One of those approaches, and perhaps the most frequently used unsupervised parameter optimization method in the context of GEOBIA is called the objective function, also known as Global Score. Unfortunately, the method exhibits a hitherto widely neglected, yet severe source of instability, which makes quality rankings inconsistent. We demonstrate the issue in detail and propose a modification of the Global Score to mitigate the problem. This hopefully serves as a starting point to spark further development of the popular approach.<\/jats:p>","DOI":"10.3390\/rs9080769","type":"journal-article","created":{"date-parts":[[2017,7,27]],"date-time":"2017-07-27T11:28:40Z","timestamp":1501154920000},"page":"769","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["On the Objectivity of the Objective Function\u2014Problems with Unsupervised Segmentation Evaluation Based on Global Score and a Possible Remedy"],"prefix":"10.3390","volume":"9","author":[{"given":"Sebastian","family":"B\u00f6ck","sequence":"first","affiliation":[{"name":"Institute of Surveying, Remote Sensing and Land Information (IVFL), University of Natural Resources and Life Sciences, Vienna (BOKU), Peter Jordan Strasse 82, 1190 Vienna, Austria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6758-1207","authenticated-orcid":false,"given":"Markus","family":"Immitzer","sequence":"additional","affiliation":[{"name":"Institute of Surveying, Remote Sensing and Land Information (IVFL), University of Natural Resources and Life Sciences, Vienna (BOKU), Peter Jordan Strasse 82, 1190 Vienna, Austria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2169-8009","authenticated-orcid":false,"given":"Clement","family":"Atzberger","sequence":"additional","affiliation":[{"name":"Institute of Surveying, Remote Sensing and Land Information (IVFL), University of Natural Resources and Life Sciences, Vienna (BOKU), Peter Jordan Strasse 82, 1190 Vienna, Austria"}]}],"member":"1968","published-online":{"date-parts":[[2017,7,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhang, Y. (2006). An overview of image and video segmentation in the last 40 Years. Advances in Image and Video Segmentation, Idea Group Inc.","DOI":"10.4018\/978-1-59140-753-9.ch001"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1987","DOI":"10.1016\/j.patcog.2006.05.015","article-title":"Evaluation for uncertain image classification and segmentation","volume":"39","author":"Martin","year":"2006","journal-title":"Pattern Recognit."},{"key":"ref_3","unstructured":"Zhang, H., Cholleti, S., Goldman, S.A., and Fritts, J.E. (2006, January 17\u201322). Meta-evaluation of image segmentation using machine learning. Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision Pattern Recognit, New York, NY, USA."},{"key":"ref_4","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":"Rusanen","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_5","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_6","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_7","doi-asserted-by":"crossref","first-page":"2492","DOI":"10.1109\/JSTARS.2013.2253089","article-title":"Optimization of object-based image analysis with Random Forests for land cover mapping","volume":"6","author":"Stefanski","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"617","DOI":"10.1080\/01431160902894475","article-title":"Segmentation performance evaluation for object-based remotely sensed image analysis","volume":"31","author":"Corcoran","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1109\/TPAMI.1985.4767640","article-title":"Dynamic measurement of computer generated image segmentations","volume":"7","author":"Levine","year":"1985","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/ASP\/2006\/96306","article-title":"Unsupervised performance evaluation of image segmentation","volume":"2006","author":"Chabrier","year":"2006","journal-title":"EURASIP J. Adv. Signal Process."},{"key":"ref_11","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 Recognit."},{"key":"ref_12","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. Remote Sens."},{"key":"ref_13","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_14","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1093\/biomet\/37.1-2.17","article-title":"Notes on continuous stochastic phenomena","volume":"37","author":"Moran","year":"1950","journal-title":"Biometrika"},{"key":"ref_15","unstructured":"Goodchild, M.F. (1986). Spatial Autocorrelation. Concepts and Techniques in Modern Geography 47, Geo Books."},{"key":"ref_16","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_17","first-page":"351","article-title":"Optimal scale in a hierarchical segmentation method for satellite images","volume":"Volume 8537","author":"Costumero","year":"2014","journal-title":"Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)"},{"key":"ref_18","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_19","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. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"4928","DOI":"10.1109\/TGRS.2011.2151866","article-title":"Segment optimization and data-driven thresholding for knowledge-based landslide detection by object-based image analysis","volume":"49","author":"Martha","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1061","DOI":"10.1016\/j.mcm.2010.11.036","article-title":"Modified ALV for selecting the optimal spatial resolution and its scale effect on image classification accuracy","volume":"54","author":"Ming","year":"2011","journal-title":"Math. Comput. Model."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"813","DOI":"10.1109\/LGRS.2011.2182604","article-title":"Semivariogram-based spatial bandwidth selection for remote sensing image segmentation with mean-shift algorithm","volume":"9","author":"Ming","year":"2012","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_23","first-page":"54","article-title":"Semi-automated stand delineation in Mediterranean Pinus sylvestris plantations through segmentation of LiDAR data: The influence of pulse density","volume":"56","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_24","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_25","first-page":"358","article-title":"A Technique for Optimal selection of segmentation scale parameters for object-oriented classification of urban scenes","volume":"2","author":"Ikokou","year":"2013","journal-title":"S. Afr. J. Geomat."},{"key":"ref_26","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_27","doi-asserted-by":"crossref","first-page":"6914","DOI":"10.1080\/01431161.2014.960617","article-title":"Optimal segmentation of a high-resolution remote-sensing image guided by area and boundary","volume":"35","author":"Chen","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1166\/sl.2012.1860","article-title":"The optimal segmentation scale identification using multispectral WorldView-2 images","volume":"10","author":"Yue","year":"2012","journal-title":"Sens. Lett."},{"key":"ref_29","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_30","doi-asserted-by":"crossref","unstructured":"Mohan Vamsee, A., Kamala, P., Martha, T.R., Vinod Kumar, K., Jai Sankar, G., and Amminedu, E. (2017). A tool assessing optimal multi-scale image segmentation. J. Indian Soc. Remote Sens.","DOI":"10.1007\/s12524-017-0685-7"},{"key":"ref_31","first-page":"12","article-title":"Multiresolution Segmentation: An optimization approach for high quality multi-scale image segmentation","volume":"58","author":"Baatz","year":"2000","journal-title":"J. Photogramm. Remote Sens."},{"key":"ref_32","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_33","unstructured":"Kim, M., Madden, M., and Warner, T. (2008). Estimation of optimal image object size for the segmentation of forest stands with multispectral IKONOS imagery. Object-Based Image Analysis: Spatial Concepts for Knowledge-Driven Remote Sensing Applications, Springer."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1111\/j.1538-4632.1984.tb00797.x","article-title":"On extreme values of Moran\u2019s I and Geary\u2019s c","volume":"16","author":"Sprenger","year":"1984","journal-title":"Geogr. Anal."},{"key":"ref_35","unstructured":"Reis, M.S., Pantalepo, E., de Siqueira Sant\u2019Anna, S.J., and Dutra, L.V. (2014, January 13\u201318). Proposal of a weighted index for segmentation evaluation. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Quebec City, QC, Canada."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/0031-3203(81)90028-5","article-title":"A survey on image segmentation","volume":"13","author":"Fu","year":"1981","journal-title":"Pattern Recognit."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/8\/769\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:44:11Z","timestamp":1760208251000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/8\/769"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,7,27]]},"references-count":36,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2017,8]]}},"alternative-id":["rs9080769"],"URL":"https:\/\/doi.org\/10.3390\/rs9080769","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,7,27]]}}}