{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T19:59:36Z","timestamp":1774641576298,"version":"3.50.1"},"reference-count":75,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2018,9,9]],"date-time":"2018-09-09T00:00:00Z","timestamp":1536451200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002749","name":"Federaal Wetenschapsbeleid","doi-asserted-by":"publisher","award":["SR\/00\/337"],"award-info":[{"award-number":["SR\/00\/337"]}],"id":[{"id":"10.13039\/501100002749","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>To classify Very-High-Resolution (VHR) imagery, Geographic Object Based Image Analysis (GEOBIA) is the most popular method used to produce high quality Land-Use\/Land-Cover maps. A crucial step in GEOBIA is the appropriate parametrization of the segmentation algorithm prior to the classification. However, little effort has been made to automatically optimize GEOBIA algorithms in an unsupervised and spatially meaningful manner. So far, most Unsupervised Segmentation Parameter Optimization (USPO) techniques, assume spatial stationarity for the whole study area extent. This can be questionable, particularly for applications in geographically large and heterogeneous urban areas. In this study, we employed a novel framework named Spatially Partitioned Unsupervised Segmentation Parameter Optimization (SPUSPO), which optimizes segmentation parameters locally rather than globally, for the Sub-Saharan African city of Ouagadougou, Burkina Faso, using WorldView-3 imagery (607 km2). The results showed that there exists significant spatial variation in the optimal segmentation parameters suggested by USPO across the whole scene, which follows landscape patterns\u2014mainly of the various built-up and vegetation types. The most appropriate automatic spatial partitioning method from the investigated techniques, was an edge-detection cutline algorithm, which achieved higher classification accuracy than a global optimization, better predicted built-up regions, and did not suffer from edge effects. The overall classification accuracy using SPUSPO was 90.5%, whilst the accuracy from undertaking a traditional USPO approach was 89.5%. The differences between them were statistically significant (p &lt; 0.05) based on a McNemar\u2019s test of similarity. Our methods were validated further by employing a segmentation goodness metric, Area Fit Index (AFI)on building objects across Ouagadougou, which suggested that a global USPO was more over-segmented than our local approach. The mean AFI values for SPUSPO and USPO were 0.28 and 0.36, respectively. Finally, the processing was carried out using the open-source software GRASS GIS, due to its efficiency in raster-based applications.<\/jats:p>","DOI":"10.3390\/rs10091440","type":"journal-article","created":{"date-parts":[[2018,9,10]],"date-time":"2018-09-10T10:28:57Z","timestamp":1536575337000},"page":"1440","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":42,"title":["Scale Matters: Spatially Partitioned Unsupervised Segmentation Parameter Optimization for Large and Heterogeneous Satellite Images"],"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\/0000-0002-9837-1832","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\/0000-0002-2870-4515","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\/0000-0001-6226-1968","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":[[2018,9,9]]},"reference":[{"key":"ref_1","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_2","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1186\/s12942-016-0051-y","article-title":"Mapping intra-urban malaria risk using high resolution satellite imagery: A case study of Dar es Salaam","volume":"15","author":"Kabaria","year":"2016","journal-title":"Int. J. Health Geogr."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Linard, C., Gilbert, M., Snow, R.W., Noor, A.M., and Tatem, A.J. (2012). Population distribution, settlement patterns and accessibility across Africa in 2010. PLoS ONE., 7.","DOI":"10.1371\/journal.pone.0031743"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Taubenbock, H., Wurm, M., Setiadi, N., Gebert, N., Roth, A., Strunz, G., Birkmann, J., and Dech, S. (2009, January 20\u201322). Integrating remote sensing and social science. Proceedings of the IEEE Joint Urban Remote Sensing Event, Shanghai, China.","DOI":"10.1109\/URS.2009.5137506"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/S0022-1694(02)00142-7","article-title":"Land-use impacts on storm-runoff generation: Scenarios of land-use change and simulation of hydrological response in a meso-scale catchment in SW-Germany","volume":"267","author":"Niehoff","year":"2002","journal-title":"J. Hydrol."},{"key":"ref_6","first-page":"27","article-title":"Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms","volume":"12","author":"Otukei","year":"2010","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Manakos, I., and Braun, M. (2014). Land Use and Land Cover Mapping in Europe, Springer Nature. [3rd ed.].","DOI":"10.1007\/978-94-007-7969-3"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Iizuka, K., Johnson, B.A., Onishi, A., Magcale-Macandog, D.B., Endo, I., and Bragais, M. (2017). Modeling Future Urban Sprawl and Landscape Change in the Laguna de Bay Area, Philippines. Land, 6.","DOI":"10.3390\/land6020026"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.isprsjprs.2013.09.014","article-title":"Geographic Object-Based Image Analysis\u2014Towards a new paradigm","volume":"87","author":"Blaschke","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Chen, G., Weng, Q., Hay, G.J., and He, Y. (2018). Geographic Object-based Image Analysis (GEOBIA): Emerging trends and future opportunities. GIScience Remote Sens., 55.","DOI":"10.1080\/15481603.2018.1426092"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Gu, H., Li, H., Yan, L., Liu, Z., Blaschke, T., and Soergel, U. (2017). An object-based semantic classification method for high resolution remote sensing imagery using ontology. Remote Sens., 9.","DOI":"10.3390\/rs9040329"},{"key":"ref_12","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_13","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_14","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1109\/LGRS.2018.2803259","article-title":"Very high resolution object-based land use-land cover urban classification using extreme gradient boosting","volume":"15","author":"Georganos","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Ma, L., Li, M., Blaschke, T., Ma, X., Tiede, D., Cheng, L., Chen, Z., and Chen, D. (2016). Object-based change detection in urban areas: The effects of segmentation strategy, scale, and feature space on unsupervised methods. Remote Sens., 8.","DOI":"10.3390\/rs8090761"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Srivastava, M., Arora, M.K., and Raman, B. (2015, January 13\u201315). Selection of critical segmentation-A prerequisite for Object based image classification. Proceedings of the 2015 National Conference on Recent Advances in Electronics & Computer Engineering (RAECE), Roorkee, India.","DOI":"10.1109\/RAECE.2015.7510243"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"890","DOI":"10.1109\/JSTARS.2011.2157659","article-title":"Detecting an optimal scale parameter in object-oriented classification","volume":"4","author":"Lowe","year":"2011","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_18","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-Inform."},{"key":"ref_19","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_20","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_21","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1109\/LGRS.2012.2194694","article-title":"An energy-driven total variation model for segmentation and classification of high spatial resolution remote-sensing imagery","volume":"10","author":"Zhang","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_22","unstructured":"Baatz, M., and Schape, A. (2017, December 20). Multiresolution Segmentation: An Optimization Approach for High Quality Multi-Scale Image Segmentation. Available online: https:\/\/www.semanticscholar.org\/paper\/Multiresolution-Segmentation-an-optimization-appro-Baatz-Sch%C3%A4pe\/364cc1ff514a2e11d21a101dc072575e5487d17e."},{"key":"ref_23","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_24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/15481603.2016.1215769","article-title":"A comparative study of the segmentation of weighted aggregation and multiresolution segmentation","volume":"53","author":"Du","year":"2016","journal-title":"GISci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"83696","DOI":"10.1117\/1.JRS.8.083696","article-title":"Investigating the impact of spatial and spectral resolution of satellite images on segmentation quality","volume":"8","author":"Mesner","year":"2014","journal-title":"J. Appl. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"6061","DOI":"10.1109\/TGRS.2016.2580643","article-title":"Multiscale and multifeature normalized cut segmentation for high spatial resolution remote sensing imagery","volume":"54","author":"Zhong","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","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_28","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1016\/j.cviu.2007.08.003","article-title":"Image segmentation evaluation: A survey of unsupervised methods. Comput. Vis","volume":"110","author":"Zhang","year":"2008","journal-title":"Image Underst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"441","DOI":"10.5589\/m03-006","article-title":"Preliminary evaluation of ecognition object-based software for cut block delineation and feature extraction","volume":"29","author":"Flanders","year":"2003","journal-title":"Can. J. Remote Sens."},{"key":"ref_30","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_31","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_32","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_33","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_34","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_35","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_36","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_37","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":"Dragut","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_38","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_39","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. Remote Sens."},{"key":"ref_40","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 urban obia classification. Proceedings of the GEOBIA 2016: Solutions and Synergies, Enschede, The Netherlands.","DOI":"10.3990\/2.367"},{"key":"ref_41","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_42","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_43","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_44","doi-asserted-by":"crossref","unstructured":"Grippa, T., Georganos, S., Vanhuysse, S.G., Lennert, M., and Wolff, E. (2017). A local segmentation parameter optimization approach for mapping heterogeneous urban environments using VHR imagery. Remote Sens. Technol. Appl. Urban Environ. II, 10431.","DOI":"10.1117\/12.2278422"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Remote sensing of environment google earth engine: Planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"234","DOI":"10.2307\/143141","article-title":"A computer movie simulating urban growth in the detroit region","volume":"46","author":"Tobler","year":"1970","journal-title":"Econ. Geogr."},{"key":"ref_47","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_48","doi-asserted-by":"crossref","unstructured":"Grippa, T., Georganos, S., Zarougui, S., Bognounou, P., Diboulo, E., Forget, Y., Lennert, M., Vanhuysse, S., Mboga, N., and Wolff, E. (2018). Mapping urban land use at street block level using open street map, Remote Sensing Data, and Spatial Metrics. ISPRS Int. J. Geo-Inform., 7.","DOI":"10.3390\/ijgi7070246"},{"key":"ref_49","unstructured":"United Nations (2014). World Urbanization Prospects: The 2014 Revision, Highlights."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.rse.2018.03.022","article-title":"Mapping patterns of urban development in Ouagadougou, Burkina Faso, using machine learning regression modeling with bi-seasonal Landsat time series","volume":"210","author":"Schug","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_51","unstructured":"Momsen, E., Metz, M., and GRASS Development TEAM (2018, August 01). Module i.segment 2015. Available online: https:\/\/grass.osgeo.org\/grass75\/manuals\/i.segment.html."},{"key":"ref_52","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_53","doi-asserted-by":"crossref","unstructured":"Georganos, S., Lennert, M., Grippa, T., Vanhuysse, S., Johnson, B., and Wolff, E. (2018). Normalization in unsupervised segmentation parameter optimization: A solution based on local regression trend analysis. Remote Sens., 10.","DOI":"10.3390\/rs10020222"},{"key":"ref_54","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."},{"key":"ref_55","unstructured":"Lennert, M., and GRASS Development TEAM (2018, August 01). Module i.segment.uspo 2017. Available online: https:\/\/grass.osgeo.org\/grass74\/manuals\/addons\/i.segment.uspo.html."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"K\u00f6rting, T.S., Castejon, E.F., and Fonseca, L.M.G. (2013, January 18\u201321). The divide and segment method for parallel image segmentation. Proceedings of the International Conference on Advanced Concepts for Intelligent Vision Systems, Antwerp, Belgium.","DOI":"10.1007\/978-3-319-02895-8_45"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Soares, A.R., K\u00f6rting, T.S., and Fonseca, L.M.G. (2016). Improvements of the divide and segment method for parallel image segmentation. Rev. Bras. Cartogr., 68.","DOI":"10.14393\/rbcv68n6-44486"},{"key":"ref_58","unstructured":"Satnik, D., and GRASS Development TEAM (2018, August 01). Module i.zc 2016. Available online: https:\/\/grass.osgeo.org\/grass70\/manuals\/i.zc.html."},{"key":"ref_59","unstructured":"Lennert, M., and GRASS Development TEAM (2018, August 01). Module i.cutlines 2018. Available online: https:\/\/grass.osgeo.org\/grass74\/manuals\/addons\/i.cutlines.html."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1111\/j.1472-4642.2007.00344.x","article-title":"Non-stationarity and local approaches to modelling the distributions of wildlife","volume":"13","author":"Osborne","year":"2007","journal-title":"Divers. Distrib."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (arXiv, 2016). XGBoost: Reliable large-scale tree boosting system, arXiv.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.eswa.2017.02.017","article-title":"A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring","volume":"78","author":"Xia","year":"2017","journal-title":"Expert Syst. Appl."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"19","DOI":"10.32614\/RJ-2015-018","article-title":"VSURF: An R Package for variable selection using random forests","volume":"7","author":"Genuer","year":"2015","journal-title":"R J."},{"key":"ref_64","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., 221\u2013242.","DOI":"10.1080\/15481603.2017.1408892"},{"key":"ref_65","unstructured":"Georganos, S., Grippa, T., Lennert, M., Johnson, B.A., Vanhuysse, S., and Wolff, E. (2018, August 31). SPUSPO: Spatially Partitioned Unsupervised Segmentation Parameter Optimization for Efficiently Segmenting Large Heterogeneous Areas. Available online: https:\/\/zenodo.org\/record\/1341116#.W5S1oVKtZS0."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/0034-4257(87)90015-0","article-title":"The factor of scale in remote sensing","volume":"21","author":"Woodcock","year":"1987","journal-title":"Remote Sens. Environ."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"554","DOI":"10.1111\/j.0002-9092.2004.600_2.x","article-title":"Geographically weighted regression: The analysis of spatially varying relationships","volume":"86","author":"Fotheringham","year":"2004","journal-title":"Am. J. Agric. Econom."},{"key":"ref_68","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_69","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.isprsjprs.2018.03.006","article-title":"Deep convolutional neural network training enrichment using multi-view object-based analysis of Unmanned Aerial systems imagery for wetlands classification","volume":"139","author":"Liu","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.isprsjprs.2017.11.009","article-title":"Classification with an edge: Improving semantic image segmentation with boundary detection","volume":"135","author":"Marmanis","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.apgeog.2013.07.009","article-title":"Modelling spatial patterns of urban growth in Africa","volume":"44","author":"Linard","year":"2013","journal-title":"Appl. Geogr."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"1970","DOI":"10.1109\/JSTARS.2016.2519843","article-title":"Determining the Relationship between Census Data and Spatial Features Derived From High-Resolution Imagery in Accra, Ghana","volume":"9","author":"Sandborn","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.isprsjprs.2015.04.010","article-title":"Scale parameter selection by spatial statistics for GeOBIA: Using mean-shift based multi-scale segmentation as an example","volume":"106","author":"Ming","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"3660","DOI":"10.1109\/TGRS.2012.2185054","article-title":"Hyperspectral image denoising employing a spectral-spatial adaptive total variation model","volume":"50","author":"Yuan","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Gu, H., Han, Y., Yang, Y., Li, H., Liu, Z., Soergel, U., Blaschke, T., and Cui, S. (2018). An efficient parallel multi-scale segmentation method for remote sensing imagery. 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