{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:27:16Z","timestamp":1760243236328,"version":"build-2065373602"},"reference-count":58,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2014,4,28]],"date-time":"2014-04-28T00:00:00Z","timestamp":1398643200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Sample supervised image analysis, in particular sample supervised segment generation, shows promise as a methodological avenue applicable within Geographic Object-Based Image Analysis (GEOBIA). Segmentation is acknowledged as a constituent component within typically expansive image analysis processes. A general extension to the basic formulation of an empirical discrepancy measure directed segmentation algorithm parameter tuning approach is proposed. An expanded search landscape is defined, consisting not only of the segmentation algorithm parameters, but also of low-level, parameterized image processing functions. Such higher dimensional search landscapes potentially allow for achieving better segmentation accuracies. The proposed method is tested with a range of low-level image transformation functions and two segmentation algorithms. The general effectiveness of such an approach is demonstrated compared to a variant only optimising segmentation algorithm parameters. Further, it is shown that the resultant search landscapes obtained from combining mid- and low-level image processing parameter domains, in our problem contexts, are sufficiently complex to warrant the use of population based stochastic search methods. Interdependencies of these two parameter domains are also demonstrated, necessitating simultaneous optimization.<\/jats:p>","DOI":"10.3390\/rs6053791","type":"journal-article","created":{"date-parts":[[2014,4,28]],"date-time":"2014-04-28T11:40:09Z","timestamp":1398685209000},"page":"3791-3821","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Data Transformation Functions for Expanded Search Spaces in Geographic Sample Supervised Segment Generation"],"prefix":"10.3390","volume":"6","author":[{"given":"Christoff","family":"Fourie","sequence":"first","affiliation":[{"name":"German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), 82234 Oberpfaffenhofen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Elisabeth","family":"Schoepfer","sequence":"additional","affiliation":[{"name":"German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), 82234 Oberpfaffenhofen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2014,4,28]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"Introduction to the GEOBIA 2010 special issue: From pixels to geographic objects in remote sensing image analysis","volume":"15","author":"Addink","year":"2012","journal-title":"Int. J. Appl. Earth Observ. Geoinf"},{"key":"ref_2","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_3","doi-asserted-by":"crossref","unstructured":"Blaschke, T., Lang, S., and Hay, G.J. (2008). Object-Based Image Analysis: Spatial Concepts for Knowledge-Driven Remote Sensing Applications, Springer.","DOI":"10.1007\/978-3-540-77058-9"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.isprsjprs.2013.09.014","article-title":"Geographic object-based image\u2014Towards a new paradigm","volume":"87","author":"Blaschke","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3687","DOI":"10.1080\/01431160310001654383","article-title":"Spatial variation in land cover and choice of spatial resolution for remote sensing","volume":"25","author":"Atkinson","year":"2004","journal-title":"Int. J. Remote Sens"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1016\/j.jag.2005.06.005","article-title":"An automated object-based approach for the multiscale image segmentation of forest scenes","volume":"7","author":"Hay","year":"2005","journal-title":"Int. J. Appl. Earth Observ. Geoinfor"},{"key":"ref_7","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_8","doi-asserted-by":"crossref","unstructured":"Blaschke, T., Lang, S., and Hay, G.J. (2008). Object-Based Image Analysis: Spatial Concepts for Knowledge-Driven Remote Sensing Applications, Springer.","DOI":"10.1007\/978-3-540-77058-9"},{"key":"ref_9","unstructured":"L\u00fcbker, T., and Schaab, G. (2010). A work-flow design for large-area multilevel GEOBIA: Integrating statistical measures and expert knowledge. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci, Available online: http:\/\/www.isprs.org\/proceedings\/XXXVIII\/4-C7\/pdf\/luebkerT.pdf."},{"key":"ref_10","first-page":"12","article-title":"Multiresolution Segmentation: An Optimization Approach for High Quality Multi-Scale Image Segmentation","volume":"12","author":"Strobl","year":"2000","journal-title":"Angewandte Geographische Informationsverarbeitung"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"409","DOI":"10.14358\/PERS.74.4.409","article-title":"Size-constrained region merging (scrm): An automated delineation tool for assisted photointerpretation","volume":"74","author":"Castilla","year":"2008","journal-title":"Photogramm. Eng. Remote Sens"},{"key":"ref_12","first-page":"5","article-title":"Object-based mapping and object-relationship modeling for land use classes and habitats","volume":"10","author":"Lang","year":"2006","journal-title":"Photogramm. Fernerkund. Geoinf"},{"key":"ref_13","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_14","doi-asserted-by":"crossref","first-page":"1543","DOI":"10.1109\/21.478444","article-title":"Adaptive image segmentation using a genetic algorithm","volume":"25","author":"Bhanu","year":"1995","journal-title":"IEEE Trans. Syst. Man Cybern"},{"key":"ref_15","unstructured":"Feitosa, R.Q., Ferreira, R.S., Almeida, C.M., Camargo, F.F., and Costa, G.A.O.P. (2010). Similarity metrics for genetic adaptation of segmentation parameters. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci, Available online: http:\/\/www.isprs.org\/proceedings\/XXXVIII\/4-C7\/pdf\/Feitosa_150.pdf."},{"key":"ref_16","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_17","doi-asserted-by":"crossref","first-page":"1223","DOI":"10.1016\/j.imavis.2008.09.008","article-title":"An evaluation metric for image segmentation of multiple objects","volume":"27","author":"Polak","year":"2009","journal-title":"Image Vis. Comput"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Bartz-Beielstein, T. (2006). Experimental Research in Evolutionary Computation, Springer.","DOI":"10.1145\/1274000.1274102"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Cagnoni, S. (2008, January 10\u201312). Evolutionary Computer Vision: A Taxonomic Tutorial. Barcelona, Spain.","DOI":"10.1109\/HIS.2008.168"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Riolo, R., Vladislavleva, E., Ritchie, M.D., and Moore, J.H. (2013). Genetic Programming Theory and Practice X, Springer.","DOI":"10.1007\/978-1-4614-6846-2"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1016\/S0262-8856(98)00151-6","article-title":"Automatic acquisition of hierarchical mathematical morphology procedures by genetic algorithms","volume":"17","author":"Yoda","year":"1999","journal-title":"Image Vis. Comput"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ebner, M. (2009, January 15\u201317). A Real-Time Evolutionary Object Recognition System. T\u00fcbingen, Germany.","DOI":"10.1007\/978-3-642-01181-8_23"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Rosin, P., and Herv\u00e1s, J. (2001, January 13\u201314). Image Thresholding for Landslide Detection by Genetic Programming. Trento, Italy.","DOI":"10.1142\/9789812777249_0005"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Wang, J., and Tan, Y. A. (2011, January 12\u201315). Novel Genetic Programming Algorithm for Designing Morphological Image Analysis Method. Chongqing, China.","DOI":"10.1007\/978-3-642-21515-5_65"},{"key":"ref_25","unstructured":"Feitosa, R.Q., Costa, G.A.O.P., Cazes, T.B., and Feijo, B. (2006). A genetic approach for the automatic adaptation of segmentation parameters. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci, Available online: http:\/\/www.isprs.org\/proceedings\/xxxvi\/4-c42\/Papers\/11_Adaption%20and%20further%20development%20III\/OBIA2006_Feitosa_et_al.pdf."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2364","DOI":"10.1016\/j.patrec.2010.07.007","article-title":"Supervised image segmentation using watershed transform, fuzzy classification and evolutionary computation","volume":"31","author":"Derivaux","year":"2010","journal-title":"Pattern Recognit. Lett"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Pignalberi, G., Cucchiara, R., Cinque, L., and Levialdi, S. (2003). Tuning range image segmentation by genetic algorithm. EURASIP J. Appli. Sig. Process.","DOI":"10.1155\/S1110865703303087"},{"key":"ref_28","first-page":"265","article-title":"A cognitive vision approach to image segmentation","volume":"1","author":"Martin","year":"2008","journal-title":"Tool. Artif. Intell"},{"key":"ref_29","unstructured":"Ferreira, R.S., Feitosa, R.Q., and Costa, G.A.O.P. (2012, January 7\u20139). A Multiscalar, Multicriteria Approach for Image Segmentation. Rio de Janeiro, Brazil."},{"key":"ref_30","unstructured":"Happ, P., Feitosa, R.Q., and Street, A. (2012, January 7\u20139). Assessment of Optimization Methods for Automatic Tuning of Segmentation Parameters. Rio de Janeiro, Brazil."},{"key":"ref_31","unstructured":"Freddrich, C.M.B., and Feitosa, R.Q. (2008). Automatic adaptation of segmentation parameters applied to non-homogeneous object detection. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci, Available online: http:\/\/www.isprs.org\/proceedings\/XXXVIII\/4-C1\/Sessions\/Session6\/6705_Feitosa_Proc_pap.pdf."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Michel, J., Grizonnet, M., and Canevet, O. (2012, January 22\u201327). Supervised Re-Segmentation for Very High-Resolution Satellite Images. Munich, Germany.","DOI":"10.1109\/IGARSS.2012.6351635"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1007\/s11263-008-0166-0","article-title":"Learning to combine bottom-up and top-down segmentation","volume":"81","author":"Levin","year":"2009","journal-title":"Int. J. Comput. Vis"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1394","DOI":"10.1109\/TPAMI.2011.232","article-title":"Toward holistic scene understanding: Feedback enabled cascaded classification models","volume":"34","author":"Li","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Heyden, A., Sparr, G., Nielsen, M., and Johansen, P. (2002). Computer Vision\u2014ECCV 2002, Springer.","DOI":"10.1007\/3-540-47969-4"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Hoiem, D., Efros, A.A., and Hebert, M. (2008, January 24\u201326). Closing the Loop in Scene Interpretation. Anchorage, AK, USA.","DOI":"10.1109\/CVPR.2008.4587587"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1055","DOI":"10.1016\/j.cviu.2010.07.004","article-title":"Detecting object boundaries using low-, mid-, and high-level information","volume":"114","author":"Zheng","year":"2010","journal-title":"Comput. Vis. Image Underst"},{"key":"ref_38","unstructured":"Christensen, H.I., and Nagel, H. (2006). Cognitive Vision Systems: Sampling the Spectrum of Approaches, Springer."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Leonardis, A., Bischof, H., and Pinz, A. (2006). Computer Vision\u2013ECCV 2006, Springer.","DOI":"10.1007\/11744078"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Narayanan, P.J., Nayar, S.K., and Shum, H. (2006). Computer Vision\u2013ACCV 2006, Springer.","DOI":"10.1007\/11612032"},{"key":"ref_41","unstructured":"Daelemans, W., Hoste, V., de Meulder, F., and Naudts, B. (2003). Machine Learning: Ecml 2003, Springer."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1817","DOI":"10.1016\/j.eswa.2007.08.088","article-title":"Particle swarm optimization for parameter determination and feature selection of support vector machines","volume":"35","author":"Lin","year":"2008","journal-title":"Expert Syst. Appl"},{"key":"ref_43","unstructured":"Fourie, C., Van Niekerk, A., and Mucina, L. (June, January 31). Optimising a One-Class SVM for Geographic Object-Based Novelty Detection. Cape Town, South Africa."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Chong, H.Y., Gortler, S.J., and Zickler, T. (2008). A perception-based color space for illumination-invariant image processing. ACM Trans. Gr, Available online: http:\/\/gvi.seas.harvard.edu\/sites\/all\/files\/Color_SIGGRAPH2008.pdf.","DOI":"10.1145\/1399504.1360660"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Shan, Y., Yang, F., and Wang, R. (2007, January 22\u201324). Color Space Selection for Moving Shadow Elimination. Chengdu, China.","DOI":"10.1109\/ICIG.2007.54"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.isprsjprs.2007.08.005","article-title":"Using colour, texture, and hierarchial segmentation for high-resolution remote sensing","volume":"63","author":"Stamon","year":"2008","journal-title":"ISPRS J. Photogramm. Remote Sens"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Kwok, N., Ha, Q., and Fang, G. (2009, January 17\u201319). Effect of Color Space on Color Image Segmentation. Tianjin, China.","DOI":"10.1109\/CISP.2009.5304250"},{"key":"ref_48","unstructured":"Munteanu, C., and Rosa, A. (2000, January 16\u201319). Towards Automatic Image Enhancement Using Genetic Algorithms. La Jolla, CA, USA."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"871","DOI":"10.1016\/S0031-3203(97)00073-3","article-title":"A genetic algorithm approach to color image enhancement","volume":"31","author":"Shyu","year":"1998","journal-title":"Pattern Recognit"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.ins.2011.09.033","article-title":"A cooperative particle swarm optimizer with statistical variable interdependence learning","volume":"186","author":"Sun","year":"2012","journal-title":"Inf. Sci"},{"key":"ref_51","unstructured":"Weicker, K., and Weicker, N. (1999, January 6\u20139). On the Improvement of Coevolutionary Optimizers by Learning Variable Interdependencies. Washington, DC, USA."},{"key":"ref_52","unstructured":"Fourie, C., and Schoepfer, E. (2012, January 7\u20139). Combining the Heuristic and Spectral Domains in Semi-Automated Segment Generation. Rio De Janeiro, Brazil."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1023\/A:1008202821328","article-title":"Differential evolution\u2014A simple and efficient heuristic for global optimization over continuous spaces","volume":"11","author":"Storn","year":"1997","journal-title":"J. Glob. Optim"},{"key":"ref_54","unstructured":"Melo, L.M., Costa, G.A.O.P., Feitosa, R.Q., and da Cruz, A.V. (2008). Quantum-inspired evolutionary algorithm and differential evolution used in the adaptation of segmentation parameters. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci, Available online: http:\/\/www.isprs.org\/proceedings\/xxxviii\/4-c1\/sessions\/Session11\/6667_Melo_Proc_pap.pdf."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Gorai, A., and Ghosh, A. (2009, January 9\u201311). Gray-Level Image Enhancement by Particle Swarm Optimization. Coimbatore, India.","DOI":"10.1109\/NABIC.2009.5393603"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/0167-8655(94)90058-2","article-title":"Genetic algorithms for optimal image enhancement","volume":"15","author":"Pal","year":"1994","journal-title":"Pattern Recognit. Lett"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"2274","DOI":"10.1109\/TPAMI.2012.120","article-title":"Slic superpixels compared to state-of-the-art superpixel methods","volume":"34","author":"Achanta","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell"},{"key":"ref_58","first-page":"241","article-title":"Distribution de la flore alpine: Dans le bassin des dranses et dans quelques r\u00e9gions voisines","volume":"37","author":"Jaccard","year":"1901","journal-title":"Bulletin de la Soci\u00e9t\u00e9 Vaudoise des Sciences Naturelles"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/6\/5\/3791\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T21:10:47Z","timestamp":1760217047000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/6\/5\/3791"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014,4,28]]},"references-count":58,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2014,5]]}},"alternative-id":["rs6053791"],"URL":"https:\/\/doi.org\/10.3390\/rs6053791","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2014,4,28]]}}}