{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:40:30Z","timestamp":1760244030072,"version":"build-2065373602"},"reference-count":76,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2009,1,5]],"date-time":"2009-01-05T00:00:00Z","timestamp":1231113600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Since 2008 more than half of the world population is living in cities and urban sprawl is continuing. Because of these developments, the mapping and monitoring of urban environments and their surroundings is becoming increasingly important. In this study two object-oriented approaches for high-resolution mapping of sealed surfaces are compared: a standard non-hierarchic approach and a full hierarchic approach using both multi-layer perceptrons and decision trees as learning algorithms. Both methods outperform the standard nearest neighbour classifier, which is used as a benchmark scenario. For the multi-layer perceptron approach, applying a hierarchic classification strategy substantially increases the accuracy of the classification. For the decision tree approach a one-against-all hierarchic classification strategy does not lead to an improvement of classification accuracy compared to the standard all-against-all approach. Best results are obtained with the hierarchic multi-layer perceptron classification strategy, producing a kappa value of 0.77. A simple shadow reclassification procedure based on characteristics of neighbouring objects further increases the kappa value to 0.84.<\/jats:p>","DOI":"10.3390\/s90100022","type":"journal-article","created":{"date-parts":[[2009,1,5]],"date-time":"2009-01-05T11:04:36Z","timestamp":1231153476000},"page":"22-45","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Full Hierarchic Versus Non-Hierarchic Classification Approaches for Mapping Sealed Surfaces at the Rural-Urban Fringe Using High-Resolution Satellite Data"],"prefix":"10.3390","volume":"9","author":[{"given":"Tim","family":"De Roeck","sequence":"first","affiliation":[{"name":"Vrije Universiteit Brussel, Cartography and GIS Research Unit, Department of Geography, Brussels, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tim","family":"Van de Voorde","sequence":"additional","affiliation":[{"name":"Vrije Universiteit Brussel, Cartography and GIS Research Unit, Department of Geography, Brussels, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Frank","family":"Canters","sequence":"additional","affiliation":[{"name":"Vrije Universiteit Brussel, Cartography and GIS Research Unit, Department of Geography, Brussels, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2009,1,5]]},"reference":[{"key":"ref_1","unstructured":"UNFPA http:\/\/www.unfpa.org\/swp\/2007\/presskit\/pdf\/sowp2007_eng.pdf."},{"key":"ref_2","unstructured":"EEA http:\/\/reports.eea.europa.eu\/briefing_2006_4\/en."},{"key":"ref_3","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_4","first-page":"100","article-title":"The importance of imperviousness","volume":"1","author":"Schueler","year":"1994","journal-title":"Watersh. Protect. Techn."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Weng, Q. (2008). Remote Sensing of Impervious Surfaces, Taylor & Francis Group.","DOI":"10.1201\/9781420043754.fmatt"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"963","DOI":"10.14358\/PERS.69.9.963","article-title":"A comparison of urban mapping methods using high-resolution digital imagery","volume":"69","author":"Thomas","year":"2003","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"973","DOI":"10.14358\/PERS.69.9.973","article-title":"Synergistic use of lidar and color aerial photography for mapping urban parcel imperviousness","volume":"69","author":"Hodgson","year":"2003","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"169","DOI":"10.14358\/PERS.71.2.169","article-title":"Shadow analysis in high-resolution satellite imagery of urban areas","volume":"71","author":"Dare","year":"2005","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_9","first-page":"1017","article-title":"Improving pixel-based VHR land-cover classifications of urban areas with post-classification techniques","volume":"73","author":"Canters","year":"2007","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_10","first-page":"55","article-title":"The use of contextual information in the classification of remotely sensed data","volume":"49","author":"Gurney","year":"1983","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_11","first-page":"949","article-title":"Inferring urban land use from satellite sensor images using kernel-based spatial reclassification","volume":"62","author":"Barnsley","year":"1996","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1016\/S0098-3004(99)00123-5","article-title":"Reducing structural clutter in land cover classifications of high spatial resolution remotely-sensed images for urban land use mapping","volume":"26","author":"Barr","year":"2000","journal-title":"Comput. Geosci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"786","DOI":"10.1109\/PROC.1979.11328","article-title":"Statistical and structural approaches to texture","volume":"67","author":"Haralick","year":"1979","journal-title":"Proc. IEEE"},{"key":"ref_14","first-page":"W6","article-title":"Combining spectral and textural features for multispectral image classification with artificial neural networks","volume":"32","author":"He","year":"1999","journal-title":"Int. Arch. Photogramm. Remote Sens."},{"key":"ref_15","first-page":"67","article-title":"The use of structural information for improving land-cover classification accuracies at the rural-urban fringe","volume":"56","author":"Gong","year":"1990","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1585","DOI":"10.14358\/PERS.74.12.1585","article-title":"Classification of very high spatial resolution imagery based on the fusion of edge and multispectral information","volume":"74","author":"Huang","year":"2008","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"529","DOI":"10.14358\/PERS.69.5.529","article-title":"Comparison of grey-level reduction and different texture spectrum encoding methods for land-use classification using a panchromatic Ikonos image","volume":"69","author":"Xu","year":"2003","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1633","DOI":"10.1109\/TGRS.2006.864391","article-title":"Land cover classification using fuzzy rules and aggregation of contextual information through evidence theory","volume":"44","author":"Laha","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","unstructured":"Chan, J.C.-W., Bellens, R., Canters, F., and Gautama, S. An assessment of geometric activity features for classification of urban man-made objects using very-high-resolution imagery. Photogramm. Eng. Remote Sens., In press."},{"key":"ref_20","first-page":"1451","article-title":"Land-use classification of remotely sensed data using Kohonen self-organizing feature map neural networks","volume":"66","author":"Ji","year":"2000","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2177","DOI":"10.1080\/01431160310001618464","article-title":"Examining the effect of spatial resolution and texture window size on classification accuracy: an urban environment case","volume":"25","author":"Chen","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1016\/j.compenvurbsys.2005.01.007","article-title":"A study of lacunarity-based texture analysis approaches to improve urban image classification","volume":"29","author":"Myint","year":"2005","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_23","unstructured":"(2000). Angewandte Geographische Informationsverarbeitung XI. Beitr\u00e4ge zum AGIT-Symposium Salzburg, Karlsruhe."},{"key":"ref_24","unstructured":"Schiewe, J. (2002, January July). Segmentation of high-resolution remotely sensed data \u2013 concepts, applications and problems. Ottawa. Canada."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"143","DOI":"10.14358\/PERS.69.2.143","article-title":"Class-guided building extraction from Ikonos imagery","volume":"69","author":"Lee","year":"2003","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_26","unstructured":"Liu, Z.J., Wang, J., and Liu, W.P. (2005, January July). Building extraction from high resolution imagery based on multi-scale object oriented classification and probabilistic Hough transform. Seoul, South Korea."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"441","DOI":"10.5589\/m03-006","article-title":"Preleminary 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_28","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. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"5655","DOI":"10.1080\/014311602331291215","article-title":"Integration of object-based and pixel-based classification for mapping mangroves with IKONOS imagery","volume":"25","author":"Wang","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_30","unstructured":"Siachalou, S., Doxani, G., and Tsakiri-Strati, M. (, January March). Classification enhancement in urban areas. Berlin, Germany."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1109\/36.905239","article-title":"A new approach for the morphological segmentation of high-resolution satellite imagery","volume":"39","author":"Pesaresi","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","unstructured":"Rangsaneri, Y., Thitimajshima, P., and Kanotai, S. (2001, January November). Multispectral image segmentation using art1\/art2 neural networks. Singapore."},{"key":"ref_33","unstructured":"Guarnieri, A., and Vettore, A. (2002, January July). Automated techniques for satellite image segmentation. Ottawa, Canada."},{"key":"ref_34","unstructured":"Chen, Q., Zhou, C., Luo, J., and Ming, D. (2004). IWCIA."},{"key":"ref_35","unstructured":"Esch, T., Roth, A., and Dech, S. (2005, January March). Robust approach towards an automated detection of built-up areas from high resolution radar imagery. Tempe, AZ, USA."},{"key":"ref_36","unstructured":"Meinel, G., and Neubert, M. (2004, January July,). A comparison of segmentation programs for high resolution remote sensing data. Istanbul, Turkey."},{"key":"ref_37","unstructured":"Carleer, A. (2005). Region-based Classification Potential for Land-cover Classification with Very High Spatial Resolution Satellite Data. [Ph.D. thesis, ULB]."},{"key":"ref_38","unstructured":"Zhou, Y., and Wang, Y.Q. (2006, January May,). Extraction of impervious surface area using orthophotos in Rhode Island. Reno, NV, USA."},{"key":"ref_39","unstructured":"Yuan, F., and Bauer, M.E. (2006, January May,). Mapping impervious surface area using high resolution imagery: a comparison of object-based and per pixel classification. Reno, NV, USA."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1285","DOI":"10.14358\/PERS.71.11.1285","article-title":"Assessment of very high spatial resolution satellite image segmentations","volume":"71","author":"Carleer","year":"2005","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_41","unstructured":"Diermayer, E., Hostert, P., Schiefer, S., and Damm, A. (2006, January March). Comparing pixel- and object-based classification of imperviousness with HRSC-AX data. Berlin, Germany."},{"key":"ref_42","unstructured":"Caprioli, M., and Tarantino, E. Urban features recognition from VHR satellite data with an object-oriented approach. Stuttgart, Germany."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1016\/S0034-4257(97)00049-7","article-title":"Decision tree classification of land cover from remotely sensed data","volume":"61","author":"Friedl","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"3853","DOI":"10.1080\/01431160110109570","article-title":"Hard and soft classifications by a neural network with a non-exhaustively defined set of classes","volume":"23","author":"Foody","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_45","unstructured":"Cetin, M., Kavzoglu, T., and Musaoglu, N. (2004, January July). Classification of multi-spectral, multi-temporal and multi-sensor images using principal components analysis and artificial neural networks: Beykoz case. Istanbul, Turkey."},{"key":"ref_46","unstructured":"Liu, J., Shao, G., Zhu, H., and Liu, S. (2004, January June). A neural network approach for information extraction from remotely sensed data. Sweden."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/0034-4257(94)00063-S","article-title":"A remote sensing based vegetation classification logic for global land cover analysis","volume":"51","author":"Running","year":"1995","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1075","DOI":"10.1080\/01431169608949069","article-title":"Classification trees: an alternative to traditional land cover classifiers","volume":"17","author":"Hansen","year":"1996","journal-title":"Int. J. Remote Sens."},{"key":"ref_49","unstructured":"Quinlan, R.J. (1993). C4.5: Programs for Machine Learning., Morgan Kaufmann Publishers Inc."},{"key":"ref_50","unstructured":"Sheeren, D., Puissant, A., Weber, C., Gan\u00e7arski, P., and Wemmert, C. (2006, January March). Deriving classification rules from multiple remotely sensed urban data with data mining. Berlin, Germany."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"3003","DOI":"10.1080\/014311698214398","article-title":"Image classification algorithm based on the RBF neural network and K-means","volume":"19","author":"Rollet","year":"1998","journal-title":"Int. J. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1016\/S0034-4257(99)00051-6","article-title":"A neural network method for efficient vegetation mapping","volume":"70","author":"Carpenter","year":"1999","journal-title":"Remote Sens. Environ."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/S0034-4257(98)00088-1","article-title":"Fuzzy neural network classification of global land cover from a 1\u00b0 AVHRR data set","volume":"67","author":"Gopal","year":"1999","journal-title":"Remote Sens. Environ."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"981","DOI":"10.14358\/PERS.69.9.981","article-title":"Comparing ARTMAP neural network with the maximum-likelihood classifier for detecting urban change","volume":"69","author":"Seto","year":"2003","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1109\/TGRS.1999.739167","article-title":"Extended LVQ neural network approach to land cover mapping","volume":"37","author":"Ito","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_56","unstructured":"Molinier, M., Laaksonen, J., Ahola, J., and H\u00e4me, T. (-, January July). Self-organizing map application for retrieval of man-made structures in remote sensing data. Denver, CO, USA."},{"key":"ref_57","first-page":"535","article-title":"The effect of neural-network structure on a multispectral land-use\/land-cover classification","volume":"63","author":"Paola","year":"1997","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_58","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 based on morphological transformations","volume":"41","author":"Benediktsson","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"910","DOI":"10.3390\/s8020910","article-title":"Improving Distributed Runoff Prediction in Urbanized Catchments with Remote Sensing based Estimates of Impervious Surface Cover","volume":"8","author":"Chormanski","year":"2008","journal-title":"Sensors"},{"key":"ref_60","unstructured":"Bianchin, A., and Bravin, L. (2003, January June). Land use in urban context from IKONOS image: a case study. Regensburg, Germany."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Weng, Q. (2008). Remote Sensing of Impervious Surfaces, CRC Press, Taylor & Francis Group.","DOI":"10.1201\/9781420043754.fmatt"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Yoon, J.J., Koch, C., and Ellis, T.J. (2002, January September). ShadowFlash: an approach for shadow removal in an active illumination environment. UK.","DOI":"10.5244\/C.16.62"},{"key":"ref_63","unstructured":"De Genst, W., and Canters, F. (2004, January October). Extracting detailed urban land-cover information from hyper-spectral imagery. Chiogga-Venice, Italy. unpaginated CD-ROM."},{"key":"ref_64","unstructured":"Gigandet, X. (2004). Satellite Image Segmentation and Classification., Diploma project, Signal Processing Institute of the Swiss Federal Institute of Technology."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1399","DOI":"10.14358\/PERS.71.12.1399","article-title":"Automatic segmentation of high-resolution satellite imagery by integrating texture, intensity and color features","volume":"71","author":"Hu","year":"2005","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_66","unstructured":"Baatz, M., Benz, U., Dehghani, S., Heynen, M., H\u00f6ltje, A., Hofmann, P., Lingenfelder, I., Mimler, M., Sohlbach, M., Weber, M., and Willhauck, G. (2004). eCognition Professional \u2013 User Guide 4., Munich: Definiens-Imaging."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1023\/A:1007515423169","article-title":"An empirical comparison of voting classification algorithms: bagging, boosting, and variants","volume":"36","author":"Bauer","year":"1999","journal-title":"J. Machine Learn."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1023\/A:1007607513941","article-title":"An experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting and randomization","volume":"40","author":"Dietterich","year":"2000","journal-title":"Machine Learn."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1016\/j.rse.2004.01.007","article-title":"Classification of remotely sensed imagery using stochastic gradient boosting as a refinement of classification tree analysis","volume":"90","author":"Lawrence","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"963","DOI":"10.14358\/PERS.70.8.963","article-title":"Uncertainty and confidence in land cover classification using a hybrid classifier approach","volume":"70","author":"Liu","year":"2004","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_71","unstructured":"Misakova, L., Jacquin, A., and Gay, M. (2006, January March). Mapping urban sprawl using VHR-data and object oriented classification. Berlin, Germany."},{"key":"ref_72","unstructured":"Herold, M., Gardner, M., Hadley, B., and Roberts, D. The spectral dimension in urban land cover mapping from high-resolution optical remote sensing data. Istanbul, Turkey."},{"key":"ref_73","unstructured":"Syed, S., Dare, P., and Jones, S. Automatic classification of land cover features with high resolution imagery and lidar data: an object-oriented approach. Melbourne, Australia."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Van der Meer, F., and De Jong, S. (2001). Imaging Spectrometry., Kluwer Academic.","DOI":"10.1007\/0-306-47578-2"},{"key":"ref_75","unstructured":"Chen, J., and Hepner, G. Investigation of imaging spectrometry for discriminating urban land covers and surface materials. St Louis, MO, USA. unpaginated CD-ROM."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1016\/j.rse.2004.02.013","article-title":"Spectrometry for urban area remote sensing \u2013 development and analysis of a spectral library from 350 to 2400 nm","volume":"91","author":"Herold","year":"2004","journal-title":"Remote Sens. Environ."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/9\/1\/22\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T22:09:38Z","timestamp":1760220578000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/9\/1\/22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2009,1,5]]},"references-count":76,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2009,1]]}},"alternative-id":["s90100022"],"URL":"https:\/\/doi.org\/10.3390\/s90100022","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2009,1,5]]}}}