{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T01:24:55Z","timestamp":1775784295246,"version":"3.50.1"},"reference-count":63,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2014,7,7]],"date-time":"2014-07-07T00:00:00Z","timestamp":1404691200000},"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>Quantitative methods for mapping sub-pixel land cover fractions are gaining increasing attention, particularly with regard to upcoming hyperspectral satellite missions. We evaluated five advanced regression algorithms combined with synthetically mixed training data for quantifying urban land cover from HyMap data at 3.6 and 9 m spatial resolution. Methods included support vector regression (SVR), kernel ridge regression (KRR), artificial neural networks (NN), random forest regression (RFR) and partial least squares regression (PLSR). Our experiments demonstrate that both kernel methods SVR and KRR yield high accuracies for mapping complex urban surface types, i.e., rooftops, pavements, grass- and tree-covered areas. SVR and KRR models proved to be stable with regard to the spatial and spectral differences between both images and effectively utilized the higher complexity of the synthetic training mixtures for improving estimates for coarser resolution data. Observed deficiencies mainly relate to known problems arising from spectral similarities or shadowing. The remaining regressors either revealed erratic (NN) or limited (RFR and PLSR) performances when comprehensively mapping urban land cover. Our findings suggest that the combination of kernel-based regression methods, such as SVR and KRR, with synthetically mixed training data is well suited for quantifying urban land cover from imaging spectrometer data at multiple scales.<\/jats:p>","DOI":"10.3390\/rs6076324","type":"journal-article","created":{"date-parts":[[2014,7,7]],"date-time":"2014-07-07T11:00:56Z","timestamp":1404730856000},"page":"6324-6346","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["A Comparison of Advanced Regression Algorithms for Quantifying Urban Land Cover"],"prefix":"10.3390","volume":"6","author":[{"given":"Akpona","family":"Okujeni","sequence":"first","affiliation":[{"name":"Geography Department, Humboldt-Universit\u00e4t zu Berlin, Unter den Linden 6, 10099 Berlin, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6576-8377","authenticated-orcid":false,"given":"Sebastian","family":"Van der Linden","sequence":"additional","affiliation":[{"name":"Geography Department, Humboldt-Universit\u00e4t zu Berlin, Unter den Linden 6, 10099 Berlin, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Benjamin","family":"Jakimow","sequence":"additional","affiliation":[{"name":"Geography Department, Humboldt-Universit\u00e4t zu Berlin, Unter den Linden 6, 10099 Berlin, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andreas","family":"Rabe","sequence":"additional","affiliation":[{"name":"Geography Department, Humboldt-Universit\u00e4t zu Berlin, Unter den Linden 6, 10099 Berlin, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6313-2081","authenticated-orcid":false,"given":"Jochem","family":"Verrelst","sequence":"additional","affiliation":[{"name":"Image Processing Laboratory, University of Valencia, C\/Catedr\u00e1tico Agustin Escardino 9, 46980 Valencia, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Patrick","family":"Hostert","sequence":"additional","affiliation":[{"name":"Geography Department, Humboldt-Universit\u00e4t zu Berlin, Unter den Linden 6, 10099 Berlin, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2014,7,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1016\/j.rse.2007.04.008","article-title":"Determination of robust spectral features for identification of urban surface materials in hyperspectral remote sensing data","volume":"111","author":"Heiden","year":"2007","journal-title":"Remote Sens. Environ"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1016\/j.rse.2004.02.013","article-title":"Spectrometry for urban area remote sensing\u2014Development and analysis of a spectral library from 350 to 2400 nm","volume":"91","author":"Herold","year":"2004","journal-title":"Remote Sens. Environ"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.rse.2003.04.008","article-title":"High spatial resolution spectral mixture analysis of urban reflectance","volume":"88","author":"Small","year":"2003","journal-title":"Remote Sens. Environ"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2298","DOI":"10.1016\/j.rse.2009.06.004","article-title":"The influence of urban structures on impervious surface maps from airborne hyperspectral data","volume":"113","author":"Hostert","year":"2009","journal-title":"Remote Sens. Environ"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1712","DOI":"10.1016\/j.rse.2009.03.018","article-title":"Hierarchical multiple endmember spectral mixture analysis (MESMA) of hyperspectral imagery for urban environments","volume":"113","author":"Franke","year":"2009","journal-title":"Remote Sens. Environ"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1525","DOI":"10.1109\/36.934082","article-title":"Automated differentiation of urban surfaces based on airborne hyperspectral imagery","volume":"39","author":"Roessner","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1907","DOI":"10.1109\/TGRS.2003.815238","article-title":"Spectral resolution requirements for mapping urban areas","volume":"41","author":"Herold","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"key":"ref_8","unstructured":"Quattrochi, D.W. (2006). Urban Remote Sensing, CRC Press Inc."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"013543","DOI":"10.1117\/1.2813466","article-title":"Classifying segmented hyperspectral data from a heterogeneous urban environment using support vector machines","volume":"1","author":"Janz","year":"2007","journal-title":"J. Appl. Remote Sens"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1016\/j.rse.2006.09.005","article-title":"Sub-pixel mapping of urban land cover using multiple endmember spectral mixture analysis: Manaus, Brazil","volume":"106","author":"Powell","year":"2007","journal-title":"Remote Sens. Environ"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1351","DOI":"10.1109\/TGRS.2005.846154","article-title":"Kernel-based methods for hyperspectral image classification","volume":"43","author":"Bruzzone","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1778","DOI":"10.1109\/TGRS.2004.831865","article-title":"Classification of hyperspectral remote sensing images with support vector machines","volume":"42","author":"Melgani","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2895","DOI":"10.1080\/01431160500185227","article-title":"Some issues in the classification of DAIS hyperspectral data","volume":"27","author":"Pal","year":"2006","journal-title":"Int. J. Remote Sens"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3462","DOI":"10.3390\/rs4113462","article-title":"Mapping savanna tree species at ecosystem scales using support vector machine classification and BRDF correction on airborne hyperspectral and LiDAR data","volume":"4","author":"Colgan","year":"2012","journal-title":"Remote Sens"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"327","DOI":"10.3390\/rs4020327","article-title":"Vegetation cover analysis of hazardous waste sites in Utah and Arizona using hyperspectral remote sensing","volume":"4","author":"Im","year":"2012","journal-title":"Remote Sens"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1820","DOI":"10.3390\/rs4061820","article-title":"Species-level differences in hyperspectral metrics among tropical rainforest trees as determined by a tree-based classifier","volume":"4","author":"Clark","year":"2012","journal-title":"Remote Sens"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Camps-Valls, G., and Bruzzone, L. (2009). Kernel Methods for Remote Sensing Data Analysis, John Wiley & Sons, Inc.","DOI":"10.1002\/9780470748992"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Sch\u00f6lkopf, B., and Smola, A.J. (2002). Learning with Kernels-Support Vector Machines, Regularization, Optimization, and Beyond, MIT Press.","DOI":"10.7551\/mitpress\/4175.001.0001"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1109\/JSTARS.2010.2069085","article-title":"Urban image classification with semisupervised multiscale cluster kernels","volume":"4","author":"Tuia","year":"2011","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/S0034-4257(98)00037-6","article-title":"Mapping chaparral in the Santa Monica Mountains using multiple endmember spectral mixture models","volume":"65","author":"Roberts","year":"1998","journal-title":"Remote Sens. Environ"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.rse.2011.07.021","article-title":"Synergies between VSWIR and TIR data for the urban environment: An evaluation of the potential for the hyperspectral infrared imager (HyspIRI) decadal survey mission","volume":"117","author":"Roberts","year":"2012","journal-title":"Remote Sens. Environ"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Weng, Q. (2008). Remote Sensing of Impervious Surfaces, CRC Press.","DOI":"10.1201\/9781420043754.fmatt"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.landurbplan.2011.03.017","article-title":"Mapping form and function in urban areas: An approach based on urban metrics and continuous impervious surface data","volume":"102","author":"Jacquet","year":"2011","journal-title":"Landsc. Urban Plann"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"939","DOI":"10.1016\/j.rse.2007.07.005","article-title":"Spectral mixture analysis for mapping abundance of urban surface components from the Terra\/ASTER data","volume":"112","author":"Pu","year":"2008","journal-title":"Remote Sens. Environ"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4785","DOI":"10.1080\/01431160802665918","article-title":"A comparison of two spectral mixture modelling approaches for impervious surface mapping in urban areas","volume":"30","author":"Canters","year":"2009","journal-title":"Int. J. Remote Sens"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.rse.2011.06.024","article-title":"Impervious surface quantification using a synthesis of artificial immune networks and decision\/regression trees from multi-sensor data","volume":"117","author":"Im","year":"2012","journal-title":"Remote Sens. Environ"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1045","DOI":"10.14358\/PERS.74.8.1045","article-title":"Comparison of spectral analysis techniques for impervious surface estimation using Landsat imagery","volume":"74","author":"Yuan","year":"2008","journal-title":"Photogramm. Eng. Remote Sens"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1003","DOI":"10.14358\/PERS.69.9.1003","article-title":"Urban land-cover change detection through sub-pixel imperviousness mapping using remotely sensed data","volume":"69","author":"Yang","year":"2003","journal-title":"Photogramm. Eng. Remote Sens"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1678","DOI":"10.1016\/j.rse.2009.03.012","article-title":"Large-area assessment of impervious surface based on integrated analysis of single-date Landsat-7 images and geospatial vector data","volume":"113","author":"Esch","year":"2009","journal-title":"Remote Sens. Environ"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1213","DOI":"10.14358\/PERS.74.10.1213","article-title":"Subpixel urban land cover estimation: Comparing cubist, random forests, and support vector regression","volume":"74","author":"Walton","year":"2008","journal-title":"Photogramm. Eng. Remote Sens"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1109\/LGRS.2006.871748","article-title":"Robust support vector regression for biophysical variable estimation from remotely sensed images","volume":"3","author":"Bruzzone","year":"2006","journal-title":"IEEE Geosci. Remote Sens. Lett"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.rse.2011.11.002","article-title":"Machine learning regression algorithms for biophysical parameter retrieval: Opportunities for Sentinel-2 and -3","volume":"118","author":"Verrelst","year":"2012","journal-title":"Remote Sens. Environ"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"64","DOI":"10.3390\/rs6010064","article-title":"Investigation of leaf diseases and estimation of chlorophyll concentration in seven barley varieties using fluorescence and hyperspectral indices","volume":"6","author":"Yu","year":"2013","journal-title":"Remote Sens"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1016\/j.rse.2006.07.014","article-title":"Neural network estimation of LAI, fAPAR, fCover and LAIxC(ab), from top of canopy MERIS reflectance data: Principles and validation","volume":"105","author":"Bacour","year":"2006","journal-title":"Remote Sens. Environ"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"5265","DOI":"10.3390\/rs5105265","article-title":"Empirical and physical estimation of canopy water content from CHRIS\/PROBA data","volume":"5","author":"Cernicharo","year":"2013","journal-title":"Remote Sens"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.rse.2013.06.007","article-title":"Support vector regression and synthetically mixed training data for quantifying urban land cover","volume":"137","author":"Okujeni","year":"2013","journal-title":"Remote Sens. Environ"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1107","DOI":"10.1016\/j.actaastro.2009.03.042","article-title":"Hyperspectral imaging\u2014An advanced instrument concept for the EnMAP mission (Environmental Mapping and Analysis Programme)","volume":"65","author":"Stuffler","year":"2009","journal-title":"Acta Astronaut"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Hastie, T., Tibshirani, R., and Friedman, J. (2009). The Elements of Statistical Learning-Data Mining, Inference, and Prediction, Springer. [2nd ed.].","DOI":"10.1007\/978-0-387-84858-7"},{"key":"ref_39","unstructured":"Haykin, S. (1999). Neural Networks\u2014A Comprehensive Foundation, Prentice Hall. [2nd ed.]."},{"key":"ref_40","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_41","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/S0169-7439(01)00155-1","article-title":"PLS-regression: A basic tool of chemometrics","volume":"58","author":"Wold","year":"2001","journal-title":"Chemometr. Intell. Lab. Syst"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1890\/1540-9295(2007)5[80:SHIUER]2.0.CO;2","article-title":"Spatial heterogeneity in urban ecosystems: Reconceptualizing land cover and a framework for classification","volume":"5","author":"Cadenasso","year":"2007","journal-title":"Front. Ecol. Environ"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0169-2046(00)00109-2","article-title":"Assessing the environmental performance of land cover types for urban planning","volume":"52","author":"Pauleit","year":"2000","journal-title":"Landsc. Urban Plann"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2609","DOI":"10.1080\/01431160110115834","article-title":"Geo-atmospheric processing of airborne imaging spectrometry data. Part 1: Parametric orthorectification","volume":"23","author":"Richter","year":"2002","journal-title":"Int. J. Remote Sens"},{"key":"ref_45","unstructured":"Cocks, T., Jenssen, R., Stewart, A., Wilson, I., and Shields, T. (1998, January 6\u20138). The HyMap\u2122 airborne hyperspectral sensor: The system, calibration and performance. Zurich, Switzerland."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2631","DOI":"10.1080\/01431160110115834","article-title":"Geo-atmospheric processing of airborne imaging spectrometry data. Part 2: Atmospheric\/topographic correction","volume":"23","author":"Richter","year":"2002","journal-title":"Int. J. Remote Sens"},{"key":"ref_47","unstructured":"SenStadt, Berlin Urban and Environmental Information System (UEIS). Available online: http:\/\/www.stadtentwicklung.berlin.de\/umwelt\/umweltatlas."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1016\/j.landurbplan.2012.01.001","article-title":"Urban structure type characterization using hyperspectral remote sensing and height information","volume":"105","author":"Heiden","year":"2012","journal-title":"Landsc. Urban Plann"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.rse.2005.12.003","article-title":"Correcting brightness gradients in hyperspectral data from urban areas","volume":"101","author":"Schiefer","year":"2006","journal-title":"Remote Sens. Environ"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1016\/0034-4257(94)90107-4","article-title":"Nonlinear spectral mixing models for vegetative and soil surfaces","volume":"47","author":"Borel","year":"1994","journal-title":"Remote Sens. Environ"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/0034-4257(93)90020-X","article-title":"Green vegetation, nonphotosynthetic vegetation, and soils in AVIRIS data","volume":"44","author":"Roberts","year":"1993","journal-title":"Remote Sens. Environ"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1603","DOI":"10.1016\/j.rse.2011.03.003","article-title":"Endmember variability in spectral mixture analysis: A review","volume":"115","author":"Somers","year":"2011","journal-title":"Remote Sens. Environ"},{"key":"ref_53","unstructured":"Rabe, A., van der Linden, S., and Hostert, P. ImageSVM, Version 2.1. Available online: http:\/\/www.imagesvm.net\/."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1249","DOI":"10.1109\/JSTARS.2014.2298752","article-title":"Toward a semiautomatic machine learning retrieval of biophysical parameters","volume":"7","author":"Verrelst","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"2999","DOI":"10.1016\/j.rse.2008.02.011","article-title":"Evaluation of random forest and adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery","volume":"112","author":"Chan","year":"2008","journal-title":"Remote Sens. Environ"},{"key":"ref_56","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_57","unstructured":"Breiman, L., Friedman, J.H., Olshen, R.A., and Stone, C.J. (1984). Classification and Regression Trees, Wadsworth & Brooks\/Cole Advanced Books & Software."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1016\/j.envsoft.2012.01.014","article-title":"ImageRF\u2014A user-oriented implementation for remote sensing image analysis with random forests","volume":"35","author":"Waske","year":"2012","journal-title":"Environ. Modell. Softw"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1332","DOI":"10.1109\/TGRS.2003.813128","article-title":"Analysis of hyperspectral data for estimation of temperate forest canopy nitrogen concentration: Comparison between an airborne (AVIRIS) and a spaceborne (Hyperion) sensor","volume":"41","author":"Smith","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1111\/j.1654-1103.2011.01370.x","article-title":"Mapping plant strategy types using remote sensing","volume":"23","author":"Schmidtlein","year":"2012","journal-title":"J. Veg. Sci"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1947","DOI":"10.1016\/j.jqsrt.2010.03.007","article-title":"Brightness-normalized partial least squares regression for hyperspectral data","volume":"111","author":"Feilhauer","year":"2010","journal-title":"J. Quant. Spectrosc. Radiat. Transf"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.chemolab.2004.12.011","article-title":"Performance of some variable selection methods when multicollinearity is present","volume":"78","author":"Chong","year":"2005","journal-title":"Chemometr. Intell. Lab. Syst"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1255\/jnirs.271","article-title":"Variable selection in near infrared spectroscopy based on significance testing in partial least squares regression","volume":"8","author":"Westad","year":"2000","journal-title":"J. Near Infrared Spectrosc"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/6\/7\/6324\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T21:13:21Z","timestamp":1760217201000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/6\/7\/6324"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014,7,7]]},"references-count":63,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2014,7]]}},"alternative-id":["rs6076324"],"URL":"https:\/\/doi.org\/10.3390\/rs6076324","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2014,7,7]]}}}