{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T11:42:59Z","timestamp":1778758979813,"version":"3.51.4"},"reference-count":75,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2012,4,12]],"date-time":"2012-04-12T00:00:00Z","timestamp":1334188800000},"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>Over the last two decades, multiple classifier system (MCS) or classifier ensemble has shown great potential to improve the accuracy and reliability of remote sensing image classification. Although there are lots of literatures covering the MCS approaches, there is a lack of a comprehensive literature review which presents an overall architecture of the basic principles and trends behind the design of remote sensing classifier ensemble. Therefore, in order to give a reference point for MCS approaches, this paper attempts to explicitly review the remote sensing implementations of MCS and proposes some modified approaches. The effectiveness of existing and improved algorithms are analyzed and evaluated by multi-source remotely sensed images, including high spatial resolution image (QuickBird), hyperspectral image (OMISII) and multi-spectral image (Landsat ETM+).Experimental results demonstrate that MCS can effectively improve the accuracy and stability of remote sensing image classification, and diversity measures play an active role for the combination of multiple classifiers. Furthermore, this survey provides a roadmap to guide future research, algorithm enhancement and facilitate knowledge accumulation of MCS in remote sensing community.<\/jats:p>","DOI":"10.3390\/s120404764","type":"journal-article","created":{"date-parts":[[2012,4,12]],"date-time":"2012-04-12T11:21:21Z","timestamp":1334229681000},"page":"4764-4792","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":256,"title":["Multiple Classifier System for Remote Sensing Image Classification: A Review"],"prefix":"10.3390","volume":"12","author":[{"given":"Peijun","family":"Du","sequence":"first","affiliation":[{"name":"Department of Geographical Information Science, Nanjing University, Nanjing 210093, China"},{"name":"Key Laboratory for Land Environment and Disaster Monitoring of State Bureau of Surveying and Mapping of China, China University of Mining and Technology, Xuzhou 221116, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junshi","family":"Xia","sequence":"additional","affiliation":[{"name":"Key Laboratory for Land Environment and Disaster Monitoring of State Bureau of Surveying and Mapping of China, China University of Mining and Technology, Xuzhou 221116, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Hebei Bureau of Surveying and Mapping, Shijiazhuang 050031, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kun","family":"Tan","sequence":"additional","affiliation":[{"name":"Key Laboratory for Land Environment and Disaster Monitoring of State Bureau of Surveying and Mapping of China, China University of Mining and Technology, Xuzhou 221116, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory for Land Environment and Disaster Monitoring of State Bureau of Surveying and Mapping of China, China University of Mining and Technology, Xuzhou 221116, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sicong","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory for Land Environment and Disaster Monitoring of State Bureau of Surveying and Mapping of China, China University of Mining and Technology, Xuzhou 221116, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2012,4,12]]},"reference":[{"key":"ref_1","first-page":"1747","article-title":"Remote sensing applications: An overview","volume":"93","author":"Navalgund","year":"2007","journal-title":"Current"},{"key":"ref_2","first-page":"173","article-title":"Ten years of technology advancement in remote sensing and the research in the CRC-AGIP lab in GCE","volume":"64","author":"Zhang","year":"2010","journal-title":"Geomatica"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1080\/01431160600746456","article-title":"A survey of image classification methods and techniques for improving classification performance","volume":"28","author":"Lu","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2291","DOI":"10.1109\/TGRS.2002.802476","article-title":"Multiple classifiers applied to multisource remote sensing data","volume":"40","author":"Briem","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1007\/978-3-540-72523-7_50","article-title":"Multiple classifier systems in remote sensing: From basics to recent developments","volume":"4472","author":"Benediktsson","year":"2007","journal-title":"Mult. Classif. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1016\/S0034-4257(00)00145-0","article-title":"Combining multiple classifiers: An application using spatial and remotely sensed information for land cover type mapping","volume":"74","author":"Steele","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1733","DOI":"10.1080\/01431160600962566","article-title":"Mapping a specific class with an ensemble of classifiers","volume":"28","author":"Foody","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"4609","DOI":"10.1080\/01431160701244872","article-title":"Increasing soft classification accuracy through the use of an ensemble of classifiers","volume":"28","author":"Doan","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"801","DOI":"10.1109\/TGRS.2002.1006354","article-title":"Multiple classifier systems for supervised remote sensing image classification based on dynamic classifier selection","volume":"40","author":"Smits","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2880","DOI":"10.1109\/TGRS.2010.2041784","article-title":"Sensitivity of support vector machines to random feature selection in classification of hyperspectral data","volume":"48","author":"Waske","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1007\/s100440200019","article-title":"Hierarchical fusion of multiple classifiers for hyperspectral data analysis","volume":"5","author":"Kumar","year":"2002","journal-title":"Pattern Anal. Appl."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Kuncheva, L.I. (2004). Combining Pattern Classifiers: Methods and Algorithms, Wiley-Interscience.","DOI":"10.1002\/0471660264"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Rokach, L (2010). Pattern Classification Using Ensemble Methods, World Scientific.","DOI":"10.1142\/9789814271073"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1007\/s13042-010-0007-7","article-title":"Multiple classifier systems for robust classifier design in adversarial environments","volume":"1","author":"Biggio","year":"2010","journal-title":"J. Mach. Learn. Cybern."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1080\/19479832.2010.485935","article-title":"A classiler ensemble based on fusion of support vector machines for classifying hyperspectral data","volume":"1","author":"Ceamanos","year":"2010","journal-title":"Int. J. Image Data Fusion"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/BF00058655","article-title":"Bagging predictors","volume":"24","author":"Breiman","year":"1996","journal-title":"Mach. Learn."},{"key":"ref_17","unstructured":"Freund, Y., and Schapire, R.E. (1996, January 3\u20136). Experiments with a New Boosting Algorithm. Bari, Italy."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"688","DOI":"10.1109\/21.156582","article-title":"Consensus theoretic classification methods","volume":"22","author":"Benediktsson","year":"1992","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1109\/36.602526","article-title":"Hybrid consensus theoretic classification","volume":"35","author":"Benediktsson","year":"1997","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2828","DOI":"10.1109\/TGRS.2006.876708","article-title":"Decision fusion for the classification of urban remote sensing images","volume":"44","author":"Fauvel","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","first-page":"584","article-title":"Remote sensing classification of spectral,spatial and contextual data using multiple classifier systems","volume":"20","author":"Ebeir","year":"2001","journal-title":"Image Rochester N. Y."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3858","DOI":"10.1109\/TGRS.2007.898446","article-title":"Fusion of support vector machines for classification of multisensor data","volume":"45","author":"Waske","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.isprsjprs.2006.09.004","article-title":"Multiple support vector machines for land cover change detection: An application for mapping urban extensions","volume":"61","author":"Nemmour","year":"2006","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"450","DOI":"10.1016\/j.isprsjprs.2009.01.003","article-title":"Classifier ensembles for land cover mapping using multitemporal SAR imagery","volume":"64","author":"Waske","year":"2009","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_25","first-page":"231","article-title":"Neural network ensembles, cross validation, and active learning","volume":"7","author":"Krogh","year":"1995","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"686","DOI":"10.1016\/j.neucom.2005.12.014","article-title":"Evolving hybrid ensembles of learning machines for better generalisation","volume":"69","author":"Chandra","year":"2006","journal-title":"Neurocomputing"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1023\/A:1022859003006","article-title":"Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy","volume":"51","author":"Kuncheva","year":"2003","journal-title":"Mach. Learn."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1016\/j.inffus.2004.04.004","article-title":"Diversity creation methods: A survey and categorisation","volume":"6","author":"Brown","year":"2005","journal-title":"Inf. Fusion"},{"key":"ref_29","first-page":"2539","article-title":"The effect of classifier agreement on the accuracy of the combined classifier in decision level fusion","volume":"39","author":"Michail","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1658","DOI":"10.1016\/j.rse.2009.03.014","article-title":"Classification accuracy comparison: Hypothesis tests and the use of confidence intervals in evaluations of difference, equivalence and non-inferiority","volume":"113","author":"Foody","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_31","first-page":"1","article-title":"Multi-classifier systems: Review and a roadmap for developers","volume":"3","author":"Ranawana","year":"2006","journal-title":"Int. J. Hybrid Intell. Syst."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1109\/34.982906","article-title":"A theoretical study on six classifier fusion strategies","volume":"24","author":"Kuncheva","year":"2002","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_33","unstructured":"Giacinto, G., Roli, F., and Vernazza, G (1997). Neurocomputation in Remote Sensing Data Analysis, Springer-Verlag."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1109\/34.273716","article-title":"Decision combination in multiple classifier systems","volume":"16","author":"Ho","year":"1994","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/S0167-8655(00)00096-9","article-title":"An approach to the automatic design of multiple classifier systems","volume":"22","author":"Giacinto","year":"2001","journal-title":"Pattern. Recogn. Lett."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1504\/IJKESDP.2010.034678","article-title":"Neuro-fuzzy-combiner: An effective multiple classifier system","volume":"2","author":"Ghosh","year":"2010","journal-title":"Int. J. Knowl. Eng. Soft Data Paradig."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1109\/34.588027","article-title":"Combination of multiple classifiers using local accuracy estimates","volume":"19","author":"Woods","year":"1997","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1459","DOI":"10.1016\/j.patcog.2004.01.008","article-title":"Multiple classifier combination for face-based identity verification","volume":"37","author":"Czyz","year":"2003","journal-title":"Pattern. Recogn."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1619","DOI":"10.1109\/TPAMI.2006.211","article-title":"Rotation forest: A new classifier ensemble method","volume":"28","author":"Rodriguez","year":"2009","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1367","DOI":"10.1109\/36.763301","article-title":"Classification of multisource and hyperspectral data based on decision fusion","volume":"37","author":"Benediktsson","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2828","DOI":"10.1109\/TGRS.2006.876708","article-title":"Decision fusion for the classification of urban remote sensing images","volume":"44","author":"Fauvel","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Zhou, Z.H., When, Semi-Supervised, and Learning, Meets (2009, January 10\u201312). Ensemble Learning. Reykjavik, Iceland. Volume 5.","DOI":"10.1007\/978-3-642-02326-2_53"},{"key":"ref_43","unstructured":"Webb, G.I., and Sammut, C. (2010). Encyclopedia of Machine Learning, Springer."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1109\/21.155943","article-title":"Methods of combining multiple classifiers and their applications to handwriting recognition","volume":"22","author":"Xu","year":"1992","journal-title":"EEE Trans. Syst. Man Cybern."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1007\/s100440050038","article-title":"Serial combination of multiple experts: A unified evaluation","volume":"2","author":"Rahman","year":"1999","journal-title":"Pattern Anal. Appl."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"608","DOI":"10.1016\/j.patcog.2005.08.017","article-title":"Using diversity of errors for selecting members of a committee classifier","volume":"39","author":"Aksela","year":"2006","journal-title":"Pattern. Recogn."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.patrec.2005.08.011","article-title":"Random forests for land cover classification","volume":"27","author":"Gislason","year":"2006","journal-title":"Pattern. Recogn. Lett."},{"key":"ref_48","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":"Mach. Learn."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forest","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"832","DOI":"10.1109\/34.709601","article-title":"The random subspace method for constructing decision forests","volume":"20","author":"Ho","year":"1998","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1023\/A:1013912006537","article-title":"Logistic regression, adaboost and bregman distances","volume":"48","author":"Collins","year":"2002","journal-title":"Mach. Learn."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1023\/A:1007659514849","article-title":"MultiBoosting: A technique for combining boosting and wagging","volume":"39","author":"Webb","year":"2000","journal-title":"Mach. Learn."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1524","DOI":"10.1016\/j.patrec.2008.03.006","article-title":"RotBoost: A technique for combining Rotation Forest and AdaBoost","volume":"29","author":"Zhang","year":"2008","journal-title":"Pattern. Recogn. Lett."},{"key":"ref_54","first-page":"705","article-title":"Comparison of classifier fusion methods for classification in pattern recognition tasks","volume":"4109","author":"Inesta","year":"2006","journal-title":"Struct. Syntactic Stat. Pattern Recogn."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"697","DOI":"10.1016\/S0262-8856(01)00045-2","article-title":"Design of effective neural network ensembles for image classification","volume":"19","author":"Giacinto","year":"2001","journal-title":"Image Vis. Comput. J."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/S1566-2535(02)00051-9","article-title":"Relationships between combination methods and measures of diversity in combining classifiers","volume":"3","author":"Shipp","year":"2001","journal-title":"Inf. Fusion"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1109\/34.667881","article-title":"On combining classifiers","volume":"20","author":"Kittler","year":"1998","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.isprsjprs.2006.09.004","article-title":"Multiple support vector machines for land cover change detection: An application for mapping urban extensions","volume":"61","author":"Nemmour","year":"2006","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_59","unstructured":"Kong, Z., and Cai, Z (August, January 30). Advances of Research in Fuzzy Integral for Classifiers' fusion. Washington, DC, USA."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Ben Abdallah, A.C., Frigui, H., and Gader, P (2012). Adaptive local fusion with fuzzy integrals. IEEE Trans. Fuzzy Syst., in press.","DOI":"10.1109\/TFUZZ.2012.2187062"},{"key":"ref_61","first-page":"117","article-title":"A new combination rules of evidence theory (in Chinese)","volume":"8","author":"Sun","year":"2000","journal-title":"Acta Electron. Sin."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1718","DOI":"10.1016\/j.patcog.2007.10.015","article-title":"From dynamic classifier selection to dynamic ensemble selection","volume":"41","author":"Ko","year":"2008","journal-title":"Pattern. Recogn."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1109\/34.588027","article-title":"Combination of multiple classifiers using local accuracy estimates","volume":"19","author":"Woods","year":"1997","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1049\/el:20000374","article-title":"Selection of image classifiers","volume":"36","author":"Giacinto","year":"2000","journal-title":"Electron. Lett."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1109\/TSMC.1976.5408784","article-title":"The distance-weighted k-nearest-neighbor rule","volume":"SMC-6","author":"Dudani","year":"1976","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_66","first-page":"1","article-title":"An overview of classifier fusion methods","volume":"7","author":"Ruta","year":"2000","journal-title":"Comput. Inf. Syst."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.isprsjprs.2010.11.001","article-title":"Support vector machines in remote sensing: A review","volume":"66","author":"Mountrakis","year":"2011","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_68","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_69","doi-asserted-by":"crossref","first-page":"554","DOI":"10.1016\/S0034-4257(03)00132-9","article-title":"An assessment of the effectiveness of decision tree methods for land cover classification","volume":"86","author":"Pal","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.rse.2004.06.017","article-title":"Toward intelligent training of supervised image classifications: Directing training data acquisition for SVM classification","volume":"93","author":"Foody","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"2089","DOI":"10.1016\/j.rse.2009.05.014","article-title":"Estimating impervious surfaces from medium spatial resolution imagery using the self-organizing map and multi-layer perceptron neural networks","volume":"113","author":"Hu","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_72","unstructured":"Witten, I.H., and Frank, W (2006). Data Mining: Practical Machine Learning Tools and Techniques, China Machine Press."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1016\/j.rse.2004.07.013","article-title":"A comparison of error metrics and constraints for multiple endmember spectral mixture analysis and spectral angle mapper","volume":"93","author":"Dennison","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_74","unstructured":"Quinlan, J.R. (1993). C4.5: Programs for Machine Learning, Morgan Kaufmann Publishers Inc."},{"key":"ref_75","first-page":"113","article-title":"Application of simplified fuzzy H-ARTMAP network to hyperspectral remote sensing classification (in Chinese)","volume":"33","author":"Zhang","year":"2009","journal-title":"Sci. Surv. Mapp."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/12\/4\/4764\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T21:49:46Z","timestamp":1760219386000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/12\/4\/4764"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2012,4,12]]},"references-count":75,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2012,4]]}},"alternative-id":["s120404764"],"URL":"https:\/\/doi.org\/10.3390\/s120404764","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2012,4,12]]}}}