Land use cartography from hyperion hyperspectral imagery analysis: Results from a mediterranean site (Conference presentation)

Petropoulos, G.P.aArvanitis, K.bSigrimis, N.bPiromalis, D.D.cBoglou, A.K.d

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dc.contributor.authorPetropoulos, G.P.aen
dc.contributor.authorArvanitis, K.ben
dc.contributor.authorSigrimis, N.ben
dc.contributor.authorPiromalis, D.D.cen
dc.contributor.authorBoglou, A.K.den
dc.rightsDefault License-
dc.subjectLand cover mappingen
dc.subjectSpectral angle mappersen
dc.subjectSupport vector machine (SVMs)en
dc.subjectArtificial intelligenceen
dc.subjectError statisticsen
dc.subjectRemote sensingen
dc.subjectSupport vector machinesen
dc.titleLand use cartography from hyperion hyperspectral imagery analysis: Results from a mediterranean siteen
heal.generalDescriptionart. no. 6495629, pp. 26-31.en
heal.generalDescriptionInstitute of Geography and Earth Sciences, University of Aberystwyth, United Kingdomen
heal.generalDescriptionDepartment of Natural Resources Management and Agricultural Engineering, Agricultural University of Athens, Athens, Greeceen
heal.generalDescriptionDepartment of Automation, Technological Education Institute of Piraeus, Athens, Greeceen
heal.generalDescriptionDepartment of Sciences, Technological Education Institute of Kavala, Kavala, Greeceen
heal.generalDescriptionISSN: 10823409en
heal.generalDescriptionISBN: 9780769549156en
heal.classificationSupport vector machinesen
heal.recordProviderΔεν υπάρχει πληροφορίαel
heal.bibliographicCitationOtukei, J.R., Blaschke, T.T., Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms (2010) Int. J Appl. Earth Observ. Geoinformation, S12, pp. S27-S31; Sanchez-Hernandez, C., Boyd, D.S., Foody, G.M., Mapping specific habitats from remotely sensed imagery: Support vector machine and support vector data description based classification of coastal saltmarsh habitats (2007) Ecol Informatics, 2, pp. 83-88; Castillejo-González, L., López-Granados, F., García-Ferrer, A., Peña-Barragán, J.M., Jurado-Expósito, M., De La Orden, M.S., González-Audicana, M., Object-and pixel-based analysis for mapping crops and their agro-environmental associated measures using QuickBird imagery (2009) Comp. Electronics Agric., 68 (2), pp. 207-215; Chintan, A.S., Arora, M.K., Pramod, K.V., Unsupervised classification of hyperspectral data: An ICA mixture model based approach (2004) Intern. J. Rem. Sens., 25, pp. 481-487; Cihlar, J., Land cover mapping of large areas from satellites: Status and research priorities (2000) Intern. J. Rem. Sens., 21, pp. 1093-1114; Carrao, H., Goncalves, P., Caetano, M., Contribution of multispectral and multitemporal information from MODIS images to land cover classification (2008) Rem. Sens. Environ., 112, pp. 986-997; Li, D.-C., Liu, C.-W., A class possibility based kernel to increase classification accuracy for small data sets using support vector machines (2010) Exp. Syst. Appl., 37, pp. 3104-3110; Du, P., Tan, K., Xing, X., Wavelet SVM in reproducing kernel Hilbert space for hyperspectral remote sensing image classification (2010) Opt. Communications, 283, pp. 4978-4984; Goodenough, D.G., Dyk, A., Niemann, O., Pearlman, J.S., Chen, H., Han, T., Murdoch, M., West, C., Processing HYPERION and ALI for forest classification (2003) . IEEE Trans. Geosci. 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Sen, 27, pp. 2895-2916; Karimi, Y., Prasher, S.O., Patel, R.M., Kim, S.H., Application of support vector machines technology for weed and nitrogen stress detection in corn (2006) . Comp Electronics Agric., 51, pp. 99-109 CORRESPONDENCE ADDRESS: Petropoulos, G.P.; Institute of Geography and Earth Sciences, University of AberystwythUnited Kingdom; email:
heal.abstractLand cover is a fundamental variable of the Earth's system intimately connected with many parts of the human and physical environment. Recent advances in remote sensor technology have led to the launch of spaceborne hyperspectral remote sensing sensors, such as Hyperion. The present study is exploring the potential of Hyperion hyperspectral imagery combined with the Spectral Angle Mapper (SAM) and Support Vectors Machine (SVMs) pixel-based classifiers in obtaining land cover cartography. A typical Mediterranean setting was selected as a case study, located close to the capital of Greece. Validation of the derived thematic maps was performed on the basis of the error matrix statistics using for consistency the same set of validation points. Both classifiers produced generally reasonable results with the SVMs however significantly outperforming the SAM in both overall classification accuracy and kappa coefficient. The higher classification accuracy by SVMs was attributed principally to the classifier ability to identify an optimal separating hyperplane for classes' separation which allows a low generalization error, thus producing the best possible classes' separation. Yet, as a shortcoming of both classifiers was that none of them operates on a sub-pixel level, that potentially reduces their accuracy as a result of spectral mixing problems that can be commonly found in coarse spatial resolution imagery and at fragmented landscapes. © 2012 IEEE.en
heal.sponsorBiological and Artificial Intelligence Foundation (BAIF)en
heal.sponsorHellenic Artificial Intelligence Society (EETN)en
heal.sponsorIEEE; University of Piraeusen
heal.sponsorUniversity of Piraeus Research Centeren
heal.conferenceNameProceedings - International Conference on Tools with Artificial Intelligence, ICTAIel
heal.type.enConference presentationen
heal.type.elΔημοσίευση σε συνέδριοel
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