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


 Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/7258
Land 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.
Institution and School/Department of submitter: Δεν υπάρχει πληροφορία
Subject classification: Support vector machines
Keywords: Greece;Hyperion;Land cover mapping;Spectral angle mappers;Support vector machine (SVMs);Artificial intelligence;Error statistics;Mapping;Maps;Remote sensing;Separation;Spectroscopy;Support vector machines
URI: http://hdl.handle.net/123456789/7258
Item type: conferenceItem
General Description / Additional Comments: art. no. 6495629, pp. 26-31.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84876837966&partnerID=40&md5=e353e2a09acbea4124e1ddbc633f39df
Institute of Geography and Earth Sciences, University of Aberystwyth, United Kingdom
Department of Natural Resources Management and Agricultural Engineering, Agricultural University of Athens, Athens, Greece
Department of Automation, Technological Education Institute of Piraeus, Athens, Greece
Department of Sciences, Technological Education Institute of Kavala, Kavala, Greece
ISSN: 10823409
ISBN: 9780769549156
Subject classification: Support vector machines
???metadata.heal.dateAvailable???: 2015-11-27T09:54:24Z
Item language: en
Item access scheme: free
Institution and School/Department of submitter: Δεν υπάρχει πληροφορία
Publication date: 2012
Bibliographic citation: Otukei, 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. Remote Sensing, 41 (2), pp. 1321-1331; Galvao, L.S., Dar, A., Roberts, A.R., Formaggio, I., Numata, F., Breunig, M., View angle effects on the discrimination of soybean varieties and on the relationships between vegetation indices and yield using off-nadir Hyperion data (2009) . Rem. Sens. Environ., 113, pp. 846-856; Wang, J., Chen, Y., He, T., Lv, C., Liu, A., Application of geographic image cognition approach in land type classification using Hyperion image: A case study in China (2010) . Int. J. Applied Earth Observ. Geoinformaiton, 12 S, pp. S212-S222; Elatawneh, A., Kalaitzidis, C., Petropoulos, G.P., Schneider, T., Evaluation of diverse classification approaches for land use/cover mapping in a Mediterranean region utilizing Hyperion data (2012) Int. J.Digital Earth, pp. 1-23; Walsh, S.J., McCleary, A.L., Mena, C.F., Shao, Y., Tuttle, J.P., González, A., Atkinson, R., QuickBird and Hyperion data analysis of an invasive plant species in the Galapagos Islands of Ecuador: Implications for control and land use management (2008) . Remote Sens. Environ., 112, pp. 1927-1941; Pignatti, S., Cavalli, R.M., Cuomo, V., Fusilli, V., Pascucci, S., Poscolieri, M., Santini, F., Evaluating Hyperion capability for land cover mapping in a fragmented ecosystem: Pollino National Park, Italy (2009) . Rem. Sens. Environ., 113, pp. 622-634; Kruse, F.A., Lefkoff, A.B., Boardam, J.W., Heidebrecht, K.B., Shapiro, A.T., Barloon, J.P., Goetz, A.F.H., The spectral image processing system (SIPS)-Interactive visualization and analysis of imaging spectrometer data (1993) . Rem. Sens. Environ., 44, pp. 145-165; Vapnik, V., (1995) The Nature of Statistical Learning Theory, , Springer-Verlag, New York, NY; EO1 User Guide, , http://eo1.usgs.gov/userGuide/index.php?page=program; Pengra, B.W., Johnston, C.A., Loveland, T.R., Mapping and invasive plant, Phragmites australis, in coastal wetlands using EO-1 Hyperion hyperspectral sensor (2007) Rem. Sens. Environ.., 108, pp. 74-81; Foody, G.M., Mather, A., A relative evaluation of multiclass image (2004) Geosci. Rem. Sensing, 42, pp. 1335-1343; Huang, C., Song, K., Kim, S., Townshend, J.R.G., Davis, P., Masek, J.G., Goward, S.N., Use of dark object concept and support vector machines to automate forest cover change analysis (2008) . Rem. Sens. Environ., 112, pp. 970-985; Petropoulos, G.P., Kontoes, C., Keramitsoglou, I., Burnt area delineation from a uni-temporal perspective based on Landsat TM imagery classification using support vector machines Intern. J. Applied Earth Observ. & Geoinf, , in press; Kuemmerle, T., Chaskovskyy, O., Knorn, J., Radeloff, V.C., Kruhlov, I., Forest cover change and illegal logging in the Ukranian Carpathians in the transition period from 1988 to 2007 (2009) . Rem. Sen. Environ., 113, pp. 1194-1207; (2008) ENVI User's Manual, ITT Visual Information Solutions, , ENVI User's Guide; Petropoulos, G.P., Vadrevu, K.P., Xanthopoulos, G., Karantounias, G., Scholze, M., A comparison of spectral angle mapper and artificial neural network classifiers combined with landsat TM imagery analysis for obtaining burnt area mapping (2010) . Sensors, 10, pp. 1967-1985; Congalton, R., Green, K., (1999) Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, , CRC/Lewis Press, Boca Raton, FL; Pal, M., Mather, P.M., Some issues in the classification of DAIS hyperspectral data (2005) . Int. J. Rem. 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: george.petropoulos@aber.ac.uk
Abstract: Land 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.
Sponsor: Biological and Artificial Intelligence Foundation (BAIF)
Hellenic Artificial Intelligence Society (EETN)
IEEE; University of Piraeus
University of Piraeus Research Center
Conference name: Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI
Type of the conference item: poster
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