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Original Articles

Spatial location and ecological content of support vectors in an SVM classification of tropical vegetation

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Pages 686-695 | Received 14 Jan 2013, Accepted 08 Mar 2013, Published online: 15 Apr 2013

References

  • Andréfouët, S. and Leroux, L., 1998, Characterization of ecotones using membership degrees computed with a fuzzy classifier. International Journal of Remote Sensing, 19, pp. 3205–3211.
  • Benediktsson, J.A., Swain, P.H. and Ersoy, O.K., 1990, Neural network approaches versus statistical methods in classification of multisource remote sensing data. IEEE Transactions on Geoscience and Remote Sensing, 28, pp. 540–552.
  • Boyd, D.S. and Foody, G.M., 2011, An overview of recent remote sensing and GIS based research in ecological informatics. Ecological Informatics, 6, pp. 25–36.
  • Burges, C.J.C., 1998, A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2, pp. 121–167.
  • Camps-Valls, G., Gomez-Chova, L., Calpe-Maravilla, J., Martin-Guerrero, J.D., Soria-Olivas, E., Alonso-Chorda, L. and Moreno, J., 2004, Robust support vector method for hyperspectral data classification and knowledge discovery. IEEE Transactions on Geoscience and Remote Sensing, 42, pp. 1530–1542.
  • Candade, N., 2004, Multispectral classification of Landsat images: a comparison of support vector machine and neural network classifiers. In ASPRS Annual Conference Proceedings, 23–28 May, Denver, CO.
  • Chang, C.-C. and Lin, C.-J., 2001, LIBSVM: a library for support vector machines. Available online at: http://www.csie.ntu.edu.tw/~cjlin/libsvm/ (27 March 2013).
  • Chi, M., Feng, R. and Bruzzone, L., 2008, Classification of hyperspectral remote-sensing data with primal SVM for small-sized training dataset problem. Advances in Space Research, 41, pp. 1793–1799.
  • Eitrich, T. and Lang, B., 2006, Efficient optimization of support vector machine learning parameters for unbalanced datasets. Journal of Computational and Applied Mathematics, 196, pp. 425–436.
  • Foody, G.M. and Mathur, A., 2004, A relative evaluation of multiclass image classification of support vector machines. IEEE Transactions on Geoscience and Remote Sensing, 42, pp. 1335–1343.
  • Foody, G.M. and Mathur, A., 2006, The use of small training sets containing mixed pixels for accurate hard image classification: training on mixed spectral responses for classification by a SVM. Remote Sensing of Environment, 103, pp. 179–189.
  • Hatton, T.J., Salvucci, G.D. and Wu, H.I., 1997, Eagleson’s optimality theory of an ecohydrological equilibrium: quo vadis? Functional Ecology, 11, pp. 665–674.
  • Hsu, C.-W., Chang, C.-C. and Lin, C.-J., 2010, A practical guide to support vector classification. Department of Computer Science, National Taiwan University, Taipei. Available online at: http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf (27 March 2013).
  • Huang, C., Davis, L.S. and Townshend, J.R.G., 2002, An assessment of support vector machines for land cover classification. International Journal of Remote Sensing, 23, pp. 725–749.
  • Lesparre, J. and Gorte, B.G.H., 2006, Using mixed pixels for the training of a maximum likelihood classification. In Proceedings of the ISPRS Committee VII Mid-term Symposium, from Pixels to Processes, 8–11 May, Enschede, The Netherlands, pp. 632–637.
  • Lieng, E., Vikhamar Schuler, D., Kastdalen, L., Fjone, G., Hansen, M. and Bolstad, J.P., 2005, Classification of land cover using decision trees and multiple references data sources. In Proceedings of International Symposium of Remote Sensing of Environment. 20–24 June, St. Petersburg, Russia.
  • Mantero, P., Moser, G. and Serpico, S.B., 2005, Partially supervised classification of remote sensing images through SVM-based probability density estimation. IEEE Transactions on Geoscience and Remote Sensing, 43, pp. 559–570.
  • Mas, J.F. and Flores, J.J., 2008, The application of artificial neural networks to the analysis of remotely sensed data. International Journal of Remote Sensing, 29, pp. 617–663.
  • Mathur, A. and Foody, G.M., 2008, Crop classification by support vector machine with intelligent selected training data for an operational application. International Journal of Remote Sensing, 29, pp. 2227–2240.
  • Mountrakis, G., Im, J. and Ogole, C., 2011, Support vector machines in remote sensing: a review. ISPRS Journal of Photogrammetry and Remote Sensing, 66, pp. 247–259.
  • Pal, M. and Mather, P.M., 2005, Support vector machines for classification in remote sensing. International Journal of Remote Sensing, 5, pp. 1007–1011.
  • Pal, M. and Mather, P.M., 2006, Some issues in the classification of DAIS hyperspectral data. International Journal of Remote Sensing, 27, pp. 2895–2916.
  • Schölkopf, B. and Smola, A.J., 2002, Learning with Kernels, 626pp (Cambridge, MA: MIT Press).
  • Townshend, J.R.G., Huang, C., Kalluri, S.N.V., Defries, R.S. and Liang, S., 2000, Beware a per-pixel characterization of land cover. International Journal of Remote Sensing, 21, pp. 839–843.
  • Turner, W., Spector, S., Gardiner, N., Fladeland, M., Sterling, E. and Steininger, M., 2003, Remote sensing for biodiversity science and conservation. Trends in Ecology and Evolution, 18, pp. 306–314.
  • Vapnik, V., 1998, Statistical Learning Theory, 736pp (New York: John Wiley).

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