Region-Based Classification of PolSAR Data Through Kernel Methods and Stochastic Distances

  • Rogério G. Negri
  • Wallace C. O. Casaca
  • Erivaldo A. Silva
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10657)


Stochastic distances combined with Minimum Distance method for region-based classification of Polarimetric Synthetic Aperture Radar (PolSAR) image was successfully verified in Silva et al. (2013). Methods like K-Nearest Neighbors may also adopt stochastic distances and then used in a similar purpose. The present study investigates the use of kernel methods for PolSAR region-based classification. For this purpose, the Jeffries-Matusita stochastic distance between Complex Multivariate Wishart distributions is integrated in a kernel function and then used in Support Vector Machine and Graph-Based kernel methods. A case study regarding PolSAR remote sensing image classification is carried to assess the above mentioned methods. The results show superiority of kernel methods in comparison to the other analyzed methods.


PolSAR Image classification Region-based Stochastic distances Kernel function 



The authors thank FAPESP (Proc.: 2014/14830-8) for funding this research.


  1. 1.
    Camara, G., Souza, R.C.M., Ii, F.M., Freitas, U., Garrido, J.: Spring: integrating remote sensing and GIS by object-oriented data modelling. Comput. Graph. 20, 3 (1996)CrossRefGoogle Scholar
  2. 2.
    Camps-Valls, G., Tatyana, V.B., Zhou, D.: Semi-supervised graph-based hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 45, 2044–3054 (2007)CrossRefGoogle Scholar
  3. 3.
    Cloude, S.R., Pottier, E.: An entropy based classification scheme for land applications of polarimetric SAR. IEEE Trans. Geosci. Remote Sens. 35(1), 68–78 (1997)CrossRefGoogle Scholar
  4. 4.
    Congalton, R.G., Green, K.: Assessing the Accuracy of Remotely Sensed Data. CRC Press, Boca Raton (2009)Google Scholar
  5. 5.
    Frery, A.C., Nascimento, A.D.C., Cintra, R.J.: Analytic expressions for stochastic distances between relaxed complex Wishart distributions. IEEE Trans. Geosci. Remote Sens. 52(2), 1213–1226 (2014)CrossRefGoogle Scholar
  6. 6.
    Lee, J., Grunes, M., Kwok, R.: Classification of multi-look polarimetric SAR imagery based on complex Wishart distribution. Int. J. Remote Sens. 15(11), 2299–2311 (1994)CrossRefGoogle Scholar
  7. 7.
    Negri, R.G., Dutra, L.V., Sant’Anna, S.J.S., Lu, D.: Examining region-based methods for land cover classification using stochastic distances. Int. J. Remote Sens. 37(8), 1902–1921 (2016). CrossRefGoogle Scholar
  8. 8.
    Schölkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Adaptive Computation and Machine Learning. MIT Press, Cambridge (2002)Google Scholar
  9. 9.
    Silva, W.B., Freitas, C.C., Sant’Anna, S.J.S., Frery, A.C.: Classification of segments in PolSAR imagery by minimum stochastic distances between Wishart distributions. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 6(3), 1263–1273 (2013)CrossRefGoogle Scholar
  10. 10.
    Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer-Verlag New York Inc., New York (1995)CrossRefMATHGoogle Scholar

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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Instituto de Ciência e TecnologiaUniv. Estadual PaulistaSão José dos CamposBrazil
  2. 2.Campus Experimental de RosanaUniv. Estadual PaulistaRosanaBrazil
  3. 3.Faculdade de Ciência e TecnologiaUniv. Estadual PaulistaPresidente PrudenteBrazil

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