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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)

Abstract

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.

Keywords

PolSAR Image classification Region-based Stochastic distances Kernel function 

Notes

Acknowledgments

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

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Copyright information

© 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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