Region-based classification of PolSAR data can be effectively performed by
seeking for the assignment that minimizes a distance between prototypes and
segments. Silva et al (2013) used stochastic distances between complex
multivariate Wishart models which, differently from other measures, are
computationally tractable. In this work we assess the robustness of such
approach with respect to errors in the training stage, and propose an extension
that alleviates such problems. We introduce robustness in the process by
incorporating a combination of radial basis kernel functions and stochastic
distances with Support Vector Machines (SVM). We consider several stochastic
distances between Wishart: Bhatacharyya, Kullback-Leibler, Chi-Square,
R\'{e}nyi, and Hellinger. We perform two case studies with PolSAR images, both
simulated and from actual sensors, and different classification scenarios to
compare the performance of Minimum Distance and SVM classification frameworks.
With this, we model the situation of imperfect training samples. We show that
SVM with the proposed kernel functions achieves better performance with respect
to Minimum Distance, at the expense of more computational resources and the
need of parameter tuning. Code and data are provided for reproducibility.Comment: Accepted for publication in the International Journal of Digital
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