| Summary: | (Dis)similarity measures play an important role in the interpretation of polarimetric synthetic aperture radar (PolSAR) images. Here, the authors introduce a kind of similarity measures for PolSAR images based on the concepts of Hölder pseudo-divergence and Hölder divergence. Authors’ similarity measures are more generalised as the derived formulas indicate that they contain several widely used measures, such as Bartlett or Bhattahcharyya distances and Chernoff distance. Also, their similarity measures are derived from the complex Wishart distribution, so these measures are good at quantifying the similarity between two covariance matrices and perform well while dealing with classification problems for PolSAR images. Experimental results on unsupervised and supervised classification of PolSAR images also verify the effectiveness of their measures.
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