Negri, Rogério G. [UNESP]Casaca, Wallace C. O. [UNESP]Silva, Erivaldo A. [UNESP]2022-04-292022-04-292018-01-01Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 10657 LNCS, p. 433-440.1611-33490302-9743http://hdl.handle.net/11449/228509Stochastic 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.433-440engImage classificationKernel functionPolSARRegion-basedStochastic distancesTrabalho apresentado em evento10.1007/978-3-319-75193-1_522-s2.0-85042224492