Region-based classification of PolSAR data through kernel methods and stochastic distances
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Data
2018-01-01
Autores
Negri, Rogério G. [UNESP]
Casaca, Wallace C. O. [UNESP]
Silva, Erivaldo A. [UNESP]
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Resumo
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.
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Image classification, Kernel function, PolSAR, Region-based, Stochastic distances
Como citar
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 10657 LNCS, p. 433-440.