Package: SITSAR 1.0.0

SITSAR: Information Theory, Geometry and Statistics for Synthetic Aperture Radar Data

Statistical models, information-theoretic measures and image analysis tools for Synthetic Aperture Radar (SAR) and polarimetric SAR (PolSAR) data. It implements intensity models (Gamma, generalized Gamma and G0I) and matrix models (scaled complex Wishart, G0 polarimetric and the compound truncated Poisson and compound geometric complex Wishart laws), maximum likelihood and expectation-maximization estimation, closed-form Shannon entropies with their asymptotic variances, entropy-based hypothesis tests and change detection, entropy-based image segmentation by (stochastic) expectation-maximization, stochastic distances and a family of divergence-based classifiers (Kullback-Leibler, Jeffreys, Bhattacharyya and Renyi) for Gaussian vector features and complex Wishart matrix features. The main tools are 'R6' classes and the classifiers are available as a 'parsnip' model, so they can be used inside 'tidymodels' workflows. Methods are described in Ferreira, Coelho and Nascimento (2021) <doi:10.1080/01431161.2020.1826060>, Ferreira and Nascimento (2020) <doi:10.1109/JSTARS.2020.2997666>, Ferreira, Nascimento and Frery (2022) <doi:10.3390/rs14205083> and Nascimento, Ferreira and Frery (2023) <doi:10.1007/s11222-023-10298-0>.

Authors:Jodavid Ferreira [aut, cre], Abraao Nascimento [aut]