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>.

Version: 1.0.0
Depends: R (≥ 4.1.0)
Imports: generics, graphics, grDevices, parsnip (≥ 1.0.0), R6 (≥ 2.5.0), rlang, stats, tibble, utils
Suggests: dials, knitr, recipes, rmarkdown, rsample, testthat (≥ 3.0.0), tune, workflows, yardstick
Published: 2026-11-06
DOI: 10.32614/CRAN.package.SITSAR (may not be active yet)
Author: Jodavid Ferreira ORCID iD [aut, cre], Abraao Nascimento ORCID iD [aut]
Maintainer: Jodavid Ferreira <jodavid at protonmail.com>
BugReports: https://github.com/jodavid/SITSAR/issues
License: MIT + file LICENSE
URL: https://github.com/jodavid/SITSAR, https://jodavid.github.io/SITSAR/
NeedsCompilation: no
Language: en-US
Citation: SITSAR citation info
Materials: README, NEWS
CRAN checks: SITSAR results

Documentation:

Reference manual: SITSAR.html , SITSAR.pdf
Vignettes: Getting started with SITSAR (source, R code)
Divergence-based classification with tidymodels (source, R code)

Downloads:

Package source: SITSAR_1.0.0.tar.gz
Windows binaries: r-devel: not available, r-release: not available, r-oldrel: not available
macOS binaries: r-release (arm64): not available, r-oldrel (arm64): not available, r-release (x86_64): not available, r-oldrel (x86_64): not available

Linking:

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