Package: SurfRough 0.0.1.0

SurfRough: Calculate Surface/Image Texture Indexes

Methods for the computation of surface/image texture indices using a geostatistical based approach (Trevisani et al. (2023) <doi:10.1016/j.geomorph.2023.108838>). It provides various functions for the computation of surface texture indices (e.g., omnidirectional roughness and roughness anisotropy), including the ones based on the robust MAD estimator. The kernels included in the software permit also to calculate the surface/image texture indices directly from the input surface (i.e., without de-trending) using increments of order 2. It also provides the new radial roughness index (RRI), representing the improvement of the popular topographic roughness index (TRI). The framework can be easily extended with ad-hoc surface/image texture indices.

Authors:Sebastiano Trevisani [aut, cre], Ilich Alexander [ctb]

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SurfRough.pdf |SurfRough.html
SurfRough/json (API)

# Install 'SurfRough' in R:
install.packages('SurfRough', repos = c('https://strevisani.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/strevisani/surfrough/issues

Datasets:
  • k05ck2 - Basic kernels
  • k1c - Basic kernels
  • k1ck2 - Basic kernels
  • k2c - Basic kernels
  • k2ck2 - Basic kernels
  • k4c - Basic kernels
  • k6c - Basic kernels
  • k8c - Basic kernels

On CRAN:

8 exports 1 stars 1.25 score 2 dependencies 113 downloads

Last updated 12 days agofrom:b3f2c6ffa9. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 06 2024
R-4.5-winOKSep 06 2024
R-4.5-linuxOKSep 06 2024
R-4.4-winOKSep 06 2024
R-4.4-macOKSep 06 2024
R-4.3-winOKSep 06 2024
R-4.3-macOKSep 06 2024

Exports:circularDispersionGVcircularDispersionNVKernelCircularKernelRectangularMadscanMeanscanRRITrik2

Dependencies:Rcppterra