Calculate Surface/Image Texture Indexes

Methods for the computation of surface/image texture indices using a geostatistical based approach (Trevisani et al. (2023) and Trevisani and Guth (2025) ). 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 and of order 4. 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.

README

Sebastiano Trevisani

July 29, 2024

SurfRough

Algorithms for surface texture (roughness) and image texture analysis using a geostatistical based approach from https://doi.org/10.1016/j.catena.2023.106927

Installation

Update 6 Sept. 2024 The package has been published in Cran repository: https://cran.r-project.org/package=SurfRough So you can install it with the usual R method (install.packages())

To install this package first install the remotes package if you do not have it using the code install.packages("remotes"). Then this package can be installed from github using the code remotes::install_github("strevisani/SurfRough"). This will install SurfRough and its dependencies such as terra which is used for the handling of spatial raster data.