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RStoolbox

RStoolbox is an R package providing a wide range of tools for your every-day remote sensing processing needs. The available tool-set covers many aspects from data import, pre-processing, data analysis, image classification and graphical display. RStoolbox builds upon the raster package, which makes it suitable for processing large data-sets even on smaller workstations. Moreover in most parts decent support for parallel processing is implemented.

For more details have a look at the functions overview.

Installation

The package is available on CRAN and can be installed as usual via

install.packages("RStoolbox")

To install the latest version from GitHub you need to have r-base-dev (Linux) or Rtools (Windows) installed. Then run the following lines:

library(devtools)
install_github("bleutner/RStoolbox")

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Version

Install

install.packages('RStoolbox')

Monthly Downloads

2,047

Version

0.3.0

License

GPL (>= 3)

Issues

Pull Requests

Stars

Forks

Repository

https://github.com/bleutner/RStoolbox

Homepage

https://bleutner.github.io/RStoolbox/

Maintainer

Benjamin Leutner

Last Published

March 7th, 2022

Functions in RStoolbox (0.3.0)

ImageMetaData Class

Decode QA flags to bit-words

coregisterImages

Image to Image Co-Registration based on Mutual Information

Estimate Image Haze for Dark Object Subtraction (DOS)

Fractional Cover Analysis

cloudShadowMask

Cloud Shadow Masking for Flat Terrain

Encode QA Conditions to Integers

Classify Landsat QA bands

RStoolbox: A Collection of Remote Sensing Tools

Simple Cloud Detection

Create ggplot2 Raster Plots with RGB from 3 RasterLayers

Normalize Raster Images: Center and Scale

One-hot encode a raster or vector

Extract validation results from superClass objects

Extract bandwise information from ImageMetaData

Fortify method for classes from the raster package.

Multi-layer Pixel Entropy

Change Vector Analysis

Principal Component Analysis for Rasters

Plot RasterLayers in ggplot with greyscale

Tidy import tool for EarthExplorer .csv export files

Pan Sharpen Imagery / Image Fusion

Pseudo-Invariant Features based Image Matching

Read ENVI spectral libraries

Supervised Classification

Image to Image Contrast Matching

Read Landsat MTL metadata files

Tasseled Cap Transformation

Import separate Landsat files into single stack

SRTM Digital Elevation Model

Set global options for RStoolbox

Save and Read RStoolbox Classification Results

Topographic Illumination Correction

Spectral Angle Mapper

Unsupervised Classification

spectralIndices

Spectral Indices

Landsat 5TM Example Data

Linear Image Rescaling

predict.unsuperClass

Predict a raster map based on a unsuperClass model fit.

Multiple Endmember Spectral Mixture Analysis (Spectral Unmixing)

Radiometric Calibration and Correction

Rlogo as RasterBrick

Map accuracy assessment

Write ENVI spectral libraries