[R-sig-Geo] New package > RStoolbox: Remote Sensing Data Analysis in R

[Tim Appelhans]

Benjamin,
I think we should put our heads together at some stage as we have 
already something very similar on CRAN (released in July)

https://cran.r-project.org/web/packages/satellite/index.html

The satellite package provides standard class(es) and methods for 
handling satellite data. We deliberately avoided adding any 
functionality that goes beyond basic image preparation (i.e. 
classification or prediction capabilities). The idea is to provide a 
standard package for remote sensing data handling (satellite) on top of 
which other packages can be built to provide further functionality (such 
as the numerous classification algorithms in RStoolbox). Think of it as 
the 'sp' package for satellite remote sensing data analysis.

To get a better idea of how satellite is designed please have a look at 
the following vignette (which is not quite finished but does provide an 
overview of how the 'satellite' class is defined).

https://github.com/environmentalinformatics-marburg/satellite/blob/develop/vignettes/satellite.Rmd

I would appreciate your thoughts (maybe off-list) on how to best arrange 
our efforts in order to avoid duplication and confusion for other users.

Best
Tim

On 14.09.2015 08:36, Benjamin Leutner wrote:
> Dear list members,
>
> We are happy to announce the initial release of our *RStoolbox* package.
> RStoolbox provides various tools for remote sensing data analysis and 
> is now available from CRAN:
>    https://cran.r-project.org/web/packages/RStoolbox
>
> The main focus of RStoolbox is to provide a set of high-level remote 
> sensing tools for various classification tasks. This includes 
> unsupervised and supervised classification with different classifiers, 
> fractional cover analysis and a spectral angle mapper. Furthermore, 
> several spectral transformations like vegetation indices, principal 
> component analysis or tasseled cap transformation are available as well.
>
> Besides that, we provide a set of data import and pre-processing 
> functions. These include reading and tidying Landsat meta-data, 
> importing ENVI spectral libraries, histogram matching, automatic image 
> co-registration, topographic illumination correction and so on.
>
> Last but not least, RStoolbox ships with two functions dedicated to 
> plotting remote sensing data (*raster* objects) with *ggplot2* 
> including RGB color compositing with various contrast stretching options.
>
> RStoolbox is built on top of the *raster* package. To improve 
> performance some functions use embedded C++ code via the *Rcpp* 
> package. Moreover, most functions have built-in support for parallel 
> processing, which is activated by running raster::beginCluster() 
> beforehand
>
> RStoolbox is hosted at www.github.com/bleutner/RStoolbox
>
> For a more details, including executed examples, please see
> http://bleutner.github.io/RStoolbox/rstbx-docu/
>
> We sincerely hope that this package may be helpful for some people and 
> are looking forward to any feedback, suggestions and bug reports.
>

-- 
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Tim Appelhans
Department of Geography
Environmental Informatics
Philipps Universität Marburg
Deutschhausstraße 12
35032 Marburg (Paketpost: 35037 Marburg)
Germany

Tel +49 (0) 6421 28-25957

http://environmentalinformatics-marburg.de/