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

Benjamin Leutner benjamin.leutner at uni-wuerzburg.de
Wed Sep 16 10:36:52 CEST 2015


@Tim: good to know of your package. I am checking it out right now and 
will get back to you off-list.
The satellite class seems to be exactly what we need and I think we 
could build on top of it.

@Sarah: of course we're aware of the landsat package. However, our 
students working with smaller workstations frequently encountered memory 
limitations due to the lack of raster support. Therefore we added a few 
functions which could be achieved similarly with SpatialPixelsDataFrames 
and the landsat package if you had enough memory.


On 15.09.2015 19:23, Sarah Goslee wrote:
> And also the landsat package, also on CRAN.
> Sarah
> On Tue, Sep 15, 2015 at 1:11 PM, Tim Appelhans <tim.appelhans at gmail.com> wrote:
>> 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.
>> --
>> #####################################
>> 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/

Benjamin Leutner M.Sc.

Department of Remote Sensing
University of Wuerzburg
Campus Hubland Nord 86
97074 Wuerzburg, Germany

Tel: +49-(0)931-31 89594
Fax: +49-(0)931-31 89594-0
Email: benjamin.leutner at uni-wuerzburg.de
Web: http://www.fernerkundung.uni-wuerzburg.de

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