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Software to perform supervised and pixel based raster image classification. It has been designed to facilitate land-cover analysis. Five classification algorithms can be used: Maximum Likelihood Classification, Multinomial Logistic Regression, Neural Networks, Random Forests and Support Vector Machines. The output includes the classified raster and standard classification accuracy assessment such as the accuracy matrix, the overall accuracy and the kappa coefficient. An option for in-sample verification is available.
| Version: | 0.2.2 |
| Imports: | methods, car, nnet, RSNNS, e1071, randomForest |
| Published: | 2016-05-02 |
| DOI: | 10.32614/CRAN.package.rasclass |
| Author: | Daniel Wiesmann and David Quinn |
| Maintainer: | Daniel Wiesmann <daniel.wiesmann at tecnico.ulisboa.pt> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| Materials: | NEWS |
| CRAN checks: | rasclass results |
| Reference manual: | rasclass.pdf |
| Package source: | rasclass_0.2.2.tar.gz |
| Windows binaries: | r-devel: rasclass_0.2.2.zip, r-release: rasclass_0.2.2.zip, r-oldrel: rasclass_0.2.2.zip |
| macOS binaries: | r-release (arm64): rasclass_0.2.2.tgz, r-oldrel (arm64): rasclass_0.2.2.tgz, r-release (x86_64): rasclass_0.2.2.tgz, r-oldrel (x86_64): rasclass_0.2.2.tgz |
| Old sources: | rasclass archive |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.