rasclass: Supervised Raster Image Classification

This package contains functions 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: 0.2.1
Imports: methods, car, nnet, RSNNS, e1071, randomForest
Published: 2012-01-23
Author: Daniel Wiesmann and David Quinn
Maintainer: Daniel Wiesmann <daniel.wiesmann at ist.utl.pt>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Materials: NEWS
CRAN checks: rasclass results

Downloads:

Reference manual: rasclass.pdf
Package source: rasclass_0.2.1.tar.gz
OS X binary: rasclass_0.2.1.tgz
Windows binary: rasclass_0.2.1.zip
Old sources: rasclass archive