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Topic: classification

Tool Description
i.cluster Generates spectral signatures for land cover types in an image using a clustering algorithm. The resulting signature file is used as input for i.maxlik, to generate an unsupervised image classification.
i.gensig Generates statistics for i.maxlik from raster map.
i.gensigset Generates statistics for i.smap from raster map.
i.maxlik Classifies the cell spectral reflectances in imagery data. Classification is based on the spectral signature information generated by either i.cluster, g.gui.iclass, or i.gensig.
i.signatures Manage imagery classification signature files
i.smap Performs contextual image classification using sequential maximum a posteriori (SMAP) estimation.
v.class Classifies attribute data, e.g. for thematic mapping

See also the corresponding keyword for additional references:

classification

  • g.gui.iclass - wxGUI Supervised Classification Tool
  • i.cluster - Generates spectral signatures for land cover types in an image using a clustering algorithm. The resulting signature file is used as input for i.maxlik, to generate an unsupervised image classification.
  • i.gensig - Generates statistics for i.maxlik from raster map.
  • i.gensigset - Generates statistics for i.smap from raster map.
  • i.maxlik - Classifies the cell spectral reflectances in imagery data. Classification is based on the spectral signature information generated by either i.cluster, g.gui.iclass, or i.gensig.
  • i.segment - Identifies segments (objects) from imagery data.
  • i.signatures - Manage imagery classification signature files
  • i.smap - Performs contextual image classification using sequential maximum a posteriori (SMAP) estimation.
  • i.svm.predict - Predict with a SVM Predict with a Support Vector Machine
  • i.svm.train - Train a SVM Train a Support Vector Machine
  • r.confusionmatrix - Calculates a confusion matrix and accuracies for a given classification using r.kappa.
  • r.kappa - Calculates error matrix and kappa parameter for accuracy assessment of classification result.
  • r.learn.ml - Supervised classification and regression of GRASS rasters using the python scikit-learn package
  • r.learn.predict - Apply a fitted scikit-learn estimator to rasters in a GRASS GIS imagery group.
  • r.learn.train - Supervised classification and regression of GRASS rasters using the python scikit-learn package.
  • r.terrain.texture - Unsupervised nested-means algorithm for terrain classification
  • v.class - Classifies attribute data, e.g. for thematic mapping
  • v.class.mlR - Provides supervised support vector machine classification
  • v.class.mlpy - Vector supervised classification tool which uses attributes as classification parametres (order of columns matters, names not), cat column identifies feature, class_column is excluded from classification parametres.
  • v.lidar.mcc - Reclassifies points of a LiDAR point cloud as ground / non-ground using a multiscale curvature based classification algorithm.
  • wxGUI Supervised Classification Tool - wxGUI Supervised Classification Tool