Image classification

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Image classification

Classification methods in GRASS

radiometric
unsupervised
radiometric
supervised 1
radiometric
supervised 2
radiometric & geometric
supervised
Preprocessing i.cluster i.class (monitor digitizing) i.gensig (using training maps) i.gensigset (using training maps)
Computation i.maxlik i.maxlik i.maxlik i.smap

Interactive setup

  • i.class - Generates spectral signatures for an image by allowing the user to outline regions of interest.
The resulting signature file can be used as input for i.maxlik or as a seed signature file for i.cluster.

Processing

  • 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.

Unsupervised classification

  • i.maxlik - Classifies the cell spectral reflectances in imagery data.
Classification is based on the spectral signature information generated by either i.cluster, i.class, or i.gensig.

Supervised classification

  • i.smap - Performs contextual (image segmentation) image classification using sequential maximum a posteriori (SMAP) estimation.

Further reading classification with GRASS