Image classification
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.
- i.gensig - Generates statistics for i.maxlik from raster map layer.
- i.gensigset - Generate statistics for i.smap from raster map layer.
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
- Micha Silver: Analyzing acacia tree health in the Arava with GRASS GIS
- Perrygeo: Impervious surface deliniation with GRASS
- Dylan Beaudette: Working with Landsat Data
- Dylan Beaudette: Canopy Quantification via Image Classification