Image processing

From GRASS-Wiki
Jump to navigation Jump to search

This page needs review and enhancement from an expert! Thanks!


Satellite imagery and orthophotos (aerial photographs) are handled in GRASS as raster maps and specialized tasks are performed using the imagery (i.*) modules. All general operations are handled by the raster modules.

  • Data import is generally handled by the module



Satellite Data

Ocean Color

Sea Surface Temperature (SST)


  • See the modules


Geometric preprocessing/Georectification

  • Tcl/Tk georectification tool is available from the File menu in the GUI.
  • i.points, i.vpoints (scanned maps, satellite images)
  • (aerial images)

A multi-band image may be grouped and georectified with a single set of ground control points. (,, i.rectify)

See also the Georeferencing wiki page

Radiometric preprocessing

  • use r.mapcalc to apply gain/bias formula
  • LANDSAT: you can also use i.landsat.toar from GRASS AddOns

Correction for atmospheric effects

  • i.landsat.dehaze: simple dark-object/Tasseled Cap based haze minimization (from GRASS AddOns)
  • i.atcorr: more complex correction but based on atmospheric models

Correction for topographic/terrain effects

In rugged terrain, such correction might be useful to minimize negative effects.

  • simple "cosine correction" using r.sunmask, r.mapcalc (tends to overshoot when slopes are high)
  • Minnaert or other corrections with i.topo.corr (from GRASS AddOns)

Cloud removal

Image classification

Classification methods in GRASS

supervised 1
supervised 2
radiometric & geometric
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.


  • 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.


  • i.fft, i.ifft, i.pca, r.mfilter, r.neighbors


  • i.landsat.rgb
  • i.fusion.brovey
  • i.oif

Stereo anaglyphs

Ideas collection for improving GRASS' Image processing capabilities

Below modules need some tuning before being added to GRASS 6. Volunteers welcome.

Spectral unmixing

Spectral angle mapping


  • automated search of GCPs based on FFT correlation (as improved i.points)
  • i.homography: geocoding with lines (instead of points) with homography (as improved i.points; it was formerly called i.linespoints)
  • support splines from GDAL (see GRASS_AddOns#Imagery_add-ons)

Image classification


This is stand-alone stereo modeling software (DEM extraction etc). Waits for integration into GRASS.

Lidar LAS format

LAS Tools by M. Isenburg:

   las2txt | in=- fs=" "


Improving the existing code

It might be sensible to merge the various image libraries:

  • GRASS 6 standard libs:
    • lib/imagery/: standard lib, in use (i.* except for i.points3, i.rectify3)
    • imagery/ standard lib, in use (, photo.*)
  • GRASS 5 (! only) image3 lib:
    • lib/image3/: never finished improvement which integrated the standard lib and the ortho lib. Seems to provide also ortho rectification for satellite data (i.points3, i.rectify3)
  • GRASS 5/6 image proc commands:
    • merge of i.points, i.vpoints, i.points3
    • merge of i.rectify and i.rectify3
    • addition of new resampling algorithms such as bilinear, cubic convolution (take from r.proj or upcoming r.resamp.aggreg)
    • add other warping methods (maybe thin splines from GDAL?)
    • implement/finish linewise ortho-rectification of satellite data