Image processing

Introduction

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.

• imageryintro: A short introduction to image processing in GRASS 6
• Full GRASS 4.0 Image Processing manual (PDF, 47 pages)
• imagery: Imagery module help pages

• Data import is generally handled by the r.in.gdal module

Screenshots

• The imagery screenshots page

Importing

Satellite Data

Ocean Color

• MODIS
• SeaWiFS

Sea Surface Temperature (SST)

• MODIS
• AVHRR

High resolution data

• QuickBird

Orthophotos

• see Orthorectification
• see Ortho-rectification of oblique photographs

Preprocessing

See Satellite Overpass Predictor

Geometric preprocessing/Georectification

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

A multi-band image may be grouped and georectified with a single set of ground control points (i.group, i.target, 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 - see tutorial: Atmospheric correction

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)
• In i.topo.corr (from GRASS AddOns) the following correction methods are implemented: cosine, minnaert, percent, c-factor.

Cloud removal

• with i.landsat.acca

Image classification

See the dedicated Image classification page.

Filtering

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

Texture

• r.texture: In case of panchromatic maps or limited amount of channels, it is often recommended to generate synthetic channels through texture analysis

Enhancements

Radiometric Enhancements

• i.landsat.rgb

Geometric Enhancements

• i.rgb.his and i.his.rgb: can be used for image fusion
• i.fusion.brovey: image fusion of pan-chromatic and color channels
• r.seg which performs image segmentation and discontinuity detection (based on the Mumford-Shah variational model). The module generates a piece-wise smooth approximation of the input raster map and a raster map of the discontinuities of the output approximation. The discontinuities of the output approximation are preserved from being smoothed.

Optimal channel selection for color composites

• i.oif

Stereo anaglyphs

• see 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 ideas

• i.spec.unmix

Spectral angle mapping ideas

• i.spec.sam

Geocoding ideas

• 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)
• New Georectifier: see also http://gama.fsv.cvut.cz/~landa/grass/swf/georect.html

Image matching ideas

• i.points.auto: automated search of GCPs based on FFT correlation (as improved i.points)

Reference: M. Neteler, D. Grasso, I. Michelazzi, L. Miori, S. Merler, and C. Furlanello, 2005: An integrated toolbox for image registration, fusion and classification. International Journal of Geoinformatics, 1(1), pp. 51-61 PDF

Image classification ideas

• pr: C code for classification problems
• GRASS implementation: i.pr.* source code is available here)

Stereo ideas

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

• http://grass.osgeo.org/outgoing/grass5/stereo-0.2b.tar.gz
• Stereo Tutorial

Lidar LAS format

LAS Tools by M. Isenburg, Howard Butler et al.: http://www.liblas.org

   las2txt | r.in.xyz in=- fs=" "

(see LIDAR)

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, see below)
  • imagery/i.ortho.photo/libes/: standard lib, in use (i.ortho.photo, photo.*)
• GRASS 5 (! only) image3 lib:
  • libes/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 (see above)
  • merge of i.rectify and i.rectify3 (see above)
  • addition of new resampling algorithms such as bilinear, cubic convolution (take from r.proj or r.resamp.aggreg) (done 10/2010)
  • add other warping methods (maybe lanczos or thin splines from GDAL?): Addons#i.warp
  • implement/finish linewise ortho-rectification of satellite data

Bibliography

• Search for "GRASS GIS Image processing" - Google Scholar
• Search for "GRASS GIS Remote Sensing" - Google Scholar