Image processing

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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

Orthophotos

• See the i.ortho.photo modules

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

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

• with i.landsat.acca (from GRASS AddOns)

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

Filtering

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

Enhancements

Radiometric Enhancements

• i.landsat.rgb

Geometric Enhancements

• i.rgb.his and i.his.rgb
• i.fusion.brovey

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

• i.spec.unmix (source code)

Spectral angle mapping

• i.spec.sam (source code)

Geocoding

• 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

• 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

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

Stereo

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)
  • add other warping methods (maybe thin splines from GDAL?): Addons#i.warp
  • implement/finish linewise ortho-rectification of satellite data