Image processing

From GRASS-Wiki

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Contents 1 Introduction 2 Screenshots 3 Importing 3.1 Satellite Data 3.1.1 Ocean Color 3.1.2 Sea Surface Temperature (SST) 3.2 Orthophotos 4 Preprocessing 4.1 Geometric preprocessing/Georectification 4.2 Radiometric preprocessing 4.3 Correction for atmospheric effects 4.4 Correction for topographic/terrain effects 4.5 Cloud removal 5 Image classification 5.1 Interactive setup 5.2 Processing 5.3 Unsupervised classification 5.4 Supervised classification 6 Filtering 7 Enhancement 8 Stereo anaglyphs 9 Ideas collection for improving GRASS' Image processing capabilities 9.1 Spectral unmixing 9.2 Spectral angle mapping 9.3 Geocoding 9.4 Image classification 9.5 Stereo 9.6 Lidar LAS format 9.7 Improving the existing code

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.

• A short introduction to image processing in GRASS 6
• Full GRASS 4.0 Image Processing manual (PDF, 47 pages)
• 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
• Pathfinder AVHRR

Orthophotos

• See the i.ortho.photo modules

Preprocessing

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.

Filtering

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

Enhancement

• i.landsat.rgb
• i.fusion.brovey
• 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)
• 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.itc.it/outgoing/grass5/stereo-0.2b.tar.gz
• Stereo Tutorial

Lidar LAS format

LAS Tools by M. Isenburg: http://www.cs.unc.edu/~isenburg/lastools/

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

(see r.in.xyz)

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/i.ortho.photo/libes/: standard lib, in use (i.ortho.photo, 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