NAME

       i.oif  - Calculates Optimum-Index-Factor table for spectral bands

KEYWORDS

       imagery, multispectral, statistics

SYNOPSIS

       i.oif
       i.oif --help
       i.oif  [-gs]  input=name[,name,...]   [output=name]    [--overwrite]   [--help]   [--verbose]   [--quiet]
       [--ui]

   Flags:
       -g
           Print in shell script style

       -s
           Process bands serially (default: run in parallel)

       --overwrite
           Allow output files to overwrite existing files

       --help
           Print usage summary

       --verbose
           Verbose module output

       --quiet
           Quiet module output

       --ui
           Force launching GUI dialog

   Parameters:
       input=name[,name,...] [required]
           Name of input raster map(s)

       output=name
           Name for output file (if omitted or "-" output to stdout)

DESCRIPTION

       i.oif calculates the Optimum Index Factor for multi-spectral satellite imagery.

       The Optimum Index Factor (OIF) determines the  three-band  combination  that  maximizes  the  variability
       (information)  in a multi-spectral scene. The index is a ratio of the total variance (standard deviation)
       within and the correlation between all possible band combinations. The bands that  comprise  the  highest
       scoring combination from i.oif are used as the three color channels required for d.rgb or r.composite.

       The analysis is saved to a file in the current directory called "i.oif.result".

NOTES

       Landsat 1-7 TM: Colour Composites in BGR order as important Landsat TM band combinations (example: 234 in
       BGR order means: B=2, G=3, R=4):

           •   123: near natural ("true") colour; however, because of correlation of  the  3  bands  in  visible
               spectrum, this combination contains not much more info than is contained in single band.

           •   234:  sensitive  to green vegetation (portrayed as red), coniferous as distinctly darker red than
               deciduous forests. Roads and water bodies are clear.

           •   243: green vegetation is green but coniferous forests aren’t as clear as the 234 combination.

           •   247: one of the best for info pertaining to forestry. Good for operation scale mapping of  recent
               harvest areas and road construction.

           •   345:  contains one band from each of the main reflective units (vis, nir, shortwave infra). Green
               vegetation is green and the shortwave band shows vegetational stress  and  mortality.  Roads  are
               less evident as band 3 is blue.

           •   347: similar to 345 but depicts burned areas better.

           •   354: appears more like a colour infrared photo.

           •   374: similar to 354.

           •   457: shows soil texture classes (clay, loam, sandy).

       By  default  the module will calculate standard deviations for all bands in parallel. To run serially use
       the -s flag. If the WORKERS environment variable is set, the  number  of  concurrent  processes  will  be
       limited to that number of jobs.

EXAMPLE

       North Carolina sample dataset:
       g.region raster=lsat7_2002_10 -p
       i.oif input=lsat7_2002_10,lsat7_2002_20,lsat7_2002_30,lsat7_2002_40,lsat7_2002_50,lsat7_2002_70

REFERENCES

       Jensen, 1996. Introductory digital image processing. Prentice Hall, p.98. ISBN 0-13-205840-5

SEE ALSO

        d.rgb, r.composite, r.covar, r.univar

AUTHORS

       Markus Neteler, ITC-Irst, Trento, Italy
       Updated to GRASS 5.7 by Michael Barton, Arizona State University

SOURCE CODE

       Available at: i.oif source code (history)

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       © 2003-2019 GRASS Development Team, GRASS GIS 7.8.2 Reference Manual