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

       Last changed: $Date: 2015-07-20 10:49:51 +0200 (Mon, 20 Jul 2015) $

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