i.oif(1)

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

KEYWORDS
imagery, multispectral, statistics

SYNOPSIS
i.oif.py
i.oif.py --help
i.oif.py [-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)

Accessed: unknown

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