i.oif(1grass) GRASS GIS User's Manual i.oif(1grass)
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 correla-
tion of the 3 bands in visible spectrum, this combination con-
tains not much more info than is contained in single band.
• 234: sensitive to green vegetation (portrayed as red), conifer-
ous 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 con-
struction.
• 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 environ-
ment variable is set, the number of concurrent processes will be lim-
ited 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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GRASS 7.8.7 i.oif(1grass)
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