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)
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".
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.
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
Jensen, 1996. Introductory digital image processing. Prentice Hall,
p.98. ISBN 0-13-205840-5
d.rgb,
r.composite,
r.covar,
r.univar
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)
Latest change: Monday Oct 21 17:07:17 2024 in commit: 67a160931aa01d29a896def827d5dd687e08bfd4
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© 2003-2024
GRASS Development Team,
GRASS GIS 8.5.0dev Reference Manual