1i.oif(1) Grass User's Manual i.oif(1)
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6 i.oif - Calculates Optimum-Index-Factor table for spectral bands
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9 imagery, multispectral, statistics
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12 i.oif
13 i.oif --help
14 i.oif [-gs] input=name[,name,...] [output=name] [--overwrite]
15 [--help] [--verbose] [--quiet] [--ui]
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17 Flags:
18 -g
19 Print in shell script style
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21 -s
22 Process bands serially (default: run in parallel)
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24 --overwrite
25 Allow output files to overwrite existing files
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27 --help
28 Print usage summary
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30 --verbose
31 Verbose module output
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33 --quiet
34 Quiet module output
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36 --ui
37 Force launching GUI dialog
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39 Parameters:
40 input=name[,name,...] [required]
41 Name of input raster map(s)
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43 output=name
44 Name for output file (if omitted or "-" output to stdout)
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47 i.oif calculates the Optimum Index Factor for multi-spectral satellite
48 imagery.
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50 The Optimum Index Factor (OIF) determines the three-band combination
51 that maximizes the variability (information) in a multi-spectral scene.
52 The index is a ratio of the total variance (standard deviation) within
53 and the correlation between all possible band combinations. The bands
54 that comprise the highest scoring combination from i.oif are used as
55 the three color channels required for d.rgb or r.composite.
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57 The analysis is saved to a file in the current directory called
58 "i.oif.result".
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61 Landsat 1-7 TM: Colour Composites in BGR order as important Landsat TM
62 band combinations (example: 234 in BGR order means: B=2, G=3, R=4):
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64 · 123: near natural ("true") colour; however, because of correla‐
65 tion of the 3 bands in visible spectrum, this combination con‐
66 tains not much more info than is contained in single band.
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68 · 234: sensitive to green vegetation (portrayed as red), conifer‐
69 ous as distinctly darker red than deciduous forests. Roads and
70 water bodies are clear.
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72 · 243: green vegetation is green but coniferous forests aren’t as
73 clear as the 234 combination.
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75 · 247: one of the best for info pertaining to forestry. Good for
76 operation scale mapping of recent harvest areas and road con‐
77 struction.
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79 · 345: contains one band from each of the main reflective units
80 (vis, nir, shortwave infra). Green vegetation is green and the
81 shortwave band shows vegetational stress and mortality. Roads
82 are less evident as band 3 is blue.
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84 · 347: similar to 345 but depicts burned areas better.
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86 · 354: appears more like a colour infrared photo.
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88 · 374: similar to 354.
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90 · 457: shows soil texture classes (clay, loam, sandy).
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92 By default the module will calculate standard deviations for all bands
93 in parallel. To run serially use the -s flag. If the WORKERS environ‐
94 ment variable is set, the number of concurrent processes will be lim‐
95 ited to that number of jobs.
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98 North Carolina sample dataset:
99 g.region raster=lsat7_2002_10 -p
100 i.oif input=lsat7_2002_10,lsat7_2002_20,lsat7_2002_30,lsat7_2002_40,lsat7_2002_50,lsat7_2002_70
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103 Jensen, 1996. Introductory digital image processing. Prentice Hall,
104 p.98. ISBN 0-13-205840-5
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107 d.rgb, r.composite, r.covar, r.univar
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110 Markus Neteler, ITC-Irst, Trento, Italy
111 Updated to GRASS 5.7 by Michael Barton, Arizona State University
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113 Last changed: $Date: 2015-07-20 10:45:41 +0200 (Mon, 20 Jul 2015) $
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116 Available at: i.oif source code (history)
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118 Main index | Imagery index | Topics index | Keywords index | Graphical
119 index | Full index
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121 © 2003-2019 GRASS Development Team, GRASS GIS 7.4.4 Reference Manual
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125GRASS 7.4.4 i.oif(1)