Provided by: grass-doc_6.4.3-3_all
i.oif - Calculates Optimum-Index-Factor table for LANDSAT TM bands 1-5, & 7
raster, imagery, statistics
i.oif i.oif help i.oif [-g] image1=string image2=string image3=string image4=string image5=string image7=string [--verbose] [--quiet] Flags: -g Print in shell script style --verbose Verbose module output --quiet Quiet module output Parameters: image1=string LANDSAT TM band 1. image2=string LANDSAT TM band 2. image3=string LANDSAT TM band 3. image4=string LANDSAT TM band 4. image5=string LANDSAT TM band 5. image7=string LANDSAT TM band 7.
i.oif calculates the Optimum Index Factor for LANDSAT TM bands 1,2,3,4,5 and 7. The Optimum Index Factor is calculated to determine the band combination which shows the maximum information when combined into a composite image. The bands comprising 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".
Colour Composites in BGR order: important 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).
North Carolina sample dataset: g.region rast=lsat7_2002_10 -p i.oif image1=lsat7_2002_10 image2=lsat7_2002_20 image3=lsat7_2002_30 \ image4=lsat7_2002_40 image5=lsat7_2002_50 image7=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 Last changed: $Date: 2011-09-04 06:23:30 -0700 (Sun, 04 Sep 2011) $ Full index © 2003-2013 GRASS Development Team