DESCRIPTION

i.spec.unmix is used to perform Spectral Unmixing. The result is written in percent (rounded to nearest integer).

Concept of mixed pixels (Landsat example)

EXAMPLES

This example is based on the North Carolina Sample dataset.

Prior to spectral unmixing the pixel values (DN) of the Landsat scene need to be converted to reflectance values (here: using DOS1):

# rename channels or make a copy to match i.landsat.toar's input name scheme:
g.copy raster=lsat7_2002_10,lsat7_2002.1
g.copy raster=lsat7_2002_20,lsat7_2002.2
g.copy raster=lsat7_2002_30,lsat7_2002.3
g.copy raster=lsat7_2002_40,lsat7_2002.4
g.copy raster=lsat7_2002_50,lsat7_2002.5
g.copy raster=lsat7_2002_61,lsat7_2002.61
g.copy raster=lsat7_2002_62,lsat7_2002.62
g.copy raster=lsat7_2002_70,lsat7_2002.7
g.copy raster=lsat7_2002_80,lsat7_2002.8

Calculation of reflectance values from DN using DOS1 (metadata obtained from p016r035_7x20020524.met.gz):

# set computational region to first Landsat band
g.region raster=lsat7_2002_10 -p

i.landsat.toar input=lsat7_2002. output=lsat7_2002_toar. sensor=tm7 \
  method=dos1 date=2002-05-24 sun_elevation=64.7730999 \
  product_date=2004-02-12 gain=HHHLHLHHL

The resulting Landsat bands are named lsat7_2002_toar.1 .. lsat7_2002_toar.8. They are used as input for the next steps.

In order to obtain pure spectra ("endmembers") to be searched for during the spectral unmixing process later on we can either obtain them from spectral libraries (ASTER Spectral Library, USGS Spectral Library, field spectrometer, etc.) or through a PCA analysis as follows.

In order to identify pure endmembers, they are supposed to be in the corners of the PCA feature space:

i.pca -n input=lsat7_2002_toar.1,lsat7_2002_toar.2,lsat7_2002_toar.3,lsat7_2002_toar.4,lsat7_2002_toar.5,lsat7_2002_toar.7 \
         output=pca_lsat7_2002_toar
d.mon wx0
# d.correlate or use scatterplot tool in g.gui
d.correlate map=pca_lsat7_2002_toar.1,pca_lsat7_2002_toar.2

# TODO: problem: how to obtain the unprojected coordinates for the corner pixels?
# (in 1998 the xgobi software did this nicely, check today's ggobi)

Next the ASCII file (e.g. called "spectrum.dat") containing six spectra needs to be written using either spectral data from a spectral library or from the PCA analysis.

Sample content of "spectrum.dat":

# Channels: r g b i1 i2 i3
# Enter spectra linewise!
# 1. Sagebrush 
# 2. Saltbush
# 3. Soil
# 4. Dry grass
#
Matrix: 4 by 6
row0:  8.87  13.14  11.71  35.85  28.26 10.54
row1: 13.59  20.12  19.61  70.66 34.82 16.35
row2: 28.26  34.82  38.27  40.1 38.27 23.7
row3: 10.54  16.35  23.7   38.98 40.1 38.98

Spectral unmixing step (requires input data to be collected in an imagery group):

i.group group=lsat7_2002_toar subgroup=lsat7_2002_toar \
  input=lsat7_2002_toar.1,lsat7_2002_toar.2,lsat7_2002_toar.3,lsat7_2002_toar.4,lsat7_2002_toar.5,lsat7_2002_toar.7

i.spec.unmix group=lsat7_2002_toar matrix=sample/spectrum.dat result=lsat7_2002_unmix \
  error=lsat7_2002_unmix_err iter=lsat7_2002_unmix_iterations

# todo: reclass to 0..100%

REFERENCES

Neteler, M., 1999: Spectral Mixture Analysis von Satellitendaten zur Bestimmung von Bodenbedeckungsgraden im Hinblick auf die Erosionsmodellierung. M.Sc. thesis, University of Hannover, Germany.
Neteler, M., D. Grasso, I. Michelazzi, L. Miori, S. Merler, and C. Furlanello, 2004. New image processing tools for GRASS. - In Proc. Free/Libre and Open Source Software for Geoinformatics: GIS-GRASS Users Conference, 12-14 Sep 2004, Bangkok, Thailand.
Neteler, M., D. Grasso, I. Michelazzi, L. Miori, S. Merler, and C. Furlanello, 2005. An integrated toolbox for image registration, fusion and classification. International Journal of Geoinformatics, 1(1), pp. 51-61. (PDF)

AUTHOR

Markus Neteler, University of Hannover, 1999

Mohammed Rashad (rashadkm gmail.com) (2012, update to GRASS 7)

Last changed: $Date$

DESCRIPTION

EXAMPLES

REFERENCES

SEE ALSO

AUTHOR