NAME
i.pysptools.unmix - Extract endmembers from imagery group and perform spectral unmixing using pysptools
KEYWORDS
imagery,
endmember,
spectral unmixing
SYNOPSIS
i.pysptools.unmix
i.pysptools.unmix --help
i.pysptools.unmix [-n] input=name [output=name] [prefix=string] [endmembers=name] endmember_n=integer [extraction_method=string] [unmixing_method=string] [maxit=integer] [--overwrite] [--help] [--verbose] [--quiet] [--ui]
Flags:
- -n
- Do not use Automatic Target Generation Process (ATGP)
- --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 [required]
- Input imagery group
- output=name
- Text file storing endmember information for i.spec.unmix
- prefix=string
- Prefix for resulting raster maps
- endmembers=name
- Vector map representing identified endmembers
- endmember_n=integer [required]
- Number of endmembers to identify
- extraction_method=string
- Method for endmember extraction
- Options: FIPPI, PPI, NFINDR
- Default: NFINDR
- unmixing_method=string
- Algorithm for spectral unmixing
- Options: FCLS, UCLS, NNLS
- Default: FCLS
- maxit=integer
- Maximal number of iterations for endmember extraction (default=3*number of bands)
i.pysptools.unmix extracts endmembers from imagery group and performs
spectral unmixing using
pysptools. The module creates
an endmember text file and endmember raster maps.
The module is a wrapper around the pysptools Python library, that integrates
its functionality for
Endmember Extraction
and Spectral
Unmixing into GRASS GIS.
It requires that the Python libraries pysptools and scikit-learn
are installed (see below).
Supported algorithms for
Endmember Extraction
are:
- NFINDR: N-FINDR endmembers induction algorithm after Winter (1999),
that also makes use of an Automatic Target Generation Process (ATGP) (Plaza &
Chang 2006). (Default)
- FIPPI: Fast Iterative Pixel Purity Index after Chang (2006)
- PPI: Pixel Purity Index
Supported algorithms for
Spectral Unmixing
are:
- FCLS: Fully Constrained Least Squares (FCLS): Estimates endmember
abundance per pixel with the constraint that values are non-negative and sum up
to one per pixel (Default)
- UCLS: Unconstrained Least Squares (UCLS): Estimates endmember
abundance per pixel in an unconstrained way
- NNLS: Non-negative Constrained Least Squares (NNLS): Estimates endmember
abundance per pixel with the constraint that values are non-negative
Number of endmembers to extract (endmember_n) is supposed to be lower
than the number of bands in the imagery group. Only the PPI method can
extract more endmembers than there are bands in the imagery group.
Example for the North Carolina sample dataset:
# Create list of bands excluding thermal bands
bands=`g.list type=raster pattern="lsat7_2002*" exclude="lsat7_2002_6?" separator=','`
echo "$bands"
# Create imagery group
i.group group=lsat_2002 input="$bands"
# set computation region
g.region raster=lsat7_2002_10 -p
# Extract Endmembers and perform spectral unmixing using pysptools
# resulting in an endmember text file and raster maps (here: 5 endmember)
i.pysptools.unmix input=lsat_2002 endmembers=endmembers endmember_n=5 \
output=spectrum.txt prefix=lsat_spectra --v
# Compare to result from i.spec.unmix addon
i.spec.unmix group=lsat7_2002 matrix=sample/spectrum.dat result=lsat7_2002_unmix \
error=lsat7_2002_unmix_err iter=lsat7_2002_unmix_iterations
- python-cvxopt (install through system software management)
- python-matplotlib (install through system software management)
- python-scikit-learn (install through system software management)
- python-scipy (install through system software management)
- pysptools library
- scikit-learn library
-
Chang, C.-I. 2006: A fast iterative algorithm for implementation of pixel
purity index. Geoscience and Remote Sensing Letters, IEEE, 3(1): 63-67.
-
Plaza, A. & Chang, C.-I. 2006: Impact of Initialization on Design of
Endmember Extraction Algorithms. Geoscience and Remote Sensing,
IEEE Transactions. 44(11): 3397-3407.
-
Winter, M. E. 1999: N-FINDR: an algorithm for fast autonomous spectral
end-member determination in hyperspectral data. Presented at the
Imaging Spectrometry V, Denver, CO, USA, (3753): 266-275.
i.spec.unmix
Stefan Blumentrath,
Norwegian Institute for Nature Research (NINA), Oslo, Norway
Zofie Cimburova,
Norwegian Institute for Nature Research (NINA), Oslo, Norway
SOURCE CODE
Available at:
i.pysptools.unmix source code
(history)
Latest change: Tuesday Jun 13 10:40:13 2023 in commit: 0afa5ad5bcc67719abe437372c8952d635bc25ba
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