GRASS GIS manual: i.pysptools.unmix

NAME

i.pysptools.unmix - Extract endmembers from imagery group and perform spectral unmixing using pysptools

KEYWORDS

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)

DESCRIPTION
NOTES
EXAMPLES
REQUIREMENTS
REFERENCES
SEE ALSO
AUTHORS

DESCRIPTION

i.pysptools.unmix extracts endmembers from imagery group and performs spectral unmixing using pysptools.

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.

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

NOTES

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.

EXAMPLES

# List bands
bands=`g.list type=raster pattern=lsat7_2002* separator=','`

# Create imagery group
i.group group=lsat_2002 input="$bands"

# Extract Endmembers and perform spectral unmixing using pysptools
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
i.spec.unmix group=lsat7_2002 matrix=sample/spectrum.dat result=lsat7_2002_unmix \
error=lsat7_2002_unmix_err iter=lsat7_2002_unmix_iterations

REQUIREMENTS

REFERENCES

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.