Multi-instrument data sets present an interesting
challenge to feature extraction algorithm developers.
Beyond the immediate problems of spatial
co-registration, the remote sensing scientist must
explore a complex algorithm space in which both spatial
and spectral signatures may be required to identify a
feature of interest. We describe a genetic
programming/supervised classifier software system,
called Genie, which evolves and combines
spatio-spectral image processing tools for remotely
sensed imagery. We describe our representation of
candidate image processing pipelines, and discuss our
set of primitive image operators. Our primary
application has been in the field of geospatial feature
extraction, including wildfire scars and general
land-cover classes, using publicly available
multi-spectral imagery (MSI) and hyper-spectral imagery
(HSI). Here, we demonstrate our system on Landsat 7
Enhanced Thematic Mapper (ETM+) MSI. We exhibit an
evolved pipeline, and discuss its operation and
performance.
%0 Conference Paper
%1 Brumby:2001:FUSION
%A Brumby, Steven P.
%A Theiler, James
%A Perkins, Simon
%A Harvey, Neal R.
%A Szymanski, John J.
%B FUSION 2001: Fourth International Conference on Image
Fusion
%C Montreal, Quebec, Canada
%D 2001
%K Computation, Evolutionary Image Imagery, Multispectral Panchromatic Processing, Remote Sensing, algorithms, genetic imagery programming,
%T Genetic programming approach to extracting features
from remotely sensed imagery
%U http://public.lanl.gov/perkins/webdocs/brumbyFUSION2001.pdf
%X Multi-instrument data sets present an interesting
challenge to feature extraction algorithm developers.
Beyond the immediate problems of spatial
co-registration, the remote sensing scientist must
explore a complex algorithm space in which both spatial
and spectral signatures may be required to identify a
feature of interest. We describe a genetic
programming/supervised classifier software system,
called Genie, which evolves and combines
spatio-spectral image processing tools for remotely
sensed imagery. We describe our representation of
candidate image processing pipelines, and discuss our
set of primitive image operators. Our primary
application has been in the field of geospatial feature
extraction, including wildfire scars and general
land-cover classes, using publicly available
multi-spectral imagery (MSI) and hyper-spectral imagery
(HSI). Here, we demonstrate our system on Landsat 7
Enhanced Thematic Mapper (ETM+) MSI. We exhibit an
evolved pipeline, and discuss its operation and
performance.
@inproceedings{Brumby:2001:FUSION,
abstract = {Multi-instrument data sets present an interesting
challenge to feature extraction algorithm developers.
Beyond the immediate problems of spatial
co-registration, the remote sensing scientist must
explore a complex algorithm space in which both spatial
and spectral signatures may be required to identify a
feature of interest. We describe a genetic
programming/supervised classifier software system,
called Genie, which evolves and combines
spatio-spectral image processing tools for remotely
sensed imagery. We describe our representation of
candidate image processing pipelines, and discuss our
set of primitive image operators. Our primary
application has been in the field of geospatial feature
extraction, including wildfire scars and general
land-cover classes, using publicly available
multi-spectral imagery (MSI) and hyper-spectral imagery
(HSI). Here, we demonstrate our system on Landsat 7
Enhanced Thematic Mapper (ETM+) MSI. We exhibit an
evolved pipeline, and discuss its operation and
performance.},
added-at = {2008-06-19T17:35:00.000+0200},
address = {Montreal, Quebec, Canada},
author = {Brumby, Steven P. and Theiler, James and Perkins, Simon and Harvey, Neal R. and Szymanski, John J.},
biburl = {https://www.bibsonomy.org/bibtex/268afa2569f35d954407d9ac37ef36d82/brazovayeye},
booktitle = {FUSION 2001: Fourth International Conference on Image
Fusion},
email = {brumby@lanl.gov},
interhash = {ba66b6de2eecb7c2df27394ffeb104a8},
intrahash = {68afa2569f35d954407d9ac37ef36d82},
keywords = {Computation, Evolutionary Image Imagery, Multispectral Panchromatic Processing, Remote Sensing, algorithms, genetic imagery programming,},
month = {7-10 August},
notes = {oai:CiteSeerPSU:567526 seems to be wrong},
size = {8 pages},
timestamp = {2008-06-19T17:37:05.000+0200},
title = {Genetic programming approach to extracting features
from remotely sensed imagery},
url = {http://public.lanl.gov/perkins/webdocs/brumbyFUSION2001.pdf},
year = 2001
}