The Cerro Grande/Los Alamos forest fire devastated
over 43,000 acres (17,500 ha) of forested land, and
destroyed over 200 structures in the town of Los Alamos
and the adjoining Los Alamos National Laboratory. The
need to measure the continuing impact of the fire on
the local environment has led to the application of a
number of remote sensing technologies. During and after
the fire, remote-sensing data was acquired from a
variety of aircraft- and satellite-based sensors,
including Landsat 7 Enhanced Thematic Mapper (ETM+). We
now report on the application of a machine learning
technique to the automated classification of land cover
using multi-spectral and multi-temporal imagery.
We apply a hybrid genetic programming/supervised
classification technique to evolve automatic feature
extraction algorithms. We use a software package we
have developed at Los Alamos National Laboratory,
called GENIE, to carry out this evolution. We use
multispectral imagery from the Landsat 7 ETM+
instrument from before, during, and after the wildfire.
Using an existing land cover classification based on a
1992 Landsat 5 TM scene for our training data, we
evolve algorithms that distinguish a range of land
cover categories, and an algorithm to mask out clouds
and cloud shadows. We report preliminary results of
combining individual classification results using a
K-means clustering approach. The details of our evolved
classification are compared to the manually produced
land-cover classification.
%0 Conference Paper
%1 oai:CiteSeerPSU:445835
%A Brumby, Steven P.
%A Theiler, James
%A Bloch, Jeffrey J.
%A Harvey, Neal R.
%A Perkins, Simon
%A Szymanski, John J.
%A Young, A. Cody
%B Proc. SPIE Imaging Spectrometry VII
%D 2002
%E Descour, Michael R.
%E Shen, Sylvia S.
%K Extraction, Feature K-means Land Multi-spectral Supervised Wildfire algorithms, classification, clustering, cover, genetic imagery, programming,
%T Evolving land cover classification algorithms for
multispectral and multitemporal imagery
%U http://citeseer.ist.psu.edu/445835.html
%V 4480
%X The Cerro Grande/Los Alamos forest fire devastated
over 43,000 acres (17,500 ha) of forested land, and
destroyed over 200 structures in the town of Los Alamos
and the adjoining Los Alamos National Laboratory. The
need to measure the continuing impact of the fire on
the local environment has led to the application of a
number of remote sensing technologies. During and after
the fire, remote-sensing data was acquired from a
variety of aircraft- and satellite-based sensors,
including Landsat 7 Enhanced Thematic Mapper (ETM+). We
now report on the application of a machine learning
technique to the automated classification of land cover
using multi-spectral and multi-temporal imagery.
We apply a hybrid genetic programming/supervised
classification technique to evolve automatic feature
extraction algorithms. We use a software package we
have developed at Los Alamos National Laboratory,
called GENIE, to carry out this evolution. We use
multispectral imagery from the Landsat 7 ETM+
instrument from before, during, and after the wildfire.
Using an existing land cover classification based on a
1992 Landsat 5 TM scene for our training data, we
evolve algorithms that distinguish a range of land
cover categories, and an algorithm to mask out clouds
and cloud shadows. We report preliminary results of
combining individual classification results using a
K-means clustering approach. The details of our evolved
classification are compared to the manually produced
land-cover classification.
@inproceedings{oai:CiteSeerPSU:445835,
abstract = {The Cerro Grande/Los Alamos forest fire devastated
over 43,000 acres (17,500 ha) of forested land, and
destroyed over 200 structures in the town of Los Alamos
and the adjoining Los Alamos National Laboratory. The
need to measure the continuing impact of the fire on
the local environment has led to the application of a
number of remote sensing technologies. During and after
the fire, remote-sensing data was acquired from a
variety of aircraft- and satellite-based sensors,
including Landsat 7 Enhanced Thematic Mapper (ETM+). We
now report on the application of a machine learning
technique to the automated classification of land cover
using multi-spectral and multi-temporal imagery.
We apply a hybrid genetic programming/supervised
classification technique to evolve automatic feature
extraction algorithms. We use a software package we
have developed at Los Alamos National Laboratory,
called GENIE, to carry out this evolution. We use
multispectral imagery from the Landsat 7 ETM+
instrument from before, during, and after the wildfire.
Using an existing land cover classification based on a
1992 Landsat 5 TM scene for our training data, we
evolve algorithms that distinguish a range of land
cover categories, and an algorithm to mask out clouds
and cloud shadows. We report preliminary results of
combining individual classification results using a
K-means clustering approach. The details of our evolved
classification are compared to the manually produced
land-cover classification.},
added-at = {2008-06-19T17:35:00.000+0200},
author = {Brumby, Steven P. and Theiler, James and Bloch, Jeffrey J. and Harvey, Neal R. and Perkins, Simon and Szymanski, John J. and Young, A. Cody},
biburl = {https://www.bibsonomy.org/bibtex/2fa5579256e4452768160df3480cd9427/brazovayeye},
booktitle = {Proc. SPIE Imaging Spectrometry VII},
editor = {Descour, Michael R. and Shen, Sylvia S.},
interhash = {d7d90537f6c7cd93bcc2f0be179b0599},
intrahash = {fa5579256e4452768160df3480cd9427},
keywords = {Extraction, Feature K-means Land Multi-spectral Supervised Wildfire algorithms, classification, clustering, cover, genetic imagery, programming,},
notes = {Los Alamos National Lab.},
timestamp = {2008-06-19T17:37:05.000+0200},
title = {Evolving land cover classification algorithms for
multispectral and multitemporal imagery},
url = {http://citeseer.ist.psu.edu/445835.html},
volume = 4480,
year = 2002
}