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Title: Genetic programming approach to extracting features from remotely sensed imagery

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.

Authors:
 [1];  [2];  [3];  [4];
  1. (James P.)
  2. (Simon J.)
  3. (Neal R.)
  4. (John J.)
Publication Date:
Research Org.:
Los Alamos National Laboratory
Sponsoring Org.:
USDOE
OSTI Identifier:
975334
Report Number(s):
LA-UR-01-2787
TRN: US201008%%180
Resource Type:
Conference
Resource Relation:
Conference: "Submitted to: Fusion 2001, 4th International Conference on Information Fusion Montreal, Canada, Aug. 7-10"
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; ALGORITHMS; GENETICS; IMAGE PROCESSING; PERFORMANCE; PIPELINES; PROGRAMMING; REMOTE SENSING

Citation Formats

Theiler, J. P., Perkins, S. J., Harvey, N. R., Szymanski, J. J., and Brumby, Steven P.. Genetic programming approach to extracting features from remotely sensed imagery. United States: N. p., 2001. Web.
Theiler, J. P., Perkins, S. J., Harvey, N. R., Szymanski, J. J., & Brumby, Steven P.. Genetic programming approach to extracting features from remotely sensed imagery. United States.
Theiler, J. P., Perkins, S. J., Harvey, N. R., Szymanski, J. J., and Brumby, Steven P.. Mon . "Genetic programming approach to extracting features from remotely sensed imagery". United States. https://www.osti.gov/servlets/purl/975334.
@article{osti_975334,
title = {Genetic programming approach to extracting features from remotely sensed imagery},
author = {Theiler, J. P. and Perkins, S. J. and Harvey, N. R. and Szymanski, J. J. and Brumby, Steven P.},
abstractNote = {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.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2001},
month = {1}
}

Conference:
Other availability
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