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== Reference ==
James P. Theiler and Neal R. Harvey and Steven P. Brumby and John J. Szymanski and Steve Alferink and Simon J. Perkins and Reid B. Porter and Jeffrey J. Bloch: Evolving Retrieval Algorithms with a Genetic Programming Scheme. Proceedings of SPIE 3753 Imaging Spectrometry V, pp. 416-425, 1999.

== DOI ==
http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.12.8669&rank=1

== Abstract ==
The retrieval of scene properties (surface temperature, material type, vegetation health, etc.) from remotely sensed
data is the ultimate goal of many earth observing satellites. The algorithms that have been developed for these
retrievals are informed by physical models of how the raw data were generated. This includes models of radiation
as emitted and/or reflected by the scene, propagated through the atmosphere, collected by the optics, detected by
the sensor, and digitized by the electronics. To some extent, the retrieval is the inverse of this “forward” modeling
problem.
But in contrast to this forward modeling, the practical task of making inferences about the original scene usually
requires some ad hoc assumptions, good physical intuition, and a healthy dose of trial and error.
The standard MTI data processing pipeline will employ algorithms developed with this traditional approach.
But we will discuss some preliminary research on the use of a genetic programming scheme to “evolve” retrieval
algorithms. Such a scheme cannot compete with the physical intuition of a remote sensing scientist, but it may be
able to automate some of the trial and error. In this scenario, a training set is used, which consists of multispectral
image data and the associated “ground truth;” that is, a registered map of the desired retrieval quantity. The genetic
programming scheme attempts to combine a core set of image processing primitives to produce an IDL (Interactive
Data Language) program which estimates this retrieval quantity from the raw data.

== Extended Abstract ==

== Bibtex ==

== Used References ==
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2. P. G. Weber, C. C. Borel, W. B. Clodius, B. J. Cooke, and B. W. Smith, “Design considerations, modeling
and analysis for the Multispectral Thermal Imager,” in Infrared Imaging Infrared Imaging Systems: Design,
Analysis, Modeling, and Testing X, G. C. Holst, ed., Proc. SPIE 3701, 1999.

3. P. G. Weber, J. Theiler, B. W. Smith, S. P. Love, B. J. Cooke, W. B. Clodius, C. C. Borel, and S. C. Bender,
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6. This work is also described at the following website:
http://www.spd.eee.strath.ac.uk/users/harve/bbc epsrc film dirt.html.

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10. J. M. Daida, T. F. Bersano-Begey, S. J. Ross, and J. F. Vesecky, “Computer-assisted design of image classifica-
tion algorithms: Dynamic and static fitness evaluations in a scaffolded genetic programming environment,” in
Advances in Genetic Programming II, P. Angeline and K. Kinnear, eds., MIT Press, 1996.

11. J. M. Daida, J. D. Hommes, T. F. Bersano-Begey, S. J. Ross, and J. F. Vesecky, “Algorithm discovery using the
genetic-programming paradigm: Extracting low-contrast curvilinear features from SAR images of arctic ice,” in
Advances in Genetic Programming II, P. Angeline and K. Kinnear, eds., MIT Press, 1996.

12. J. M. Daida, T. F. Bersano-Begey, S. J. Ross, and J. F. Vesecky, “Evolving feature-extraction algorithms:
Adapting genetic programming for image analysis in geoscience and remote sensing,” in Proc. 1996 International
Geoscience and Remote Sensing Symposium: Remote Sensing for a Sustainable Future, IEEE Press, 1996.

13. J. M. Daida, R. G. Onstott, T. F. Bersano-Begey, S. J. Ross, and J. F. Vesecky, “Ice roughness classification and
ERS SAR imagery of arctic sea ice: Evaluation of feature-extraction algorithms by genetic programming,” in
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IEEE Press, 1996.

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16. M. Mitchell, An Introduction to Genetic Algorithms, MIT Press, Cambridge, Massachusetts, 1996.
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18. IDL (Interactive Data Language) is a commercial package for generic data and image processing, developed and
marketed by Research Systems, Inc. (http://www.rsinc.com).

19. S. P. Brumby, J. Theiler, S. Perkins, N. Harvey, J. J. Szymanski, J. J. Bloch, and M. Mitchell, “Investigation
of image feature extraction by a genetic algorithm,” in Applications and Science of Neural Networks, Fuzzy
Systems, and Evolutionary Computation II, B. Bosacchi, D. B. Fogel, and J. C. Bezdek, eds., Proc SPIE 3812,
1999.

20. C. C. Borel, W. B. Clodius, A. B. Davis, B. W. Smith, J. J. Szymanski, J. Theiler, P. V. Villeneuve, and P. G.
Weber, “MTI core science retrieval algorithms,” in Imaging Spectrometry V, M. R. Descour and S. S. Shen, eds.,
Proc SPIE 3753, 1999.

21. “The MODIS Airborne Simulator (MAS) is an airborne scanning spectrometer that acquires high spatial resolu-
tion imagery of cloud and surface features from its vantage point on-board a NASA ER-2 high-altitude research
aircraft” (http://ltpwww.gsfc.nasa.gov/MODIS/MAS/Home.html).

22. M. D. King, W. P. Menzel, P. S. Grant, J. S. Myers, G. T. Arnold, S. E. Platnick, L. E. Gumley, S. C. Tsay,
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sensing of cloud, aerosol, water vapor and surface properties,” J. Atmos. Oceanic Technol. 13, pp. 777–794,
1996.

== Links ==
=== Full Text ===
http://citeseer.ist.psu.edu/viewdoc/download?doi=10.1.1.12.8669&rep=rep1&type=pdf

[[intern file]]

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Gbachelier: Die Seite wurde neu angelegt: „ == Reference == James P. Theiler and Neal R. Harvey and Steven P. Brumby and John J. Szymanski and Steve Alferink and Simon J. Perkins and Reid B. Porter and…“