Feature Selection in AVHRR Ocean Satellite
Images by Means of Filter Methods
Jose A. Piedra-Fernandez, Manuel Canton-Garbin,
University of Almeria, Spain
James Z. Wang
The Pennsylvania State University
Automatic retrieval and interpretation of satellite images
is critical for managing the enormous volume of environmental
remote sensing data available today. It is particularly useful
in oceanography and climate studies for examination of the spatio-
temporal evolution of mesoscalar ocean structures appearing
in the satellite images taken by visible, infrared, and radar sensors.
This is because they change so quickly and several images of the
same place can be acquired at different times within the same day.
This paper describes the use of filter measures and the Bayesian
networks to reduce the number of irrelevant features necessary
for ocean structure recognition in satellite images, thereby improving
the overall interpretation system performance and reducing
the computational time. We present our results for the National
Oceanographic and Atmospheric Administration satellite
Advanced Very High Resolution Radiometer (AVHRR) images.
We have automatically detected and located mesoscale ocean phenomena
of interest in our study area (North-East Atlantic and the
Mediterranean), such as upwellings, eddies, and island wakes, using
an automatic selectionmethodology which reduces the features
used for description by about 80%. Finally, Bayesian network classifiers
are used to assess classification quality. Knowledge about
these structures is represented with numeric and nonnumeric
(IEEE page proof, PDF, 1.0MB)
Jose A. Piedra-Fernandez, M. Canton-Garbin and James Z. Wang, ``Feature Selection in AVHRR Ocean Satellite Images by Means of Filter Methods,''
IEEE Transactions on Geoscience and Remote Sensing, vol. 48, no. 12, pp. 4193-4203, 2010.
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August 25, 2010