Fuzzy Content-Based Image Retrieval for Oceanic Remote Sensing
Jose A. Piedra-Fernandez, Gloria Ortega
University of Almeria, Spain
James Z. Wang
The Pennsylvania State University
Manuel Canton-Garbin
University of Almeria, Spain
Abstract:
The detection of mesoscale oceanic structures, such
as upwellings or eddies, from satellite images has significance for
marine environmental studies, coastal resource management, and
ocean dynamics studies. Nevertheless, there is a lack of tools that
allow us to retrieve automatically relevant mesoscale structures
from large satellite image databases. This paper focuses on the
development and validation of a content-based image retrieval
system to classify and retrieve oceanic structures from satellite
images. The images were obtained from the National Oceanic and
Atmospheric Administration satellite's Advanced Very High Resolution
Radiometer sensor. The study area is about W2-21, N19-45.
This system conducts labeling and retrieval of the
most relevant and typical mesoscale oceanic structures, such as
upwellings, eddies, and island wakes located in the Canary Islands
area and in the Mediterranean and Cantabrian seas. Our work is
based on several soft computing technologies such as fuzzy logic
and neurofuzzy systems.
Full Paper
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Citation:
Jose A. Piedra-Fernandez, Gloria Ortega, James Z. Wang and M. Canton-Garbin, ``Fuzzy Content-Based Image Retrieval for Oceanic Remote Sensing,'' IEEE Transactions on Geoscience and Remote Sensing, vol. 52, no. 9, pp. 5422-5431, 2014.
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Last Modified:
December 17, 2013
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