Synthetic Aperture Radar (SAR) images features clustering using Fuzzy c-means (FCM) clustering algorithm

Remote sensing applications such as Ecological monitoring, Disaster monitoring, Volcanic monitoring, surveillance and reconnaissance requires broad range imaginary data with very high resolution. Data captured under different times such as day or night and under different weather conditions poses ad...

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Bibliographic Details
Published in:Computational ecology and software Vol. 2; no. 4; pp. 220 - 225
Main Author: Hussain, Rashid
Format: Journal Article
Language:English
Published: Hong Kong Computational Ecology and Software 01.12.2012
International Academy of Ecology and Environmental Sciences (IAEES)
International Academy of Ecology and Environmental Sciences
Subjects:
Algorithms
Clustering
Ecological monitoring
Fuzzy
Fuzzy c-means (FCM) clustering algorithm
Fuzzy set theory
Monitoring
remote sensing
satellite radar image
Synthetic aperture radar
Synthetic Aperture Radar (SAR)
ISSN:2220-721X, 2220-721X
Online Access:Get full text
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Summary:Remote sensing applications such as Ecological monitoring, Disaster monitoring, Volcanic monitoring, surveillance and reconnaissance requires broad range imaginary data with very high resolution. Data captured under different times such as day or night and under different weather conditions poses adverse affects on retrieved results. Synthetic Aperture Radar (SAR) technology is used to mitigate such adverse effects. Recently SAR technology re-emerges because of the decrease in the cost of electronic components and tremendous advancement in computing power. This paper provides an application of Fuzzy c-means (FCM) clustering algorithm to SAR Images. The objective of this study is to segment various region of interest in remote sensing images for ecological monitoring.
Bibliography:http://www.iaees.org/publications/journals/ces/articles/2012-2(4)/synthetic-aperture-radar-images-features-clustering.pdf
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ISSN:2220-721X
2220-721X
DOI:10.0000/issn-2220-721x-compuecol-2012-v2-0017