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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Vydáno v:Computational ecology and software Ročník 2; číslo 4; s. 220 - 225
Hlavní autor: Hussain, Rashid
Médium: Journal Article
Jazyk:angličtina
Vydáno: Hong Kong Computational Ecology and Software 01.12.2012
International Academy of Ecology and Environmental Sciences (IAEES)
International Academy of Ecology and Environmental Sciences
Témata:
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
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Popis
Shrnutí: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.
Bibliografie: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