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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| Vydané v: | Computational ecology and software Ročník 2; číslo 4; s. 220 - 225 |
|---|---|
| Hlavný autor: | |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Hong Kong
Computational Ecology and Software
01.12.2012
International Academy of Ecology and Environmental Sciences (IAEES) International Academy of Ecology and Environmental Sciences |
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| ISSN: | 2220-721X, 2220-721X |
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| Abstract | 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. |
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| AbstractList | 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. 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. |
| Author | Rashid Hussain |
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| Copyright | Copyright International Academy of Ecology and Environmental Sciences (IAEES) Dec 2012 |
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| DOI | 10.0000/issn-2220-721x-compuecol-2012-v2-0017 |
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| SubjectTerms | 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) |
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| Title | Synthetic Aperture Radar (SAR) images features clustering using Fuzzy c-means (FCM) clustering algorithm |
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