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Change Detection in Synthetic Aperture Radar Images Using Contourlet Based Fusion and Kernel K-Means Clustering


 
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1. Title Title of document Change Detection in Synthetic Aperture Radar Images Using Contourlet Based Fusion and Kernel K-Means Clustering
 
2. Creator Author's name, affiliation, country Venkateswaran K.; Department of Electronics and Communication Engineering, Kongu Engineering College, Erode, Tamil Nadu; India
 
2. Creator Author's name, affiliation, country Kasthuri N.; Department of Electronics and Communication Engineering, Kongu Engineering College, Erode, Tamil Nadu; India
 
2. Creator Author's name, affiliation, country Balakrishnan K.; Department of Electronics and Communication Engineering, Kongu Engineering College, Erode, Tamil Nadu; India
 
2. Creator Author's name, affiliation, country Prakash K.; Department of Electronics and Communication Engineering, Kongu Engineering College, Erode, Tamil Nadu; India
 
3. Subject Discipline(s) Remote Sensing
 
3. Subject Keyword(s) Change Detection; Difference Image; Image Fusion; Kernel-K Means Clustering; Synthetic Aperture Radar
 
3. Subject Subject classification Change Detection
 
4. Description Abstract Change detection algorithms play a vital role in overseeing the transformations on the earth surface. Unsupervised change detection has an indispensable role in an immense range of applications like remote sensing, motion detection, environmental monitoring, medical diagnosis, damage assessment, agricultural surveys, surveillance etc. In this paper, a novel method for unsupervised change detection in multitemporal images based on image fusion and kernel K-means clustering is proposed. Here difference image is generated by performing image fusion on mean-ratio and log-ratio image and for fusion contourlet transform is used. On the difference image generated by collecting the information from mean-ratio and log-ratio image kernel K-means clustering is performed. In kernel K-means clustering, non-linear clustering is performed, as a result the false alarm rate is reduced and accuracy of the clustering process is enhanced. The aggregation of image fusion and kernel K-means clustering is seen to be more effective in detecting the changes than its preexistences.
 
5. Publisher Organizing agency, location
 
6. Contributor Sponsor(s)
 
7. Date (YYYY-MM-DD) 2013-10-18
 
8. Type Status & genre Peer-reviewed Article
 
8. Type Type
 
9. Format File format PDF
 
10. Identifier Uniform Resource Identifier http://technical.cloud-journals.com/index.php/IJARSG/article/view/Tech-161
11. Source Journal/conference title; vol., no. (year) International Journal of Advanced Remote Sensing and GIS; Volume 2 (Year 2013)
 
12. Language English=en en
 
14. Coverage Geo-spatial location, chronological period, research sample (gender, age, etc.)
 
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