Change detection algorithms play a vital role in overseeing the transformations on the earth surface. Unsupervised change detection has a 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 synthetic aperture radar(SAR) 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 discrete wavelet 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.

1. S. M. Metev and V. P. Veiko, Laser Assisted Microtechnology, 2nd ed. , R. M. Osgood, Jr. , Ed. Berlin, Germany: Springer-Verlag, 1998.
2. L. Bruzzone and D. F. Prieto, "Automatic analysis of the difference image for unsupervised change detection,"IEEETrans. Geosci. Remote Sens. ,vol. 38,no. 3,pp. 1171-1182,2000.
3. L. Bruzzone and D. F. Prieto, "An adaptive semiparametric and context-based approach to unsupervised change detection in multitemporal remote-sensing images,"IEEE Trans. Image Processing. ,vol. 11,no. 4,pp. 66-77,1996.
4. C. Dumontier, F. Luthon, and J. -P. Charras, "Real-time DSP implementation for MRF-based video motion detection," IEEE Trans. Image Process. , vol. 8, no. 10, pp. 1341–1347, Oct. 1999.
5. R. Collins,A. Lipton, and T. Kanade, "Introduction to special section on video surveillance," IEEE Trans. Pattern Anal. Mach. Intell. ,vol 22,no. 8,pp. 745-746,Aug. 2000Tavel, P. 2007 Modeling and Simulation Design. AK Peters Ltd.
6. K. Grover,S. Quegan and C. da Costa Freitas, "Quantitative estimation of tropical forest cover by SAR," IEEE Trans. Geosci. Remote Sens. , vol. 37, no. 1, pp. 479–490, Jan. 1999.
7. L. Bruzzone and S. B. Serpico, "An iterative technique for the detection of land-cover transitions in multitemporal remote- sensing images," IEEE Trans. Geosci. Remote Sens. ,vol. 35,,pp. 858-867,July 1997.
8. P. S Chavez,Jr and D. J MacKinnon, "Automatic detection of vegetation changes in the south western united states using remotely sensed images,"Photogramm. Eng. Remote Sensing,vol. 60,no. 5,pp. 1285-1294,1994.
9. K. R. Merril and L. Jiajun, "A comparison of four algorithms for change detection in an urban environment," Remote Sens. Environ. , vol. 63, no. 2, pp. 95–100, Feb. 1998.
10. T. Celik, "A Bayesian approach to unsupervised multiscale change detection in synthetic aperture radar images," Signal Process. , vol. 90, no. 5,pp. 1471–1485, May 2010
11. F. Chatelain, J. -Y. Tourneret, and J. Inglada, "Change detection in multisensor SAR images using bivariate Gamma distributions," IEEETrans. Image Process. , vol. 17, no. 3, pp. 249–258, Mar. 2008.
12. Y. Bazi, L. Bruzzone, and F. Melgani, "An unsupervised approach based on the generalized Gaussian model to automatic change detection inmultitemporal SAR images," IEEE Trans. Geosci. Remote Sens. , vol. 43, no. 4, pp. 874–887, Apr. 2005.
13. A. Singh, "Digital change detection techniques using remotely sensed data," Int. J. Remote Sens. , vol. 10, no. 6, pp. 989–1003, 1989.
14. M. Sezgin and B. Sankur, "A survey over image thresholding techniques and quantitative performance evaluation," J. Electron. Imag. ,vol. 13, no. 1, pp. 146–165, Jan. 2004.
15. J. Inglada and G. Mercier, "A new statistical similarity measure for change detection in multitemporal SAR images and its extension to multiscale change analysis," IEEE Trans. Geosci. Remote Sens. , vol. 45, no. 5, pp. 1432–1445, May 2007.
16. Vrabel, J. : Multispectral imagery band sharpening study. Photogrammetric Engineeringand Remote Sensing, Vol. 62. (1969) 1075-1083.
17. M. Gong, Z. Zhou,J. Ma,"Change Detection In SAR Images Based On Image Fusion And Improved Fuzzy Clustering ,IEEE Trans. Imag. Process,Vol. 21,no. 4,pp 2141-2151,2012.
18. Volpi. M, Tuia. D, Campo-Valls. G and Kanevski. M, "Unsupervised Change Detection with kernels",IEEE Trans. Geosci. Remote Sens. ,vol. 9, no. 6, pp. 1026–1030, 2012

Change Detection Difference Image Image Fusion Kernel-k Means Clustering Synthetic Aperture Radar.