Open Journal Systems

Fusion of Satellite Images in Transform Domain

Venkatesh H

Article ID: 610
Vol 0, Issue 0, 2018, Article identifier:

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Abstract

Unique—Image fusion in light of the wavelet and fourier trans-form comes about rich multispectral points of interest yet gives less spatial subtle elements from source images. Wavelet transform performs well at straight highlights yet not at non-direct discontinuities since Wavelets don't utilize the geometric properties of structures. Curvelet transforms defeat such troubles in include rep-resentation. A novel Image fusion rule by means of high pass balance utilizing Local Magnitude Ratio (LMR) in Fast Discrete Curvelet Transforms domain (FDCT) and Discrete wavelet transform (DWT) is characterized. Indian Remote Sensing Geo satellite images are utilized for MS and Pan images. This fusion rule creates HR multispectral image with high spatial resolution. This technique is contrasted and wavelet, Principal Component Analysis (PCA), Fast Discrete Curvelet Transforms domain fusion strategies. Master postured technique spatially performs alternate strategies and results rich multispectral information.


Keywords

Image Fusion, Discrete Wavelet Transforms, Fast Discrete Curvelet Transforms, Principal Component Analysis and Guided Filtering.

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References

Emmanuel.J. Candes, L. Demanet,D.Donoho and L.Ying. “Fast Discrete Curvelet transforms(FDCT)”,Caltech, Pasadena, CA, March 2006.

F. Nencini, A. Garzelli,S. Baronti,L. Alparone , “ Remote sensing image fusion”, Informaion Fusion, Vol 8, pp:143-156, May 2006.

Ying. Li, Xing. Xu,Ben-Du. Bai,Y.N. Zhang, “ Remote sensing image fusion based on Fast Discrete Curvelet Transform”, In International conference on machine learning and cybernetics, Kumming, China, July 2008.

A. Golibagh, Mehran. Yazdi,“ A novel image fusion method using curvelet transform based on linear dependency test”, In International conference on digital image processing, pp:351-354, Bangkok, Thailand, March 2009.

Shutao. Li, Bin. Yang,“Multi focus image fusion based on wavelet and curvelet transform”, Pattern recognition letters, Vol 29, pp:1295-1301, February 2008.

C.V. Rao, P.S. Reddy, D.S. Jain, K.M.M. Rao, “Quantitative value addition analysis of multisensory data fusion”, The ICFAI journal of earth sciences, Vol 1, No 1, pp:82-96, March 2007.

M.F. Yakhdani, A. Azizi, “Quality assessment of image fusion techniques for multi sensor high resolution satellite images(cas study: IRSP5 and IRSP6 Satellite images)”, In ISPRS TC VII Symposium, pp:205-209, Vienna, Austria, July 2010.

Jianwei. Ma, Gerlind. Plonka, “A Review of curvelet and recent applica-tions”, Caltech, Pasadena, CA, March 2006.

J. Zhou,D.L. Civico, J.A. Silander, “A Wavelet Transform method to mrege Landsat TM and SPOT Panchromatic data”, International Journal of Remote Sensing, Vol 19, No 4,pp:743-757, 1998.

G. Hong, Y. Zhang, “Comparison and improvement of Wavelet-based image fusion”, International Journal of Remote Sensing, Vol 29, No 3, pp:673-691, December 2007.

Chengzhi Deng, Hanqiang Cao, Chao Cao, Shengqian Wang, “Multisen-sor image fusion using fast discrete curvelet transform”, Remote Sensing, GIS Data Processing and Applications, Vol. 4, pp:6790-6790, 2007.

C.V. Rao, J. Malleswara Rao, A. Senthil Kumar, and A. S. Manjunath. “Restoration of high frequency details while constructing the high reso-lution image”, In India Conference (INDICON), Annual IEEE, pp:1-5 , 2011.

Natasa Terzija, Markus Repges, Kerstin Luck, Walter Geisselhardt, “ Digital Image Watremarking using Discrete Wavelet Transform”, IEEE.

EePing Ong. Weisi Lin, Zhongkaiig Lu, Xiaokang Yang, Susir Yao, Feng Pan, Lijilrn Jiarrg, and Fulvio Moscheni, “ A NO-REFERENCE QUALITY METRIC FOR MEASURING IMAGE BLUR ”, IEEE.


DOI: http://dx.doi.org/10.18063/som.v0i0.610
(291 Abstract Views, 118 PDF Downloads)

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