On the EM Algorithm and Bootstrap Approach Combination for Improving Satellite Image Fusion
This paper discusses EM algorithm and Bootstrap
approach combination applied for the improvement of the satellite
image fusion process. This novel satellite image fusion method based
on estimation theory EM algorithm and reinforced by Bootstrap
approach was successfully implemented and tested. The sensor
images are firstly split by a Bayesian segmentation method to
determine a joint region map for the fused image. Then, we use the
EM algorithm in conjunction with the Bootstrap approach to develop
the bootstrap EM fusion algorithm, hence producing the fused
targeted image. We proposed in this research to estimate the
statistical parameters from some iterative equations of the EM
algorithm relying on a reference of representative Bootstrap samples
of images. Sizes of those samples are determined from a new
criterion called 'hybrid criterion'. Consequently, the obtained results
of our work show that using the Bootstrap EM (BEM) in image
fusion improve performances of estimated parameters which involve
amelioration of the fused image quality; and reduce the computing
time during the fusion process.
[1] Z. Zhang and R. S. Blum, "A categorization of multiscale
decomposition based image fusion scheme with a performance study for
a digital camera application" Proceedings of the IEEE, Vol. 87, no.8,
pp.1315- 1326, August 1999.
[2] G. Lui, Z. L. Jing, J. X. Li, S. Y. Sun Z. H . Li and H. Leung, "image
fusion based on estimation theory", Proceedings of the third
international conference on machine learning and cybernetics, Shanghai,
pp. 26-29, August 2004.
[3] J. Yang and R. S. Blum, " a statistical signal processing approach to
image fusion for concealed weapon detection", Proceedings of the
IEEE international conference on image processing, Rochester, NY, pp.
513-516, 2002.
[4] G. Simone, A. Farina, L. Morabito, S. Serpico, and L. Bruzzon, "image
fusion techniques for remote sensing applications", information fusion,
Vol. 3, no.1, pp. 3-15, 2002.
[5] R. K. Sharma, "probabilistic model-based multisensor image fusion",
PHD dissertation, Oregon graduate institute, Portland, OR, October
1999.
[6] R. K. Sharma, T. K. Leen and M. Pavel, "probabilistic image sensor
fusion", advances in neural information processing systems, Vol.11, the
MIT Press, 1999.
[7] L. Bin and P. Jiaxiong, "image fusion method based on short support
symmetric non-separable wavelet", international journal on wavelets,
multiresolution and information processing, Vol.2, no.1, pp.87-98, 2004.
[8] Z. Zhang and R. S. Blum, "region-based image fusion scheme for
concealed weapon detection", proceedings of the 31st annual conference
on information sciences and systems, march 1997.
[9] J. J. Lewis, R. J. O-callaghan , S. G. Nikolov, D. R. Bull and C. N.
Canagarajah", region based image fusion using complex wavelats ", in 7
th international conference on information fusion , Stockholm, Sweden,
28 June, 2004.
[10] A. Akerman, "pyramidal techniques for multisensor fusion",
Proceedings of SPIE; Volume 1828 Sensor Fusion V, pp.124-131, USA
November 1992.
[11] J. Yang and R. S. Blum, "A Region based Image Fusion Method using
the Expectation-Maximization Algorithm", Information Sciences and
Systems, 40th annual conference on, pp. 468-473, 2006.
[12] S. G.Nikolov, J. J. Lews, R. G. O-Callaghan, D. R. Bull and C. N.
Canagarajah, "Hybrid Fused Displas: Between Pixel-and Region-Based
Image Fusion", the 7th International conference on information fusion,
Stokholm, Sweden, 2004.
[13] A. Toet and E. M. Franken, "Perceptual of different image fusion
schmes", Displays, Vol. 24, no. 1, pp. 15-37, February 2003.
[14] Z. Wang and A. C. Bovik, "A universal image quality index", IEEE
signal processing letters, Vol. 9, no.3, pp. 81-84, march 2002.
[15] G. Piella and H. Heijmans, "A new quality metric for image fusion",
Image processing Proceedings, Vol. 3, pp. 173-6, 2003.
[16] M. Zribi, "Non parametric and unsupervised Bayesia classification with
Bootstrap sampling", Image and Vision Computing, 22, pp.1-8, 2004.
[17] M. Zribi, F. Ghorbal, "An unsupervised and nonparametric Bayesian
classifier", Pattern Recognition Letters, Vol. 24 pp. 97-112, 2003.
[18] B. Efron, "Bootstrap methods: another look at the Jackknife", Ann. Stat.
7 (1), 1979.
[19] B. Efron, R. J. Tibshirani, "An introduction to the bootstrap",
Monographs on Statistics and Applied Probability, 57, 1993.
[20] I. Buvat, "Introduction à l-approche Bootstrap", U494 INSERM, 25
septembre, 2005.
[21] C. Banga, "L-approche Bootstrap en analyse des images : Application à
la restitution de la cinétique de la fuite en choriorétinopathie Séreuse
Centrale", Thèse de doctorat, université de Rennes I, 1995.
[22] R. Veysseyre, Statistique et probabilité pour l-ingénieur, Dunod, Paris
2001.
[23] C. Ambois, "Introduction a la reconnaissance statistique des formes",
Centre de Géostatique, Ecole des mines de Paris, mars1997.
[24] G. J. McLachlan, T. Krishnan, The EM Algorithm and Extensions, John
Wiley and Son-s Inc., New York, 1997.
[25] N. Kehtarnavaz, E. Nakamura, "Generalization of the EM algorithm for
mixture density estimation", Pattern Recognition Letters 19, pp. 133-
140, 1998.
[26] A. German, M. R. Jenkin, and Y. Lesperance, " Entropy-Based Image
Merging", The 2nd Canadian Conference on Computer and Robot
Vision (CRV'05) Toronto, Ontario, Canada, pp. 81-86, 2005.
[27] T. Delleji, Mourad Zribi and Ahmed ben Hamida, "Application of
bootstrap approach Unsupervised Statistical Segmentation of Satellite
Images", 4th International Multi-Conference on Systems, Signals &
Devices; SSD-2007 March 19-22, Hammamet (Tunisia), 2007.
[1] Z. Zhang and R. S. Blum, "A categorization of multiscale
decomposition based image fusion scheme with a performance study for
a digital camera application" Proceedings of the IEEE, Vol. 87, no.8,
pp.1315- 1326, August 1999.
[2] G. Lui, Z. L. Jing, J. X. Li, S. Y. Sun Z. H . Li and H. Leung, "image
fusion based on estimation theory", Proceedings of the third
international conference on machine learning and cybernetics, Shanghai,
pp. 26-29, August 2004.
[3] J. Yang and R. S. Blum, " a statistical signal processing approach to
image fusion for concealed weapon detection", Proceedings of the
IEEE international conference on image processing, Rochester, NY, pp.
513-516, 2002.
[4] G. Simone, A. Farina, L. Morabito, S. Serpico, and L. Bruzzon, "image
fusion techniques for remote sensing applications", information fusion,
Vol. 3, no.1, pp. 3-15, 2002.
[5] R. K. Sharma, "probabilistic model-based multisensor image fusion",
PHD dissertation, Oregon graduate institute, Portland, OR, October
1999.
[6] R. K. Sharma, T. K. Leen and M. Pavel, "probabilistic image sensor
fusion", advances in neural information processing systems, Vol.11, the
MIT Press, 1999.
[7] L. Bin and P. Jiaxiong, "image fusion method based on short support
symmetric non-separable wavelet", international journal on wavelets,
multiresolution and information processing, Vol.2, no.1, pp.87-98, 2004.
[8] Z. Zhang and R. S. Blum, "region-based image fusion scheme for
concealed weapon detection", proceedings of the 31st annual conference
on information sciences and systems, march 1997.
[9] J. J. Lewis, R. J. O-callaghan , S. G. Nikolov, D. R. Bull and C. N.
Canagarajah", region based image fusion using complex wavelats ", in 7
th international conference on information fusion , Stockholm, Sweden,
28 June, 2004.
[10] A. Akerman, "pyramidal techniques for multisensor fusion",
Proceedings of SPIE; Volume 1828 Sensor Fusion V, pp.124-131, USA
November 1992.
[11] J. Yang and R. S. Blum, "A Region based Image Fusion Method using
the Expectation-Maximization Algorithm", Information Sciences and
Systems, 40th annual conference on, pp. 468-473, 2006.
[12] S. G.Nikolov, J. J. Lews, R. G. O-Callaghan, D. R. Bull and C. N.
Canagarajah, "Hybrid Fused Displas: Between Pixel-and Region-Based
Image Fusion", the 7th International conference on information fusion,
Stokholm, Sweden, 2004.
[13] A. Toet and E. M. Franken, "Perceptual of different image fusion
schmes", Displays, Vol. 24, no. 1, pp. 15-37, February 2003.
[14] Z. Wang and A. C. Bovik, "A universal image quality index", IEEE
signal processing letters, Vol. 9, no.3, pp. 81-84, march 2002.
[15] G. Piella and H. Heijmans, "A new quality metric for image fusion",
Image processing Proceedings, Vol. 3, pp. 173-6, 2003.
[16] M. Zribi, "Non parametric and unsupervised Bayesia classification with
Bootstrap sampling", Image and Vision Computing, 22, pp.1-8, 2004.
[17] M. Zribi, F. Ghorbal, "An unsupervised and nonparametric Bayesian
classifier", Pattern Recognition Letters, Vol. 24 pp. 97-112, 2003.
[18] B. Efron, "Bootstrap methods: another look at the Jackknife", Ann. Stat.
7 (1), 1979.
[19] B. Efron, R. J. Tibshirani, "An introduction to the bootstrap",
Monographs on Statistics and Applied Probability, 57, 1993.
[20] I. Buvat, "Introduction à l-approche Bootstrap", U494 INSERM, 25
septembre, 2005.
[21] C. Banga, "L-approche Bootstrap en analyse des images : Application à
la restitution de la cinétique de la fuite en choriorétinopathie Séreuse
Centrale", Thèse de doctorat, université de Rennes I, 1995.
[22] R. Veysseyre, Statistique et probabilité pour l-ingénieur, Dunod, Paris
2001.
[23] C. Ambois, "Introduction a la reconnaissance statistique des formes",
Centre de Géostatique, Ecole des mines de Paris, mars1997.
[24] G. J. McLachlan, T. Krishnan, The EM Algorithm and Extensions, John
Wiley and Son-s Inc., New York, 1997.
[25] N. Kehtarnavaz, E. Nakamura, "Generalization of the EM algorithm for
mixture density estimation", Pattern Recognition Letters 19, pp. 133-
140, 1998.
[26] A. German, M. R. Jenkin, and Y. Lesperance, " Entropy-Based Image
Merging", The 2nd Canadian Conference on Computer and Robot
Vision (CRV'05) Toronto, Ontario, Canada, pp. 81-86, 2005.
[27] T. Delleji, Mourad Zribi and Ahmed ben Hamida, "Application of
bootstrap approach Unsupervised Statistical Segmentation of Satellite
Images", 4th International Multi-Conference on Systems, Signals &
Devices; SSD-2007 March 19-22, Hammamet (Tunisia), 2007.
@article{"International Journal of Information, Control and Computer Sciences:52590", author = "Tijani Delleji and Mourad Zribi and Ahmed Ben Hamida", title = "On the EM Algorithm and Bootstrap Approach Combination for Improving Satellite Image Fusion", abstract = "This paper discusses EM algorithm and Bootstrap
approach combination applied for the improvement of the satellite
image fusion process. This novel satellite image fusion method based
on estimation theory EM algorithm and reinforced by Bootstrap
approach was successfully implemented and tested. The sensor
images are firstly split by a Bayesian segmentation method to
determine a joint region map for the fused image. Then, we use the
EM algorithm in conjunction with the Bootstrap approach to develop
the bootstrap EM fusion algorithm, hence producing the fused
targeted image. We proposed in this research to estimate the
statistical parameters from some iterative equations of the EM
algorithm relying on a reference of representative Bootstrap samples
of images. Sizes of those samples are determined from a new
criterion called 'hybrid criterion'. Consequently, the obtained results
of our work show that using the Bootstrap EM (BEM) in image
fusion improve performances of estimated parameters which involve
amelioration of the fused image quality; and reduce the computing
time during the fusion process.", keywords = "Satellite image fusion, Bayesian segmentation,
Bootstrap approach, EM algorithm.", volume = "2", number = "11", pages = "3700-10", }
