Spatial Resolution Enhancement for Hyperspectral Image Using Gaussian Scale Mixtures

Abstract:

In this paper, a wavelet-based Bayesian fusion framework is presented, in which a low spatial resolution hyperspectral (HS) image is fused with a high spatial resolution multispectral (MS) image. Particularly, a multivariate model, Gaussian Scale Mixture (GSM) model, is employed, which is believed to be capable of modeling the distribution of wavelet coefficients more accurately. A practical implementation scheme is also presented for feasible calculations. The proposed approach is validated by simulation experiments for HS and MS image fusion. The experimental results of the proposed approach are also compared with its counterpart employing a Gaussian model for performance evaluation.

• Periodical: Applied Mechanics and Materials (Volumes 713-715)
• Pages: 1926-1930
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.713-715.1926
• Online since: January 2015
• Authors: Jie Liu*, Yi Fan Zhang
• Keywords: Gaussian Scale Mixture (GSM), Hyperspectral, Remote Sensing

References

[1] C. -I. Chang, Hyperspectral imaging: techniques for spectral detection and classification. Kluwer Academic Publishers, (2003).

[2] R. Gomez, A. Jazaeri, and M. Kafatos, Wavelet-based hyperspectral and multi-spectral image fusion, Proc. SPIE, vol. 4383, pp.36-42, (2001).

[3] Y. Zhang, S. De Backer, and P. Scheunders, Noise-resistant wavelet-based Bayesian fusion of multispectral and hyperspectral images, IEEE Transaction on Geoscience and Remote Sensing, vol. 47, no. 11, pp.3834-3843, (2009).

DOI: 10.1109/tgrs.2009.2017737

[4] J. Portilla, et al., Image denoising using scale mixtures of Gaussians in the wavelet domain, IEEE Transaction on Image Processing, vol. 12, no. 11, pp.1338-1351, (2003).

DOI: 10.1109/tip.2003.818640

[5] J. Portilla and E. Simoncelli, Image restoration using Gaussian scale mixtures in the wavelet domain, Proceedings of 2003 IEEE International Conference on Image Processing, vol. 2, pp.965-968, (2003).

DOI: 10.1109/icip.2003.1246844

[6] P. Scheunders and S. De Backer, Wavelet denoising of multicomponent images using Gaussian scale mixture models and a noise-free image as priors, IEEE Transaction on Image Processing, vol. 16, no. 7, pp.1865-1872, (2007).

DOI: 10.1109/tip.2007.899598

[7] S. Mallat, A wavelet tour of signal processing. London, U. K.: Academic Press, (1998).

[8] P. Flandrin, Time-Frequency/Time-Scale Analysis. San Diego, CA: Academic Press, (1999).

[9] D. Donoho and I. Johnstone, Adapting to unknown smoothness via wavelet shrinking, Journal of the American Statistical Association, vol. 90, no. 432, pp.1200-1224, (1995).

DOI: 10.1080/01621459.1995.10476626

[10] A. Pizurica, et al., Wavelet domain denoising of multispectral remote sensing imagery adapted to the local spatial and spectral context, Proc. of 2005 IEEE International Geoscience & Remote Sensing Symposium, vol. 6, pp.4260-4263, (2005).

DOI: 10.1109/igarss.2005.1525859

[11] M. Figeiredo and R. Nowak, Wavelet-based image estimation: An empirical Bayes approach using Jeffrey's noninformative prior, IEEE Transaction on Image Processing, vol. 10, no. 9, pp.1322-1331, (2001).

DOI: 10.1109/83.941856

[12] A. Duijster, P. Scheunders, and S. De Backer, Wavelet-based EM algorithm for multispectral-image restoration, IEEE Transaction on Geoscience and Remote Sensing, vol. 47, no. 11, p.3892–3898, (2009).

DOI: 10.1109/tgrs.2009.2031103