Parameter estimation in Bayesian super-resolution pansharpening using contourlets

Abstract-The development of multisensor systems in recent years has led to great increase in the amount of available remote sensing data. Image fusion techniques aim at inferring high quality images of a given area from degraded versions of the same area obtained by multiple sensors. This paper focuses on pansharpening, which is the inference of a high spatial resolution multispectral image from two degraded versions with complementary spectral and spatial resolution characteristics: 1) a low spatial resolution multispectral image and 2) a high spatial resolution panchromatic image. We introduce a new variational model based on spatial and spectral sparsity priors for the fusion. In the spectral domain, we encourage low-rank structure, whereas in the spatial domain, we promote sparsity on the local differences. Given the fact that both panchromatic and multispectral images are integrations of the underlying continuous spectra using different channel responses, we propose to exploit appropriate regularizations based on both spatial and spectral links between panchromatic and fused multispectral images. A weighted version of the vector total variation norm of the data matrix is employed to align the spatial information of the fused image with that of the panchromatic image. With regard to spectral information, two different types of regularization are proposed to promote a soft constraint on the linear dependence between the panchromatic and fused multispectral images. The first one estimates directly the linear coefficients from the observed panchromatic and low-resolution multispectral images by linear regression while the second one employs the principal component pursuit to obtain a robust recovery of the underlying low-rank structure. We also show that the two regularizers are strongly related. The basic idea of both regularizers is that the J. Chanussot is with the GIPSA-Laboratory, University of Grenoble, Grenoble F-38402, France, and also with the Faculty of Electrical and Computer Engineering, University of Iceland, Reykjavik 101, Iceland (e-mail: jocelyn.chanussot@gipsa-lab.grenoble-inp.fr).This paper has supplementary downloadable material available at http://ieeexplore.ieee.org., provided by the author. The supplementary material contains the details of the parameter selection and the algorithm performance of the two proposed methods. It can also be downloaded at http://www.gipsalab.fr/∼jocelyn.chanussot/SupplementaryMaterial.pdf. The total size of the file is 82.5 kB. Contact greenhxy@gmail.com for further questions about this work.Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee. Index Terms-Image fusion, pansharpening, remote sensing, principal component pursuit, total variation, low rank recovery, convex optimization, proximal splitting method, split augmented Lagrangian shrinkage (SALSA).

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“…6 Implemented by us. 7 http://www.gipsa-lab.fr/jocelyn.chanussot/SupplementaryMaterial.pdf [20]. (e) AWLP fused image [23].…”

Section: A Parameter Settingmentioning

confidence: 99%

A New Pansharpening Method Based on Spatial and Spectral Sparsity Priors

Condat

Bioucas-Dias

et al. 2014

IEEE Trans. on Image Process.

148

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“…The High-Pass Filtering (HPF) algorithm in [ 11 , 18 ], can be considered to be the first approach in this category where only two levels are considered. Multi-scale decompositons, such as the wavelet transform (WT) [ 26 , 27 , 28 ], the Generalized Laplacian Pyramid (GLP) [ 29 , 30 , 31 ] or the Non-Subsampled Contourlet Transform (NSCT) [ 32 , 33 , 34 ], were used to bring more precision to the methods. The “a trous” wavelet transform (AWT) was the preferred decomposition technique [ 26 , 28 ] until the publication of [ 31 ] showed the advantages of GLP over AWT.…”

Section: Related Workmentioning

confidence: 99%

Variational Bayesian Pansharpening with Super-Gaussian Sparse Image Priors

Perez-Bueno

Vega

Mateos

et al. 2020

Sensors

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Pansharpening is a technique that fuses a low spatial resolution multispectral image and a high spatial resolution panchromatic one to obtain a multispectral image with the spatial resolution of the latter while preserving the spectral information of the multispectral image. In this paper we propose a variational Bayesian methodology for pansharpening. The proposed methodology uses the sensor characteristics to model the observation process and Super-Gaussian sparse image priors on the expected characteristics of the pansharpened image. The pansharpened image, as well as all model and variational parameters, are estimated within the proposed methodology. Using real and synthetic data, the quality of the pansharpened images is assessed both visually and quantitatively and compared with other pansharpening methods. Theoretical and experimental results demonstrate the effectiveness, efficiency, and flexibility of the proposed formulation.

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Parameter estimation in Bayesian super-resolution pansharpening using contourlets

Cited by 2 publications

References 5 publications

A New Pansharpening Method Based on Spatial and Spectral Sparsity Priors

A New Pansharpening Method Based on Spatial and Spectral Sparsity Priors

Variational Bayesian Pansharpening with Super-Gaussian Sparse Image Priors

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