Bayesian fusion of hyperspectral and multispectral images

Wei, Qi; Dobigeon, Nicolas; Tourneret, Jean-Yves

doi:10.1109/icassp.2014.6854186

Cited by 57 publications

(42 citation statements)

References 29 publications

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“…Computational complexity is one of the major problems in super resolution approach and most of the researches concentrated on obtaining the high spatial resolution of the PAN or MS image and not the spectral information to be integrated. The statistical based approaches can be improved by accounting band dependent noise variances (Wei et al 2014). In addition, a model for the joint characterization of the actual HS image and fused HS image can also be proposed.…”

Section: Challenges In Spatial Spectral Integrationmentioning

confidence: 99%

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Comprehensive review on fusion techniques for spatial information enhancement in hyperspectral imagery

Mookambiga

Gomathi²

2016

Multidim Syst Sign Process

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The volume of data grows with the advent of multiple types of remote sensing sensors and in order to extract the most useful information there is a need to combine the data gathered from the different sources. The widely used panchromatic and multispectral imageries in many applications offer decimetric and metric spatial resolution. However, the spectral resolution of these images is poor. Hyperspectral imaging has unique characteristics of providing very fine spectral resolution in a large number of bands with decametric spatial resolution and found to be highly useful for a wide span of application areas that requires high spectral resolution. The fusion of spectral and spatial information provides an effective way of enhancing the spatial quality of hyperspectral imagery as well as a method for preserving spectral quality. This fusion process is not a trivial task as always there has been a tradeoff between the preservation of spatial and spectral quality information as in the original sources of fusion. In this paper, a review on hyperspectral pansharpening and hyperspectral multispectral fusion based approaches has been reported. The widely adopted quantitative and qualitative performance measures to verify the fusion results are highlighted. In addition, the challenges in existing fusion techniques have also been discussed.

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Section: Challenges In Spatial Spectral Integrationmentioning

confidence: 99%

“…The observation models (Hardie et al 2004;Wei et al 2014Wei et al , 2015b) associated with the HS and MS fusion can be written as…”

Section: Bayesian Fusionmentioning

confidence: 99%

Comprehensive review on fusion techniques for spatial information enhancement in hyperspectral imagery

Mookambiga

Gomathi²

2016

Multidim Syst Sign Process

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“…Inspired by such finding, various methods were proposed to enhance the performance of pan-sharpening [1,3,6,7,8,9,10], among which bayesian sparse representation of dictionary outstrips others in terms of recovery quality with great success [4]. This Bayesian model divides the fusion process into two stages where Markov chain Monte Carlo (MCMC) algorithm is used to figure out the pan-sharpened image.…”

Section: Introductionmentioning

confidence: 99%

Variational Bayesian image fusion based on combined sparse representations

Lin

Tao

et al. 2016

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Hyper-spectral image fusion has been a hot topic in medical imaging and remote sensing. This paper proposes a Bayesian fusion model which combines the panchromatic (PAN) image and the low spatial resolution hyper-spectral (HS) image under the same framework. Sparsity constraint is introduced as double "spike-and-slab" priors, and anisotropic Gaussian noise is adopted for accuracy. To achieve reduction in computational complexity, we turn the anisotropic Gaussian distribution into isotropic one with modified linear transformation and propose a variational Bayesian expectation maximization (EM) algorithm to calculate the result. Experiment results show that our solution can achieve comparable performance in pan-sharpening to other state-of-art algorithms while largely reducing the computational complexity.Index Terms-Hyper-spectral image fusion, sparse prior, computational complexity, anisotropic Gaussian distribution, variational Bayesian algorithm

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“…Note that this strategy showed interesting results for many image processing problems including image denoising [37], sparse image reconstruction [38], hyperspectral image unmixing [39], and fusion of hyperspectral and panchromatic images [40]. The next sections explain how to sample from the conditional distributions of the unknown parameters and hyperparameters associated with the posterior of interest (8).…”

Section: Markov Chain Monte Carlo Methodsmentioning

confidence: 99%

Sparse EEG Source Localization Using Bernoulli Laplacian Priors

Costa

Batatia

Chaâri

et al. 2015

IEEE Trans. Biomed. Eng.

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OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. This is an author-deposited version published in : http://oatao.univ-toulouse.fr/ Eprints ID : 14691 Abstract-Source localization in electroencephalography has received an increasing amount of interest in the last decade. Solving the underlying ill-posed inverse problem usually requires choosing an appropriate regularization. The usual 2 norm has been considered and provides solutions with low computational complexity. However, in several situations, realistic brain activity is believed to be focused in a few focal areas. In these cases, the 2 norm is known to overestimate the activated spatial areas. One solution to this problem is to promote sparse solutions for instance based on the 1 norm that are easy to handle with optimization techniques. In this paper, we consider the use of an 0 + 1 norm to enforce sparse source activity (by ensuring the solution has few nonzero elements) while regularizing the nonzero amplitudes of the solution. More precisely, the 0 pseudonorm handles the position of the nonzero elements while the 1 norm constrains the values of their amplitudes. We use a Bernoulli-Laplace prior to introduce this combined 0 + 1 norm in a Bayesian framework. The proposed Bayesian model is shown to favor sparsity while jointly estimating the model hyperparameters using a Markov chain Monte Carlo sampling technique. We apply the model to both simulated and real EEG data, showing that the proposed method provides better results than the 2 and 1 norms regularizations in the presence of pointwise sources. A comparison with a recent method based on multiple sparse priors is also conducted.Index Terms-Electroencephalography (EEG), inverse problem, 0 + 1 norm regularization, Markov chain monte carlo (MCMC), source localization, sparse Bayesian restoration.

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Bayesian fusion of hyperspectral and multispectral images

Cited by 57 publications

References 29 publications

Comprehensive review on fusion techniques for spatial information enhancement in hyperspectral imagery

Comprehensive review on fusion techniques for spatial information enhancement in hyperspectral imagery

Variational Bayesian image fusion based on combined sparse representations

Sparse EEG Source Localization Using Bernoulli Laplacian Priors

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