Multispectral image data fusion under a Bayesian approach

Mascarenhas, Nelson D. A.; Banon, Gerald Jean Francis; Candeias, Ana Lúcia Bezerra

doi:10.1080/01431169608948717

Cited by 37 publications

(10 citation statements)

References 3 publications

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“…Since the work of Mascarenhas [37], a number of pansharpening methods have been proposed using the Bayesian framework (see [72,73] …”

Section: Methods Based On the Statistics Of The Imagementioning

confidence: 99%

“…Besides the Price method, the literature shows other pansharpening methods based on the statistical properties of the images, such as spatially adaptive methods [36] and Bayesian-based methods [37,38].…”

Section: Pansharpening Categoriesmentioning

confidence: 99%

“…This prior has also been used by Hardie et al [74]. In [37], the prior information is carried out by an interpolation operator and its covariance matrix; both will be used as the mean vector and the covariance matrix, respectively, for a Bayesian synthesis process. In [75], the prior knowledge about the smoothness of the object luminosity distribution within each band makes it possible to model the distribution of z using a simultaneous autoregressive model (SAR) as…”

Section: For Instance)mentioning

confidence: 99%

“…The degradation model in [37] can be also written in this way. A pansharpened image using the super-resolution method proposed in [76] is shown in Figure 3b.…”

Section: For Instance)mentioning

confidence: 99%

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A survey of classical methods and new trends in pansharpening of multispectral images

Amro

Mateos

Vega

et al. 2011

EURASIP J. Adv. Signal Process.

222

164

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There exist a number of satellites on different earth observation platforms, which provide multispectral images together with a panchromatic image, that is, an image containing reflectance data representative of a wide range of bands and wavelengths. Pansharpening is a pixel-level fusion technique used to increase the spatial resolution of the multispectral image while simultaneously preserving its spectral information. In this paper, we provide a review of the pan-sharpening methods proposed in the literature giving a clear classification of them and a description of their main characteristics. Finally, we analyze how the quality of the pansharpened images can be assessed both visually and quantitatively and examine the different quality measures proposed for that purpose.

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“…Since the work of Mascarenhas [37], a number of pansharpening methods have been proposed using the Bayesian framework (see [72,73] …”

Section: Methods Based On the Statistics Of The Imagementioning

confidence: 99%

Section: Pansharpening Categoriesmentioning

confidence: 99%

Section: For Instance)mentioning

confidence: 99%

“…The degradation model in [37] can be also written in this way. A pansharpened image using the super-resolution method proposed in [76] is shown in Figure 3b.…”

Section: For Instance)mentioning

confidence: 99%

See 2 more Smart Citations

A survey of classical methods and new trends in pansharpening of multispectral images

Amro

Mateos

Vega

et al. 2011

EURASIP J. Adv. Signal Process.

222

164

View full text Add to dashboard Cite

show abstract

“…Theory and approaches adopted for multi-source fusion include Bayesian decision theory (Mascarenhas et al 1996), Shafer's theory of evidence (Lee et al 1987), a statistical approach (Benediktsson et al 1990, Stein 2005) and neural networks (Benediktsson et al 1990, Bruzzone 1999. Lee et al (1987) utilised two methods for combining the information from multiple sources of remote sensing image data and spatial data in general.…”

Section: Fusion Of Optical Images and Sarmentioning

confidence: 99%

Multi-source remote sensing data fusion: status and trends

Zhang

2010

International Journal of Image and Data Fusion

567

312

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With the fast development of remote sensor technologies, e.g. the appearance of Very High Resolution (VHR) optical sensors, SAR, LiDAR, etc., mounted on either airborne or spaceborne platforms, multi-source remote sensing data fusion techniques are emerging due to the demand for new methods and algorithms. The general fusion techniques have been well developed and applied in various fields ranging from satellite earth observation to computer vision, medical image processing, defence security and so on. Despite the fast development, the techniques remain challenging for multi-source data fusion within varying spatial and temporal resolutions. This article reviews current techniques of multi-source remote sensing data fusion and discusses their future trends and challenges through the concept of hierarchical classification, i.e., pixel/data level, feature level and decision level. This article concentrates on discussing optical panchromatic and multi-spectral data fusing methods. So far, the pixel level fusion methods have mainly focused on optical data fusion; high-level fusion includes feature level and decision level fusion of multi-source data, such as synthetic aperture radar, optical images, LiDAR and other types of data. Finally, this article summarises several trends tending to broaden the application of multi-source data fusion.

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Fusion of MRI and CT images using guided image filter and image statistics

Bavirisetti

Kollu

Xiao

et al. 2017

Int J Imaging Syst Tech

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In medical imaging using different modalities such as MRI and CT, complementary information of a targeted organ will be captured. All the necessary information from these two modalities has to be integrated into a single image for better diagnosis and treatment of a patient. Image fusion is a process of combining useful or complementary information from multiple images into a single image. In this article, we present a new weighted average fusion algorithm to fuse MRI and CT images of a brain based on guided image filter and the image statistics. The proposed algorithm is as follows: detail layers are extracted from each source image by using guided image filter. Weights corresponding to each source image are calculated from the detail layers with help of image statistics. Then a weighted average fusion strategy is implemented to integrate source image information into a single image. Fusion performance is assessed both qualitatively and quantitatively. Proposed method is compared with the traditional and recent image fusion methods. Results showed that our algorithm yields superior performance.

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Multispectral image data fusion under a Bayesian approach

Cited by 37 publications

References 3 publications

A survey of classical methods and new trends in pansharpening of multispectral images

A survey of classical methods and new trends in pansharpening of multispectral images

Multi-source remote sensing data fusion: status and trends

Fusion of MRI and CT images using guided image filter and image statistics

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