Best Tradeoff for High-Resolution Image Fusion to Preserve Spatial Details and Minimize Color Distortion

Tu, Te-Ming; Cheng, Wen-Chun; Chang, Chien-Ping; Huang, Ping; Chang, Jyh-Chian

doi:10.1109/lgrs.2007.894143

Cited by 52 publications

(23 citation statements)

References 10 publications

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“…Previous studies have introduced similar weighting parameters s to balance the contributions from different information sources [41,44,54] but lack of detail explanation. With a parameter t, Choi's method [41] can be rewritten into the general CS framework by setting [44] introduced a parameter l, which can be written into the general CS framework by setting…”

Section: Cs Methods From a Bayesian Perspectivementioning

confidence: 99%

“…With a parameter t, Choi's method [41] can be rewritten into the general CS framework by setting [44] introduced a parameter l, which can be written into the general CS framework by setting…”

Section: Cs Methods From a Bayesian Perspectivementioning

confidence: 99%

“…Many CS method developers believed this tradeoff and tried to balance this tradeoff by introducing weight parameters to control the spatial injection [41,43,44]. Some MRA method developers implicitly assumed this tradeoff as they used optimization technologies with competitive objectives, i.e., to maximize the spatial similarity to the Pan image and the spectral similarity to the up-sampled MS images [45] to determine the best injection model [45][46][47].…”

Section: Introductionmentioning

confidence: 99%

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A New Look at Image Fusion Methods from a Bayesian Perspective

Zhang

Huang

2015

Remote Sensing

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Component substitution (CS) and multi-resolution analysis (MRA) are the two basic categories in the extended general image fusion (EGIF) framework for fusing panchromatic (Pan) and multispectral (MS) images. Despite of the method diversity, there are some unaddressed questions and contradictory conclusions about fusion. For example, is the spatial enhancement of CS methods better than MRA methods? Is spatial enhancement and spectral preservation competitive? How to achieve spectral consistency defined by Wald et al. in 1997? In their definition any synthetic image should be as identical as possible to the original image once degraded to its original resolution. To answer these questions, this research first finds out that all the CS and MRA methods can be derived from the Bayesian fusion method by adjusting a weight parameter to balance contributions from the spatial injection and spectral preservation models. The spectral preservation model assumes a Gaussian distribution of the desired high-resolution MS images, with the up-sampled low-resolution MS images comprising the mean value. The spatial injection model assumes a linear correlation between Pan and MS images. Thus the spatial enhancement depends on the weight parameter but is irrelevant of which category (i.e., MRA or CS) the method belongs to. This paper then adds a spectral consistency model in the Bayesian fusion framework to guarantee Wald's spectral consistency with OPEN ACCESSRemote Sens. 2015, 7 6829 regard to arbitrary sensor point spread function. Although the spectral preservation in the EGIF methods is competitive to spatial enhancement, the Wald's spectral consistency property is complementary with spatial enhancement. We conducted experiments on satellite images acquired by the QuickBird and WorldView-2 satellites to confirm our analysis, and found that the performance of the traditional EGIF methods improved significantly after adding the spectral consistency model.

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Section: Cs Methods From a Bayesian Perspectivementioning

confidence: 99%

Section: Cs Methods From a Bayesian Perspectivementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A New Look at Image Fusion Methods from a Bayesian Perspective

Zhang

Huang

2015

Remote Sensing

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“…Huge effort has been put in developing fusion methods that preserve the spectral information and increase detail information in the hybrid product produced by fusion process. Methods based on IHS transform (Choi, 2006;Schetselaar, 1998;Tu et al, 2001a, 2001b, Tu et al, 2004Tu et al, 2007) and Principal Components Analysis (PCA) (Chavez, 1989) probably are the most popular approaches used to enhance the spatial resolution of multispectral images with panchromatic images. However, both methods suffer from the problem that the radiometry on the spectral channels is modified after fusion.…”

Section: Fusion Methodsmentioning

confidence: 99%

Image Fusion for Remote Sensing Applications

Fonseca¹,

Namikawa²,

Castejon³

et al. 2011

Image Fusion and Its Applications

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“…Some approaches have proposed an external mechanism to the fusion method (Lillo-Saavedra et al, 2005;Yunhao et al, 2006), and others have introduced modifications to known methods (LilloSaavedra and Gonzalo, 2006a;Choi, 2006;Tu et al, 2007). New strategies that intrinsically provide this control have been proposed (Choi et al, 2005;Lillo-Saavedra and Gonzalo, 2007).…”

Section: Introductionmentioning

confidence: 99%

A directed search algorithm for setting the spectral–spatial quality trade-off of fused images by the wavelet à trous method

Gonzalo¹,

Lillo‐Saavedra²

2008

Canadian Journal of Remote Sensing

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Abstract. This paper proposes a method to determine, in an objective and accurate way, the weighting factor (α) to be applied to the detailed panchromatic image information that will be integrated with the background multispectral image information to obtain the "best" fused image with the same spatial and spectral quality. The fusion method is a weighting variant of the fusion algorithm based on the wavelet transform, calculated using the à trous (WAT) algorithm. The α factor is determined, for each band of the multispectral source images using the simulated annealing (SA) search algorithm, which optimizes an objective function (OF) associated with both spatial and spectral quality measures for the fused images. The results obtained have demonstrated that for each one of the spectral bands there is an α value that provides fused images with the optimal trade-off between the two qualities for any decomposition level value (n) of the wavelet transform.Résumé. Dans cet article, on propose une méthode pour déterminer, de façon objective et précise, le facteur de pondération (α) à appliquer à l'information détaillée d'une image panchromatique qui sera intégrée à l'information de l'image multispectrale de base afin d'obtenir la « meilleure » image fusionnée avec la même qualité spatiale et spectrale. La méthode de fusion est une variante de pondération de l'algorithme de fusion basé sur la transformée en ondelettes, calculée à l'aide de l'algorithme à trous. Le facteur α est déterminé, pour chaque bande des images multispectrales sources, en utilisant l'algorithme de recherche du recuit simulé (SA), qui optimise la fonction objectif (FO) associée avec les mesures de la qualité spatiale et spectrale des images fusionnées. Les résultats obtenus ont démontré que, pour chacune des bandes spectrales, il y a une valeur de α qui fournit aux images fusionnées un compromis optimal entre les deux qualités pour toute valeur de niveau de décomposition (n) de la transformée en ondelettes. [Traduit par la Rédaction]

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Best Tradeoff for High-Resolution Image Fusion to Preserve Spatial Details and Minimize Color Distortion

Cited by 52 publications

References 10 publications

A New Look at Image Fusion Methods from a Bayesian Perspective

A New Look at Image Fusion Methods from a Bayesian Perspective

Image Fusion for Remote Sensing Applications

A directed search algorithm for setting the spectral–spatial quality trade-off of fused images by the wavelet à trous method

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