Optimal MMSE Pan Sharpening of Very High Resolution Multispectral Images

Garzelli, Andrea; Nencini, F.; Capobianco, Luca

doi:10.1109/tgrs.2007.907604

Cited by 477 publications

(246 citation statements)

References 23 publications

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“…Some recent papers, to avoid improper modeling, recast the problem in an optimization framework. In [25], a different MMSE-optimal detail image is extracted for each MS band, by evaluating band-dependent generalized intensities. This method was extended in [26], where parameter estimation is performed through nonlocal parameter optimization based on K-means clustering.…”

Section: Introductionmentioning

confidence: 99%

Pansharpening by Convolutional Neural Networks

et al. 2016

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Abstract:A new pansharpening method is proposed, based on convolutional neural networks. We adapt a simple and effective three-layer architecture recently proposed for super-resolution to the pansharpening problem. Moreover, to improve performance without increasing complexity, we augment the input by including several maps of nonlinear radiometric indices typical of remote sensing. Experiments on three representative datasets show the proposed method to provide very promising results, largely competitive with the current state of the art in terms of both full-reference and no-reference metrics, and also at a visual inspection.

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Section: Introductionmentioning

confidence: 99%

Pansharpening by Convolutional Neural Networks

et al. 2016

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“…In BDSD, the fusion parameter is designed based on the MVU (Minimum-Variance-Unbiased) estimator using the panchromatic image, resampled multispectral bands, and spatially degraded multispectral bands (Garzelli et al, 2008). BDSD can be used in the local and global injection model, similar to the CS-based pansharpening model.…”

Section: Bdsd (Band-dependent Spatial-detail With Local Parameter Estmentioning

confidence: 99%

Performance Evaluation of Pansharpening Algorithms for WorldView-3 Satellite Imagery

Hyeok¹,

Park²,

Hee³

et al. 2016

Journal of the Korean Society of Surveying, Geodesy, Photogramm

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Worldview-3 satellite sensor provides panchromatic image with high-spatial resolution and 8-band multispectral images. Therefore, an image-sharpening technique, which sharpens the spatial resolution of multispectral images by using high-spatial resolution panchromatic images, is essential for various applications of Worldview-3 images based on image interpretation and processing. The existing pansharpening algorithms tend to tradeoff between spectral distortion and spatial enhancement. In this study, we applied six pansharpening algorithms to Worldview-3 satellite imagery and assessed the quality of pansharpened images qualitatively and quantitatively. We also analyzed the effects of time lag for each multispectral band during the pansharpening process. Quantitative assessment of pansharpened images was performed by comparing ERGAS (Erreur Relative Globale Adimensionnelle de Synthèse), SAM (Spectral Angle Mapper), Q-index and sCC (spatial Correlation Coefficient) based on real data set. In experiment, quantitative results obtained by MRA (Multi-Resolution Analysis)-based algorithm were better than those by the CS (Component Substitution)-based algorithm. Nevertheless, qualitative quality of spectral information was similar to each other. In addition, images obtained by the CS-based algorithm and by division of two multispectral sensors were shaper in terms of spatial quality than those obtained by the other pansharpening algorithm. Therefore, there is a need to determine a pansharpening method for Worldview-3 images for application to remote sensing data, such as spectral and spatial information-based applications. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http:// creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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“…In addition, recently the numerical and optimization methods were designed to calculate the final fusion results according to the ideal imaging conditions and assessment criteria [65][66][67][68][69][70][71]. A statistical approach, University of New Brunswick (UNB)-pansharp, is presented in [72].…”

Section: A Short Overview Of Existing Pan-sharpening Algorithmsmentioning

confidence: 99%

“…(6) Optimization and numerical computing: more than 24, such as those algorithms [65][66][67][68][69][70].…”

Section: The Optimal Selection Of the Number Of Processorsmentioning

confidence: 99%

A Parallel Computing Paradigm for Pan-Sharpening Algorithms of Remotely Sensed Images on a Multi-Core Computer

2014

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Pan-sharpening algorithms are data-and computation-intensive, and the processing performance can be poor if common serial processing techniques are adopted. This paper presents a parallel computing paradigm for pan-sharpening algorithms based on a generalized fusion model and parallel computing techniques. The developed modules, including eight typical pan-sharpening algorithms, show that the framework can be applied to implement most algorithms. The experiments demonstrate that if parallel strategies are adopted, in the best cases the fastest times required to finish the entire fusion operation (including disk input/output (I/O) and computation) are close to the time required to directly read and write the images without any computation. The parallel processing implemented on a workstation with two CPUs is able to perform these operations up to 13.9 times faster than serial execution. An algorithm in the framework is 32.6 times faster than the corresponding version in the ERDAS IMAGINE software. Additionally, no obvious differences in the fusion effects are observed between the fusion results of different implemented versions.

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Optimal MMSE Pan Sharpening of Very High Resolution Multispectral Images

Cited by 477 publications

References 23 publications

Pansharpening by Convolutional Neural Networks

Pansharpening by Convolutional Neural Networks

Performance Evaluation of Pansharpening Algorithms for WorldView-3 Satellite Imagery

A Parallel Computing Paradigm for Pan-Sharpening Algorithms of Remotely Sensed Images on a Multi-Core Computer

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