Comparison of Pansharpening Algorithms: Outcome of the 2006 GRS-S Data-Fusion Contest

Alparone, Luciano; Wald, Lucien; Chanussot, Jocelyn; Thomas, Claire; Gamba, Paolo; Bruce, Lori

doi:10.1109/tgrs.2007.904923

Cited by 761 publications

(375 citation statements)

References 26 publications

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“…To obtain better coregistered upscaled images, [18] uses the Induction scaling technique, followed by a new fusion technique called Indusion. Other methods following the same philosophy work in a MRA setting, e.g., ATWT [15,[19][20][21] or with LP representations [16,[22][23][24]. Some recent papers, to avoid improper modeling, recast the problem in an optimization framework.…”

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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“…SAM [16], ERGAS [5], Q4 [16] cannot be used. Therefore we adopt the QNR metric proposed in [17] to examine the quality of the fused image.…”

Section: Comparison To Other Pansharpening Methodsmentioning

confidence: 99%

“…Classical pansharpening techniques can be divided into two categories [5]: the component substitution methods [6,7,8] and the multiresolution-analysis methods [9,10]. The component substitution methods first make use of a projection of the upsampled MS images to obtain a better representation of which one component contains most of the image structures.…”

Section: Introductionmentioning

confidence: 99%

Pansharpening using total variation regularization

Condat

Chanussot

et al. 2012

2012 IEEE International Geoscience and Remote Sensing Symposium

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In remote sensing, pansharpening refers to the technique that combines the complementary spectral and spatial resolution characteristics of a multispectral image and a panchromatic image, with the objective to generate a high-resolution color image. This paper presents a new pansharpening method based on the minimization of a variant of total variation. We consider the fusion problem as the colorization of each pixel in the panchromatic image. A new term concerning the gradient of the panchromatic image is introduced in the functional of total variation so as to preserve edges. Experimental results on IKONOS satellite images demonstrate the effectiveness of the proposed method.

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“…The multi-modality aspect considers the whole set of modalities and assesses whether its multimodality properties are close to those of the reference ensemble. Likewise, this protocol takes into account two properties that should be checked for individual modality as well as for the multi-modality set: For assessment of the fusion process results we use the following metrics (Alparone et al, 2007;Thomas and Wald, 2007;Wald, 2002):…”

Section: Quality Assessmentmentioning

confidence: 99%

Quality assessment by region in spot images fused by means dual-tree complex wavelet transform

Renza

Martínez

Arquero

2011

Advances in Space Research

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This work is motivated in providing and evaluating a fusion algorithm of remotely sensed images, i.e. the fusion of a high spatial resolution panchromatic image with a multi-spectral image (also known as pansharpening) using the dual-tree complex wavelet transform (DT-CWT), an effective approach for conducting an analytic and oversampled wavelet transform to reduce aliasing, and in turn reduce shift dependence of the wavelet transform. The proposed scheme includes the definition of a model to establish how information will be extracted from the PAN band and how that information will be injected into the MS bands with low spatial resolution. The approach was applied to Spot 5 images where there are bands falling outside PAN's spectrum. We propose an optional step in the quality evaluation protocol, which is to study the quality of the merger by regions, where each region represents a specific feature of the image. The results show that DT-CWT based approach offers good spatial quality while retaining the spectral information of original images, case SPOT 5. The additional step facilitates the identification of the most affected regions by the fusion process.

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Comparison of Pansharpening Algorithms: Outcome of the 2006 GRS-S Data-Fusion Contest

Abstract: Abstract-In

Cited by 761 publications

References 26 publications

Pansharpening by Convolutional Neural Networks

Pansharpening by Convolutional Neural Networks

Pansharpening using total variation regularization

Quality assessment by region in spot images fused by means dual-tree complex wavelet transform

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