Increasing the spatial resolution of gridded data by fusion with other data sets

Wald, Lucien; Ranchin, Thierry; Lefèvre, Mireille; Albuisson, Michel; Remund, Jan

doi:10.1109/icif.2003.177444

Cited by 5 publications

(3 citation statements)

References 9 publications

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“…To evaluate the performance of all methods, there four image quality metrics are employed, which are root‐mean‐square error (RMSE), structural similarity (SSIM) [64], relative dimensionless global error in synthesis (ERGAS) [65], and spectral angle mapping (SAM) [66]. RMSE, ERGAS, and SAM with smaller value imply better fidelity, while SSIM with large value suggests higher performance.…”

Section: Resultsmentioning

confidence: 99%

RGB‐guided hyperspectral image super‐resolution with deep progressive learning

Zhang

Huang

et al. 2023

CAAI Trans on Intel Tech

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Due to hardware limitations, existing hyperspectral (HS) camera often suffer from low spatial/temporal resolution. Recently, it has been prevalent to super‐resolve a low resolution (LR) HS image into a high resolution (HR) HS image with a HR RGB (or multispectral) image guidance. Previous approaches for this guided super‐resolution task often model the intrinsic characteristic of the desired HR HS image using hand‐crafted priors. Recently, researchers pay more attention to deep learning methods with direct supervised or unsupervised learning, which exploit deep prior only from training dataset or testing data. In this article, an efficient convolutional neural network‐based method is presented to progressively super‐resolve HS image with RGB image guidance. Specifically, a progressive HS image super‐resolution network is proposed, which progressively super‐resolve the LR HS image with pixel shuffled HR RGB image guidance. Then, the super‐resolution network is progressively trained with supervised pre‐training and unsupervised adaption, where supervised pre‐training learns the general prior on training data and unsupervised adaptation generalises the general prior to specific prior for variant testing scenes. The proposed method can effectively exploit prior from training dataset and testing HS and RGB images with spectral‐spatial constraint. It has a good generalisation capability, especially for blind HS image super‐resolution. Comprehensive experimental results show that the proposed deep progressive learning method outperforms the existing state‐of‐the‐art methods for HS image super‐resolution in non‐blind and blind cases.

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Section: Resultsmentioning

confidence: 99%

RGB‐guided hyperspectral image super‐resolution with deep progressive learning

Zhang

Huang

et al. 2023

CAAI Trans on Intel Tech

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“…ERGAS [ 52 ] can more comprehensively reflect the quality of the fusion result. The smaller the value of ERGAS, the better the result, with an ideal value of 0.…”

Section: Methodsmentioning

confidence: 99%

Pansharpening Model of Transferable Remote Sensing Images Based on Feature Fusion and Attention Modules

Liu

Deng

Dou

et al. 2023

Sensors

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The purpose of the panchromatic sharpening of remote sensing images is to generate high-resolution multispectral images through software technology without increasing economic expenditure. The specific method is to fuse the spatial information of a high-resolution panchromatic image and the spectral information of a low-resolution multispectral image. This work proposes a novel model for generating high-quality multispectral images. This model uses the feature domain of the convolution neural network to fuse multispectral and panchromatic images so that the fused images can generate new features so that the final fused features can restore clear images. Because of the unique feature extraction ability of convolution neural networks, we use the core idea of convolution neural networks to extract global features. To extract the complementary features of the input image at a deeper level, we first designed two subnetworks with the same structure but different weights, and then used single-channel attention to optimize the fused features to improve the final fusion performance. We select the public data set widely used in this field to verify the validity of the model. The experimental results on the GaoFen-2 and SPOT6 data sets show that this method has a better effect in fusing multi-spectral and panchromatic images. Compared with the classical and the latest methods in this field, our model fusion obtained panchromatic sharpened images from both quantitative and qualitative analysis has achieved better results. In addition, to verify the transferability and generalization of our proposed model, we directly apply it to multispectral image sharpening, such as hyperspectral image sharpening. Experiments and tests have been carried out on Pavia Center and Botswana public hyperspectral data sets, and the results show that the model has also achieved good performance in hyperspectral data sets.

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“…In recent years, regional fusion methods for model--derived solar radiation data and ground-based observations have started to surface (Journée et al, 2012;Skamarock et al, 2008;Wald et al, 2003). Very recently, other authors have proposed an advanced regional fusion method (Ruiz--Arias et al, 2015).…”

Section: Regional Site Adaptationmentioning

confidence: 99%

Integration of ground measurements with model-derived data

Polo¹,

Wilbert²,

Ruiz‐Arias³

et al. 2015

Self Cite

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Bankable data for solar energy projects needs to ensure as much as possible the accuracy and general quality of solar radiation data to be used in the solar resource assessment studies for any site of interest in a project development. The term "site adaptation" is being used in the framework of solar energy projects to refer to the improvement that can be achieved in satellite-derived (or more generally model-derived) solar irradiance when short-term local ground measurements are used to correct systematic errors and bias of the original dataset. This document presents a review of different techniques for correcting long-term satellite-derived solar radiation data by using short-term ground measurements. The collaborative work has been done within the framework of Task 46 "Solar Resource Assessment and Forecasting" of the International Energy Agency's Solar Heating and Cooling Programme. Different approaches whose use depends on the origin and characteristics of the uncertainties of the modelled data are presented. Recommendations to the use of ground measurements and the results of several approaches to improve satellite-derived data are shown through this report highlighting the importance of using site adaptation and the different degree of improvement that can be achieved depending on the climatological characteristics of the site.

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Increasing the spatial resolution of gridded data by fusion with other data sets

Cited by 5 publications

References 9 publications

RGB‐guided hyperspectral image super‐resolution with deep progressive learning

RGB‐guided hyperspectral image super‐resolution with deep progressive learning

Pansharpening Model of Transferable Remote Sensing Images Based on Feature Fusion and Attention Modules

Integration of ground measurements with model-derived data

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