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Sentinel-2 sharpening using a reduced-rank method / Magnus Orn Ulfarsson in IEEE Transactions on geoscience and remote sensing, vol 57 n° 9 (September 2019)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 57 n° 9 (September 2019) . - pp 6408 - 6420 Titre : Sentinel-2 sharpening using a reduced-rank method Type de document : Article/Communication Auteurs : Magnus Orn Ulfarsson, Auteur ; Frosti Palsson, Auteur ; Mauro Dalla Mura, Auteur ; Johannes R. Sveinsson, Auteur Année de publication : 2019 Article en page(s) : pp 6408 - 6420 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] affinage d'image [Termes IGN] ajustement de paramètres [Termes IGN] estimation bayesienne [Termes IGN] fusion de données [Termes IGN] image à haute résolution [Termes IGN] image multibande [Termes IGN] image Sentinel-MSI [Termes IGN] largeur de bande Résumé : (auteur) Recently, the Sentinel-2 (S2) satellite constellation was deployed for mapping and monitoring the Earth environment. Images acquired by the sensors mounted on the S2 platforms have three levels of spatial resolution: 10, 20, and 60 m. In many remote sensing applications, the availability of images at the highest spatial resolution (i.e., 10 m for S2) is often desirable. This can be achieved by generating a synthetic high-resolution image through data fusion. To this end, researchers have proposed techniques exploiting the spectral/spatial correlation inherent in multispectral data to sharpen the lower resolution S2 bands to 10 m. In this paper, we propose a novel method that formulates the sharpening process as a solution to an inverse problem. We develop a cyclic descent algorithm called S2Sharp and an associated tuning parameter selection algorithm based on generalized cross validation and Bayesian optimization. The tuning parameter selection method is evaluated on a simulated data set. The effectiveness of S2Sharp is assessed experimentally by comparisons to state-of-the-art methods using both simulated and real data sets. Numéro de notice : A2019-340 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2906048 Date de publication en ligne : 22/04/2019 En ligne : http://doi.org/10.1109/TGRS.2019.2906048 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93377 [article]

Quantitative quality evaluation of pansharpened imagery: consistency versus synthesis / Frosti Palsson in IEEE Transactions on geoscience and remote sensing, vol 54 n° 3 (March 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 3 (March 2016) . - pp 1247 - 1259 Titre : Quantitative quality evaluation of pansharpened imagery: consistency versus synthesis Type de document : Article/Communication Auteurs : Frosti Palsson, Auteur ; Johannes R. Sveinsson, Auteur ; Magnus Orn Ulfarsson, Auteur ; Jon Atli Benediktsson, Auteur Année de publication : 2016 Article en page(s) : pp 1247 - 1259 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] cohérence des données [Termes IGN] évaluation [Termes IGN] fusion d'images [Termes IGN] image de synthèse [Termes IGN] image Quickbird [Termes IGN] image Worldview [Termes IGN] pansharpening (fusion d'images) [Termes IGN] problème inverse [Termes IGN] qualité des données Résumé : (Auteur) Pansharpening is the process of fusing a high-resolution panchromatic image and a low-spatial-resolution multispectral image to yield a high-spatial-resolution multispectral image. This is a typical ill-posed inverse problem, and in the past two decades, many methods have been proposed to solve it. Still, there is no general consensus on the best way to quantitatively evaluate the spectral and spatial quality of the fused image. In this paper, we compare the two most widely used and accepted methods for quality evaluation. The first method is the verification of the synthesis property which states that the fused image should be as identical as possible to the multispectral image that the sensor would observe at a higher resolution. This is impossible to verify unless the observed images are spatially degraded so that the original observed multispectral image can be used as reference. The second method is to use metrics that do not use a reference, such as the quality no reference (QNR) metrics. However, there is another property, i.e., the consistency property, which states that the fused image reduced to the resolution of the original multispectral image should be as identical to the original image as possible. This has generally been considered a necessary condition that does not have to imply correct fusion. Using real WorldView-2 and QuickBird data and a total of 18 component substitution and multiresolution analysis methods, we demonstrate that the consistency property can indeed be used to give reliable assessment of the relative performance of pansharpening methods and is superior to using the QNR metrics. Numéro de notice : A2016-126 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2476513 En ligne : http://dx.doi.org/10.1109/TGRS.2015.2476513 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80007 [article]

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