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Fusing Sentinel-2 and Landsat 8 satellite images using a model-based method / Jakob Sigurdsson in Remote sensing, vol 14 n° 13 (July-1 2022)  

Public [article]  inRemote sensing > vol 14 n° 13 (July-1 2022) . - n° 3224 Titre : Fusing Sentinel-2 and Landsat 8 satellite images using a model-based method Type de document : Article/Communication Auteurs : Jakob Sigurdsson, Auteur ; Sveinn E. Armannsson, Auteur ; Magnus Orn Ulfarsson, Auteur ; Johannes R. Sveinsson, Auteur Année de publication : 2022 Article en page(s) : n° 3224 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] fusion d'images [Termes IGN] image Landsat-8 [Termes IGN] image Sentinel-MSI [Termes IGN] limite de résolution géométrique [Termes IGN] modèle géométrique de prise de vue Résumé : (auteur) The Copernicus Sentinel-2 (S2) constellation comprises of two satellites in a sun-synchronous orbit. The S2 sensors have three spatial resolutions: 10, 20, and 60 m. The Landsat 8 (L8) satellite has sensors that provide seasonal coverage at spatial resolutions of 15, 30, and 60 m. Many remote sensing applications require the spatial resolutions of all data to be at the highest resolution possible, i.e., 10 m for S2. To address this demand, researchers have proposed various methods that exploit the spectral and spatial correlations within multispectral data to sharpen the S2 bands to 10 m. In this study, we combined S2 and L8 data. An S2 sharpening method called Sentinel-2 Sharpening (S2Sharp) was modified to include the 30 m and 15 m spectral bands from L8 and to sharpen all bands (S2 and L8) to the highest resolution of the data, which was 10 m. The method was evaluated using both real and simulated data. Numéro de notice : A2022-573 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : https://doi.org/10.3390/rs14133224 Date de publication en ligne : 05/07/2022 En ligne : https://doi.org/10.3390/rs14133224 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101289 [article]

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]

Sparse distributed multitemporal hyperspectral unmixing / Jakob Sigurdsson in IEEE Transactions on geoscience and remote sensing, vol 55 n° 11 (November 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 11 (November 2017) . - pp 6069 - 6084 Titre : Sparse distributed multitemporal hyperspectral unmixing Type de document : Article/Communication Auteurs : Jakob Sigurdsson, Auteur ; Magnus Orn Ulfarsson, Auteur ; Johannes R. Sveinsson, Auteur ; José M. Bioucas-Dias, Auteur Année de publication : 2017 Article en page(s) : pp 6069 - 6084 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse des mélanges spectraux [Termes IGN] image hyperspectrale [Termes IGN] image multitemporelle Résumé : (Auteur) Blind hyperspectral unmixing jointly estimates spectral signatures and abundances in hyperspectral images (HSIs). Hyperspectral unmixing is a powerful tool for analyzing hyperspectral data. However, the usual huge size of HSIs may raise difficulties for classical unmixing algorithms, namely, due to limitations of the hardware used. Therefore, some researchers have considered distributed algorithms. In this paper, we develop a distributed hyperspectral unmixing algorithm that uses the alternating direction method of multipliers and ℓ1 sparse regularization. The hyperspectral unmixing problem is split into a number of smaller subproblems that are individually solved, and then the solutions are combined. A key feature of the proposed algorithm is that each subproblem does not need to have access to the whole HSI. The algorithm may also be applied to multitemporal HSIs with due adaptations accounting for variability that often appears in multitemporal images. The effectiveness of the proposed algorithm is evaluated using both simulated data and real HSIs. Numéro de notice : A2017-742 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2720539 En ligne : https://doi.org/10.1109/TGRS.2017.2720539 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88776 [article]

Hyperspectral feature extraction using total variation component analysis / Behnood Rasti in IEEE Transactions on geoscience and remote sensing, vol 54 n° 12 (December 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 12 (December 2016) . - pp 6976 - 6985 Titre : Hyperspectral feature extraction using total variation component analysis Type de document : Article/Communication Auteurs : Behnood Rasti, Auteur ; Magnus Orn Ulfarsson, Auteur ; Johannes R. Sveinsson, Auteur Année de publication : 2016 Article en page(s) : pp 6976 - 6985 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] extraction automatique [Termes IGN] image hyperspectrale [Termes IGN] précision de la classification Résumé : (Auteur) In this paper, a novel feature extraction method, called orthogonal total variation component analysis (OTVCA), is proposed for remotely sensed hyperspectral data. The features are extracted by minimizing a total variation (TV) penalized optimization problem. The TV penalty promotes piecewise smoothness of the extracted features which is useful for classification. A cyclic descent algorithm called OTVCA-CD is proposed for solving the minimization problem. In the experiments, OTVCA is applied on a rural hyperspectral image having low spatial resolution and an urban hyperspectral image having high spatial resolution. The features extracted by OTVCA show considerable improvements in terms of classification accuracy compared with features extracted by other state-of-the-art methods. Numéro de notice : A2016-922 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2593463 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2593463 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83326 [article]

Blind hyperspectral unmixing using total variation and ℓq sparse regularization / Jakob Sigurdsson in IEEE Transactions on geoscience and remote sensing, vol 54 n° 11 (November 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 11 (November 2016) . - pp 6371 - 6384 Titre : Blind hyperspectral unmixing using total variation and ℓq sparse regularization Type de document : Article/Communication Auteurs : Jakob Sigurdsson, Auteur ; Magnus Orn Ulfarsson, Auteur ; Johannes R. Sveinsson, Auteur Année de publication : 2016 Article en page(s) : pp 6371 - 6384 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse des mélanges spectraux [Termes IGN] image hyperspectrale [Termes IGN] régularisation d'image [Termes IGN] simulation d'image [Termes IGN] variable Résumé : (Auteur) Blind hyperspectral unmixing involves jointly estimating endmembers and fractional abundances in hyperspectral images. An endmember is the spectral signature of a specific material in an image, while an abundance map specifies the amount of a material seen in each pixel in an image. In this paper, a new cyclic descent algorithm for blind hyperspectral unmixing using total variation (TV) and ℓq sparse regularization is proposed. Abundance maps are both spatially smooth and sparse. Their sparsity derives from the fact that each material in the image is not represented in all pixels. The abundance maps are assumed to be piecewise smooth since adjacent pixels in natural images tend to be composed of similar material. The TV regularizer is used to encourage piecewise smooth images, and the ℓq regularizer promotes sparsity. The dyadic expansion decouples the problem, making a cyclic descent procedure possible, where one abundance map is estimated, followed by the estimation of one endmember. A novel debiasing technique is also employed to reduce the bias of the algorithm. The algorithm is evaluated using both simulated and real hyperspectral images. Numéro de notice : A2016-914 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2582824 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2582824 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83136 [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)  

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Hyperspectral unmixing with [lq] regularization / Jakob Sigurdsson in IEEE Transactions on geoscience and remote sensing, vol 52 n° 11 tome 1 (November 2014)  

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Data fusion and feature extraction in the wavelet domain / Magnus Orn Ulfarsson in International Journal of Remote Sensing IJRS, vol 24 n° 20 (October 2003)  

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