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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]

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]

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]

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

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 52 n° 11 tome 1 (November 2014) . - pp 6793 - 6806 Titre : Hyperspectral unmixing with [lq] regularization Type de document : Article/Communication Auteurs : Jakob Sigurdsson, Auteur ; Magnus Orn Ulfarsson, Auteur ; Johannes R. Sveinsson, Auteur Année de publication : 2014 Article en page(s) : pp 6793 - 6806 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] traitement du signal Résumé : (Auteur) Hyperspectral unmixing is an important technique for analyzing remote sensing images. In this paper, we consider and examine the ℓq, 0 ≤ q ≤ 1 penalty on the abundances for promoting sparse unmixing of hyperspectral data. We also apply a first-order roughness penalty to promote piecewise smooth end-members. A novel iterative algorithm for simultaneously estimating the end-members and the abundances is developed and tested both on simulated and two real hyperspectral data sets. We present an extensive simulation study where we vary both the SNR and the sparsity of the simulated data and identify the model parameters that minimize the reconstruction errors and the spectral angle distance. We show that choosing 0 ≤ q Numéro de notice : A2014-540 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2303155 En ligne : https://doi.org/10.1109/TGRS.2014.2303155 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=74157 [article]

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