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Pansharpening: context-based generalized Laplacian pyramids by robust regression / Gemine Vivone in IEEE Transactions on geoscience and remote sensing, vol 58 n° 9 (September 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 9 (September 2020) . - pp 6152 - 6167 Titre : Pansharpening: context-based generalized Laplacian pyramids by robust regression Type de document : Article/Communication Auteurs : Gemine Vivone, Auteur ; Stefano Marano, Auteur ; Jocelyn Chanussot, Auteur Année de publication : 2020 Article en page(s) : pp 6152 - 6167 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse multirésolution [Termes descripteurs IGN] fonction de transfert de modulation [Termes descripteurs IGN] fusion d'images [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] image panchromatique [Termes descripteurs IGN] lissage de données [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] pansharpening (fusion d'images) [Termes descripteurs IGN] régression [Termes descripteurs IGN] transformation en ondelettes Résumé : (auteur) Pansharpening refers to the combination of panchromatic (PAN) and multispectral (MS) images, designed to obtain a fused product retaining the fine spatial resolution of the former and the high spectral content of the latter. One of the most popular and successful approaches to pansharpening is the method known as context-based generalized Laplacian pyramid, which requires as a key ingredient for the estimation of the so-called injection coefficients. In this article, we propose the adoption of robust techniques for the estimation of the injection coefficients and detection strategies to select the clusters for which robust regression is needed, providing a suitable balancing between fusion performance and computational burden. Experimental results conducted on five real data sets acquired by the sensors QuickBird, WorldView-3, and WorldView-4, show the superiority of the proposed method with respect to current state-of-the-art pansharpening techniques. Numéro de notice : A2020-528 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2974806 date de publication en ligne : 04/03/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2974806 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95706 [article]

Remote sensing for target object detection and identification / Gemine Vivone (2020)  

Public Titre : Remote sensing for target object detection and identification Type de document : Monographie Auteurs : Gemine Vivone, Editeur scientifique ; Paolo Addesso, Editeur scientifique ; Amanda Ziemann, Editeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2020 Importance : 336 p. Format : 17 x 25 cm ISBN/ISSN/EAN : 978-3-03928-333-0 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] détection d'objet [Termes descripteurs IGN] détection de cible [Termes descripteurs IGN] image captée par drone [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] surveillance de l'urbanisation [Termes descripteurs IGN] surveillance écologique Résumé : (éditeur) Target object detection and identification are among the primary uses for a remote sensing system. This is crucial in several fields, including environmental and urban monitoring, hazard and disaster management, and defense and military. In recent years, these analyses have used the tremendous amount of data acquired by sensors mounted on satellite, airborne, and unmanned aerial vehicle (UAV) platforms. This book promotes papers exploiting different remote sensing data for target object detection and identification, such as synthetic aperture radar (SAR) imaging and multispectral and hyperspectral imaging. Several cutting-edge contributions, which provide examples of how to select of a technology or another depending on the specific application, will be detailed. Note de contenu : Editorial 1- Pixel tracking to estimate rivers water flow elevation using Cosmo-SkyMed synthetic aperture radar data 2- Flood distance algorithms and fault hidden danger recognition for transmission line towers based on SAR Images 3- Geospatial object detection on high resolution remote sensing imagery based on double multi-scale feature pyramid network 4- A novel multi-model decision fusion network for object detection in remote sensing images 5- Local region proposing for frame-based vehicle detection in satellite videos 6- Efficient object detection framework and hardware architecture for remote sensing images 7- Unsupervised saliency model with color Markov chain for oil tank detection 8- Affine-function transformation-based object matching for vehicle detection from unmanned aerial vehicle imagery 9- Hyperspectral anomaly detection via dictionary construction-based low-rank representation and adaptive weighting 10- Infrared small target detection based on non-convex optimization with Lp-Norm constraint 11- Infrared small target detection based on partial sum of the tensor nuclear norm 12- Infrared small-faint target detection using non-i.i.d. mixture of Gaussians and flux density 13- Mask sparse representation based on semantic features for thermal infrared target tracking 14- Infrared optical observability of an earth entry orbital test vehicle using ground-based remote sensors Numéro de notice : 25885 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Monographie DOI : 10.3390/books978-3-03928-333-0 En ligne : https://doi.org/10.3390/books978-3-03928-333-0 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95781

Intersensor statistical matching for pansharpening : theoretical issues and practical solutions / Luciano Alparone in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 8 (August 2017) . - pp 4682 - 4695 Titre : Intersensor statistical matching for pansharpening : theoretical issues and practical solutions Type de document : Article/Communication Auteurs : Luciano Alparone, Auteur ; Andrea Garzelli, Auteur ; Gemine Vivone, Auteur Année de publication : 2017 Article en page(s) : pp 4682 - 4695 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes descripteurs IGN] appariement d'histogramme [Termes descripteurs IGN] appariement d'images [Termes descripteurs IGN] image Ikonos [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] image multicapteur [Termes descripteurs IGN] image panchromatique [Termes descripteurs IGN] image Worldview [Termes descripteurs IGN] pansharpening (fusion d'images) [Termes descripteurs IGN] résolution multiple Résumé : (Auteur) In this paper, the authors investigate the statistical matching of the panchromatic (Pan) image to the multispectral (MS) bands, also known as the histogram matching, for the two main classes of pansharpening methods, i.e., those based on component substitution (CS) or spectral methods and those based on multiresolution analysis (MRA) or spatial methods. Also, hybrid methods combining CS with MRA, like the widespread additive wavelet luminance proportional (AWLP), are investigated. It is shown that all spectral, spatial, and hybrid methods must perform a dynamics matching of the enhancing Pan to the individual MS bands for MRA or a combination of them (the component that shall be substituted) for CS. For hybrid methods, the problem is more complex and both types of histogram matching may be suitable. Such an intersensor balance may be either explicit or implicitly performed by the detail-injection model, e.g., the popular projective and multiplicative injection models. An experimental setup exploiting IKONOS and WorldView-2 data sets demonstrates that a correct histogram matching is the key to attain extra performance from established methods. As a first result of this paper, the AWLP method has been revisited and its performance significantly improved by simply performing the histogram matching of Pan to the individual MS bands, rather than to the intensity component, thereby losing the original proportionality feature. Numéro de notice : A2017-502 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2697943 En ligne : http://dx.doi.org/10.1109/TGRS.2017.2697943 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86447 [article]

A critical comparison among pansharpening algorithms / Gemine Vivone in IEEE Transactions on geoscience and remote sensing, vol 53 n° 5 (mai 2015)

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 53 n° 5 (mai 2015) . - pp 2565 - 2586 Titre : A critical comparison among pansharpening algorithms Type de document : Article/Communication Auteurs : Gemine Vivone, Auteur ; Luciano Alparone, Auteur ; Jocelyn Chanussot, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 2565 - 2586 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] algorithme de fusion [Termes descripteurs IGN] analyse comparative [Termes descripteurs IGN] analyse multibande [Termes descripteurs IGN] analyse multirésolution [Termes descripteurs IGN] état de l'art [Termes descripteurs IGN] image à haute résolution [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] image panchromatique [Termes descripteurs IGN] image satellite [Termes descripteurs IGN] Matlab [Termes descripteurs IGN] pansharpening (fusion d'images) [Termes descripteurs IGN] qualité des données Résumé : (Auteur) Pansharpening aims at fusing a multispectral and a panchromatic image, featuring the result of the processing with the spectral resolution of the former and the spatial resolution of the latter. In the last decades, many algorithms addressing this task have been presented in the literature. However, the lack of universally recognized evaluation criteria, available image data sets for benchmarking, and standardized implementations of the algorithms makes a thorough evaluation and comparison of the different pansharpening techniques difficult to achieve. In this paper, the authors attempt to fill this gap by providing a critical description and extensive comparisons of some of the main state-of-the-art pansharpening methods. In greater details, several pansharpening algorithms belonging to the component substitution or multiresolution analysis families are considered. Such techniques are evaluated through the two main protocols for the assessment of pansharpening results, i.e., based on the full- and reduced-resolution validations. Five data sets acquired by different satellites allow for a detailed comparison of the algorithms, characterization of their performances with respect to the different instruments, and consistency of the two validation procedures. In addition, the implementation of all the pansharpening techniques considered in this paper and the framework used for running the simulations, comprising the two validation procedures and the main assessment indexes, are collected in a MATLAB toolbox that is made available to the community. Numéro de notice : A2015-523 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77534 [article]

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A class of cloud detection algorithms based on a MAP-MRF approach in space and time / Gemine Vivone in IEEE Transactions on geoscience and remote sensing, vol 52 n° 8 Tome 2 (August 2014)

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 52 n° 8 Tome 2 (August 2014) . - pp 5100 - 5115 Titre : A class of cloud detection algorithms based on a MAP-MRF approach in space and time Type de document : Article/Communication Auteurs : Gemine Vivone, Auteur ; Paolo Adesso, Auteur ; Maurizio Longo, Auteur ; et al., Auteur Année de publication : 2014 Article en page(s) : pp 5100 - 5115 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] champ aléatoire de Markov [Termes descripteurs IGN] classification [Termes descripteurs IGN] corrélation croisée maximale [Termes descripteurs IGN] densité de probabilité [Termes descripteurs IGN] détection des nuages Résumé : (Auteur) A recurrent concern in cloud detection approaches is the high misclassification rate for pixels close to cloud edges. We tackle this problem by introducing a novel penalty term within the classical maximum a posteriori probability-Markov random field (MAP-MRF) approach. To improve the classification rate, such term, for which we suggest two different functional forms, accounts for the predictable motion of cloud volumes across images. Two mass tracking techniques are proposed. The first one is an effective and efficient implementation of the probability hypothesis density (PHD) filter, which is based on Gaussian mixtures (GMs) and relies on finite set statistics (FISST). The second one is a region matching procedure based on a maximum cross-correlation (MCC) that is characterized by low computational load. Through extensive tests on simulated images and real data, acquired by the SEVIRI sensor, both methods show a clear performance gain in comparison with classical spatial MRF-based algorithms. Numéro de notice : A2014-435 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=73972 [article]

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