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Termes IGN > imagerie > image numérique > image optique > image multibande
image multibandeSynonyme(s)Image xs ;Image multispectrale donnees multispectralesVoir aussi |
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Using high-resolution, multispectral imagery to assess the effect of soil properties on vegetation reflectance at an abandoned feedlot / Prosper Gbolo in Geocarto international, vol 30 n° 7 - 8 (August - September 2015)
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Titre : Using high-resolution, multispectral imagery to assess the effect of soil properties on vegetation reflectance at an abandoned feedlot Type de document : Article/Communication Auteurs : Prosper Gbolo, Auteur ; Phil J. Gerla, Auteur ; Gregory S. Vanderberg, Auteur Année de publication : 2015 Article en page(s) : pp 793 - 809 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] élevage
[Termes IGN] image multibande
[Termes IGN] nutriment végétal
[Termes IGN] qualité du sol
[Termes IGN] réflectance végétale
[Termes IGN] télédétection spatialeRésumé : (Auteur) Remotely sensed multispectral imagery, soils and graminoid samples from an abandoned cattle feedlot and adjacent wetlands were used to characterize plant vigour and soil nutrient distribution and evaluate the relationship between soil properties and vegetation reflectance. The feedlot lies on a sandy beach ridge, which likely mitigates the mobility of soil phosphorus. Soil phosphorus remains concentrated directly beneath the feedlot pens, where vegetation indices are low. In contrast, nitrate is transported through preferential pathways into the wetlands, where vegetation indices and plant vigour are high. Although spectral vegetation indices did not show any significant relationship with plant tissue nutrient concentration, the indices showed statistically significant relationships to some soil properties. Results of this study indicate that the abundance of nutrients in the soil does not necessarily enhance plant growth. This can limit the extent that remotely sensed vegetation indices can be used to evaluate soil nutrients concentrations. Numéro de notice : A2015-502 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2014.985746 Date de publication en ligne : 15/06/2015 En ligne : http://www.tandfonline.com/doi/full/10.1080/10106049.2014.985746 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77419
in Geocarto international > vol 30 n° 7 - 8 (August - September 2015) . - pp 793 - 809[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2015041 RAB Revue Centre de documentation En réserve L003 Disponible Hyperspectral and multispectral image fusion based on a sparse representation / Qi Wei in IEEE Transactions on geoscience and remote sensing, vol 53 n° 7 (July 2015)
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Titre : Hyperspectral and multispectral image fusion based on a sparse representation Type de document : Article/Communication Auteurs : Qi Wei, Auteur ; José Bioucas-Dias, Auteur ; Nicolas Dobigeon, Auteur ; Jean-Yves Tourneret, Auteur Année de publication : 2015 Article en page(s) : pp 3658 - 3668 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] décomposition d'image
[Termes IGN] fusion d'images
[Termes IGN] image hyperspectrale
[Termes IGN] image multibande
[Termes IGN] optimisation (mathématiques)
[Termes IGN] problème inverse
[Termes IGN] représentation parcimonieuseRésumé : (Résumé) This paper presents a variational-based approach for fusing hyperspectral and multispectral images. The fusion problem is formulated as an inverse problem whose solution is the target image assumed to live in a lower dimensional subspace. A sparse regularization term is carefully designed, relying on a decomposition of the scene on a set of dictionaries. The dictionary atoms and the supports of the corresponding active coding coefficients are learned from the observed images. Then, conditionally on these dictionaries and supports, the fusion problem is solved via alternating optimization with respect to the target image (using the alternating direction method of multipliers) and the coding coefficients. Simulation results demonstrate the efficiency of the proposed algorithm when compared with state-of-the-art fusion methods. Numéro de notice : A2015-315 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2381272 En ligne : https://doi.org/10.1109/TGRS.2014.2381272 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=76564
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 7 (July 2015) . - pp 3658 - 3668[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2015071 RAB Revue Centre de documentation En réserve L003 Disponible Local binary patterns and extreme learning machine for hyperspectral imagery classification / Wei Li in IEEE Transactions on geoscience and remote sensing, vol 53 n° 7 (July 2015)
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Titre : Local binary patterns and extreme learning machine for hyperspectral imagery classification Type de document : Article/Communication Auteurs : Wei Li, Auteur ; Chen Chen, Auteur ; Hongjun Su, Auteur ; Qian Du, Auteur Année de publication : 2015 Article en page(s) : pp 3681 - 3693 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] classification spectrale
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] filtre de Gabor
[Termes IGN] image hyperspectrale
[Termes IGN] texture d'imageRésumé : (Auteur) It is of great interest in exploiting texture information for classification of hyperspectral imagery (HSI) at high spatial resolution. In this paper, a classification paradigm to exploit rich texture information of HSI is proposed. The proposed framework employs local binary patterns (LBPs) to extract local image features, such as edges, corners, and spots. Two levels of fusion (i.e., feature-level fusion and decision-level fusion) are applied to the extracted LBP features along with global Gabor features and original spectral features, where feature-level fusion involves concatenation of multiple features before the pattern classification process while decision-level fusion performs on probability outputs of each individual classification pipeline and soft-decision fusion rule is adopted to merge results from the classifier ensemble. Moreover, the efficient extreme learning machine with a very simple structure is employed as the classifier. Experimental results on several HSI data sets demonstrate that the proposed framework is superior to some traditional alternatives. Numéro de notice : A2015-316 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2381602 En ligne : https://doi.org/10.1109/TGRS.2014.2381602 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=76566
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 7 (July 2015) . - pp 3681 - 3693[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2015071 RAB Revue Centre de documentation En réserve L003 Disponible Multiclass feature learning for hyperspectral image classification: Sparse and hierarchical solutions / Devis Tuia in ISPRS Journal of photogrammetry and remote sensing, vol 105 (July 2015)
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Titre : Multiclass feature learning for hyperspectral image classification: Sparse and hierarchical solutions Type de document : Article/Communication Auteurs : Devis Tuia, Auteur ; Rémi Flamary, Auteur ; Nicolas Courty, Auteur Année de publication : 2015 Article en page(s) : pp 272 - 285 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image hyperspectrale
[Termes IGN] régression logistiqueRésumé : (auteur) In this paper, we tackle the question of discovering an effective set of spatial filters to solve hyperspectral classification problems. Instead of fixing a priori the filters and their parameters using expert knowledge, we let the model find them within random draws in the (possibly infinite) space of possible filters. We define an active set feature learner that includes in the model only features that improve the classifier. To this end, we consider a fast and linear classifier, multiclass logistic classification, and show that with a good representation (the filters discovered), such a simple classifier can reach at least state of the art performances. We apply the proposed active set learner in four hyperspectral image classification problems, including agricultural and urban classification at different resolutions, as well as multimodal data. We also propose a hierarchical setting, which allows to generate more complex banks of features that can better describe the nonlinearities present in the data. Numéro de notice : A2015-705 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2015.01.006 En ligne : https://doi.org/10.1016/j.isprsjprs.2015.01.006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78341
in ISPRS Journal of photogrammetry and remote sensing > vol 105 (July 2015) . - pp 272 - 285[article]A novel negative abundance‐oriented hyperspectral unmixing algorithm / Rubén Marrero in IEEE Transactions on geoscience and remote sensing, vol 53 n° 7 (July 2015)
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Titre : A novel negative abundance‐oriented hyperspectral unmixing algorithm Type de document : Article/Communication Auteurs : Rubén Marrero, Auteur ; Sebastian Lopez, Auteur ; Gustavo Callicó, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 3772 - 3790 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse de mélange spectral d’extrémités multiples
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] image hyperspectrale
[Termes IGN] propagation d'erreur
[Termes IGN] variabilitéRésumé : (Auteur) Spectral unmixing is a popular technique for analyzing remotely sensed hyperspectral data sets with subpixel precision. Over the last few years, many algorithms have been developed for each of the main processing steps involved in spectral unmixing (SU) under the LMM assumption: 1) estimation of the number of endmembers; 2) identification of the spectral signatures of the endmembers; and 3) estimation of the abundance of endmembers in the scene. Although this general processing chain has proven to be effective for unmixing certain types of hyperspectral images, it also has some drawbacks. The first one comes from the fact that the output of each stage is the input of the following one, which favors the propagation of errors within the unmixing chain. A second problem is the huge variability of the results obtained when estimating the number of endmembers of a hyperspectral scene with different state-of-the-art algorithms, which influences the rest of the process. A third issue is the computational complexity of the whole process. To address the aforementioned issues, this paper develops a novel negative abundance-oriented SU algorithm that covers, for the first time in the literature, the main steps involved in traditional hyperspectral unmixing chains. The proposed algorithm can also be easily adapted to a scenario in which the number of endmembers is known in advance and two additional variations of the algorithm are provided to deal with high-noise scenarios and to significantly reduce its execution time, respectively. Our experimental results, conducted using both synthetic and real hyperspectral scenes, indicate that the presented method is highly competitive (in terms of both unmixing accuracy and computational performance) with regard to other SU techniques with similar requirements, while providing a fully self-contained unmixing chain without the need for any input parameters. Numéro de notice : A2015-317 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2383440 En ligne : https://doi.org/10.1109/TGRS.2014.2383440 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=76567
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 7 (July 2015) . - pp 3772 - 3790[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2015071 RAB Revue Centre de documentation En réserve L003 Disponible Spectral–spatial kernel regularized for hyperspectral image denoising full text / Yuan Yuan in IEEE Transactions on geoscience and remote sensing, vol 53 n° 7 (July 2015)
PermalinkExtension of the linear chromodynamics model for spectral change detection in the presence of residual spatial misregistration / Karmon Vongsy in IEEE Transactions on geoscience and remote sensing, vol 53 n° 6 (June 2015)
PermalinkFast forward feature selection of hyperspectral images for classification with gaussian mixture models / Mathieu Fauvel in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol 8 n° 6 (June 2015)
PermalinkA fully-automated approach to land cover mapping with airborne LiDAR and high resolution multispectral imagery in a forested suburban landscape / Jason R. Parent in ISPRS Journal of photogrammetry and remote sensing, vol 104 (June 2015)
PermalinkMTF-adjusted pansharpening approach based on coupled multiresolution decompositions / Abdelaziz Kallel in IEEE Transactions on geoscience and remote sensing, vol 53 n° 6 (June 2015)
PermalinkObject-based building change detection from a single multispectral image and pre-existing geospatial information / Georgia Doxani in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 6 (June 2015)
PermalinkSubstance dependence constrained sparse NMF for hyperspectral unmixing / Yuan Yuan in IEEE Transactions on geoscience and remote sensing, vol 53 n° 6 (June 2015)
PermalinkComplementarity of discriminative classifiers and spectral unmixing techniques for the interpretation of hyperspectral images / Jun Li in IEEE Transactions on geoscience and remote sensing, vol 53 n° 5 (mai 2015)
PermalinkA critical comparison among pansharpening algorithms / Gemine Vivone in IEEE Transactions on geoscience and remote sensing, vol 53 n° 5 (mai 2015)
PermalinkHYCA: A new technique for hyperspectral compressive sensing / G. Martin in IEEE Transactions on geoscience and remote sensing, vol 53 n° 5 (mai 2015)
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