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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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Hyperspectral image denoising employing a spectral-spatial adaptive total variation model / Q. Yuan in IEEE Transactions on geoscience and remote sensing, vol 50 n° 10 Tome 1 (October 2012)
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[article]
Titre : Hyperspectral image denoising employing a spectral-spatial adaptive total variation model Type de document : Article/Communication Auteurs : Q. Yuan, Auteur ; L. Zhang, Auteur ; H. Shen, Auteur Année de publication : 2012 Article en page(s) : pp 3660 - 3677 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] filtrage du bruit
[Termes IGN] image hyperspectrale
[Termes IGN] itération
[Termes IGN] variationRésumé : (Auteur) The amount of noise included in a hyperspectral image limits its application and has a negative impact on hyperspectral image classification, unmixing, target detection, and so on. In hyperspectral images, because the noise intensity in different bands is different, to better suppress the noise in the high-noise-intensity bands and preserve the detailed information in the low-noise-intensity bands, the denoising strength should be adaptively adjusted with the noise intensity in the different bands. Meanwhile, in the same band, there exist different spatial property regions, such as homogeneous regions and edge or texture regions; to better reduce the noise in the homogeneous regions and preserve the edge and texture information, the denoising strength applied to pixels in different spatial property regions should also be different. Therefore, in this paper, we propose a hyperspectral image denoising algorithm employing a spectral-spatial adaptive total variation (TV) model, in which the spectral noise differences and spatial information differences are both considered in the process of noise reduction. To reduce the computational load in the denoising process, the split Bregman iteration algorithm is employed to optimize the spectral-spatial hyperspectral TV model and accelerate the speed of hyperspectral image denoising. A number of experiments illustrate that the proposed approach can satisfactorily realize the spectral-spatial adaptive mechanism in the denoising process, and superior denoising results are produced. Numéro de notice : A2012-523 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2185054 Date de publication en ligne : 07/03/2012 En ligne : https://doi.org/10.1109/TGRS.2012.2185054 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31969
in IEEE Transactions on geoscience and remote sensing > vol 50 n° 10 Tome 1 (October 2012) . - pp 3660 - 3677[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2012101A RAB Revue Centre de documentation En réserve L003 Disponible Semisupervised classification of remote sensing images with active queries / Jordi Munoz-Mari in IEEE Transactions on geoscience and remote sensing, vol 50 n° 10 Tome 1 (October 2012)
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Titre : Semisupervised classification of remote sensing images with active queries Type de document : Article/Communication Auteurs : Jordi Munoz-Mari, Auteur ; Devis Tuia, Auteur ; G. Camps-Valls, Auteur Année de publication : 2012 Article en page(s) : pp 3751 - 3763 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse de groupement
[Termes IGN] apprentissage (cognition)
[Termes IGN] carte d'occupation du sol
[Termes IGN] carte de confiance
[Termes IGN] classification semi-dirigée
[Termes IGN] image multibande
[Termes IGN] occupation du sol
[Termes IGN] requête spatiale
[Termes IGN] télédétection spatialeRésumé : (Auteur) We propose a semiautomatic procedure to generate land cover maps from remote sensing images. The proposed algorithm starts by building a hierarchical clustering tree, and exploits the most coherent pixels with respect to the available class information. For a given amount of labeled pixels, the algorithm returns both classification and confidence maps. Since the quality of the map depends of the number and informativeness of the labeled pixels, active learning methods are used to select the most informative samples to increase confidence in class membership. Experiments on four different data sets, accounting for hyperspectral and multispectral images at different spatial resolutions, confirm the effectiveness of the proposed approach, and how active learning techniques reduce the uncertainty of the classification maps. Specifically, more accurate results with fewer labeled samples are obtained. Inclusion of spatial information in the classifiers drastically improves the classification accuracy, leading to faster convergence curves and tighter confidence intervals. In conclusion, the presented algorithm provides efficient image classification and, at the same time, yields a confidence map that may be very useful in many Earth observation applications. Numéro de notice : A2012-524 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2185504 Date de publication en ligne : 08/03/2012 En ligne : https://doi.org/10.1109/TGRS.2012.2185504 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31970
in IEEE Transactions on geoscience and remote sensing > vol 50 n° 10 Tome 1 (October 2012) . - pp 3751 - 3763[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2012101A RAB Revue Centre de documentation En réserve L003 Disponible Information fusion in the redundant-wavelet-transform domain for noise-robust hyperspectral classification / S. Prasad in IEEE Transactions on geoscience and remote sensing, vol 50 n° 9 (October 2012)
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Titre : Information fusion in the redundant-wavelet-transform domain for noise-robust hyperspectral classification Type de document : Article/Communication Auteurs : S. Prasad, Auteur ; J. Fowler, Auteur ; L. Bruce, Auteur ; W. Li, Auteur Année de publication : 2012 Article en page(s) : pp 3474 - 3486 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification dirigée
[Termes IGN] filtrage du bruit
[Termes IGN] fusion de données
[Termes IGN] image hyperspectrale
[Termes IGN] méthode robuste
[Termes IGN] partitionnement
[Termes IGN] redondance de données
[Termes IGN] transformation en ondelettesRésumé : (Auteur) Hyperspectral imagery comprises high-dimensional reflectance vectors representing the spectral response over a wide range of wavelengths per pixel in the image. The resulting high-dimensional feature spaces often result in statistically ill-conditioned class-conditional distributions. Conventional methods for alleviating this problem typically employ dimensionality reduction such as linear discriminant analysis along with single-classifier systems, yet these methods are suboptimal and lack noise robustness. In contrast, a divide-and-conquer approach is proposed to address the high dimensionality of hyperspectral data for effective and noise-robust classification. Central to the proposed framework is a redundant wavelet transform for representing the data in a feature space amenable to noise-robust multiscale analysis as well as a multiclassifier and decision-fusion system for classification and target recognition in high-dimensional spaces under small-sample-size conditions. The proposed partitioning of this feature space assigns a collection of all coefficients across all scales at a particular spectral wavelength to a dedicated classifier. It is demonstrated that such a partitioning of the feature space for a multiclassifier system yields superior noise performance for classification tasks. Additionally, validation studies with experimental hyperspectral data show that the proposed system significantly outperforms conventional denoising and classification approaches. Numéro de notice : A2012-451 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2185053 Date de publication en ligne : 06/03/2012 En ligne : https://doi.org/10.1109/TGRS.2012.2185053 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31897
in IEEE Transactions on geoscience and remote sensing > vol 50 n° 9 (October 2012) . - pp 3474 - 3486[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2012091 RAB Revue Centre de documentation En réserve L003 Exclu du prêt The influence of subpixel measurement on digital camera calibration / Mauricio Galo in Revue Française de Photogrammétrie et de Télédétection, n° 198 - 199 (Septembre 2012)
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Titre : The influence of subpixel measurement on digital camera calibration Type de document : Article/Communication Auteurs : Mauricio Galo, Auteur ; Antonio Maria Garcia Tommaselli, Auteur ; J.K. Hasegawa, Auteur Année de publication : 2012 Article en page(s) : pp 62 - 70 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s)
[Termes IGN] chambre DTC
[Termes IGN] compensation par moindres carrés
[Termes IGN] élément d'orientation interne
[Termes IGN] étalonnage de chambre métrique
[Termes IGN] extraction automatique
[Termes IGN] image multibande
[Termes IGN] point d'intérêt
[Termes IGN] précision infrapixellaireRésumé : (Auteur) The aim of this work is to evaluate the influence of point measurements in images, with subpixel accuracy, and its contribution in the calibration of digital cameras. Also, the effect of subpixel measurements in 3D coordinates of check points in the object space will be evaluated. With this purpose, an algorithm that allows subpixel accuracy was implemented for semi-automatic determination of points of interest, based on Förstner operator. Experiments were accomplished with a block of images acquired with the multispectral camera DuncanTech MS3100-CIR. The influence of subpixel measurements in the adjustment by Least Square Method (LSM) was evaluated by the comparison of estimated standard deviation of parameters in both situations, with manual measurement (pixel accuracy) and with subpixel estimation. Additionally, the influence of subpixel measurements in the 3D reconstruction was also analyzed. Based on the obtained results, i. e., on the quantification of the standard deviation reduction in the Inner Orientation Parameters (IOP) and also in the relative error of the 3D reconstruction, it was shown that measurements with subpixel accuracy are relevant for some tasks in Photogrammetry, mainly for those in which the metric quality is of great relevance, as Camera Calibration. Numéro de notice : A2012-425 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.52638/rfpt.2012.73 Date de publication en ligne : 21/04/2014 En ligne : https://doi.org/10.52638/rfpt.2012.73 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31871
in Revue Française de Photogrammétrie et de Télédétection > n° 198 - 199 (Septembre 2012) . - pp 62 - 70[article]Applying six classifiers to airborne hyperspectral imagery for detecting giant reed / C. Yang in Geocarto international, vol 27 n° 5 (August 2012)
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Titre : Applying six classifiers to airborne hyperspectral imagery for detecting giant reed Type de document : Article/Communication Auteurs : C. Yang, Auteur ; J. Goolsby, Auteur ; James H. Everitt, Auteur ; Q. Du, Auteur Année de publication : 2012 Article en page(s) : pp 413 - 424 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] classificateur
[Termes IGN] classification barycentrique
[Termes IGN] classification par la distance de Mahalanobis
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] classification Spectral angle mapper
[Termes IGN] espèce exotique envahissante
[Termes IGN] Etats-Unis
[Termes IGN] image aérienne
[Termes IGN] image hyperspectrale
[Termes IGN] macrophyte
[Termes IGN] Mexique
[Termes IGN] Rio Grande (fleuve)Résumé : (Auteur) This study evaluated and compared six image classifiers, including minimum distance (MD), Mahalanobis distance (MAHD), maximum likelihood (ML), spectral angle mapper (SAM), mixture tuned matched filtering (MTMF) and support vector machine (SVM), for detecting and mapping giant reed (Arundo donax L.), an invasive weed that presents a severe threat to agroecosystems throughout the southern US and northern Mexico. Airborne hyperspectral imagery was collected from a giant reed-infested site along the US-Mexican portion of the Rio Grande in 2009 and 2010. The imagery was transformed with minimum noise fraction (MFN) and the six classifiers were applied to the 30-band MNF imagery for each year. Accuracy assessment showed that SVM and ML generally performed better than the other four classifiers for overall classification and for distinguishing giant reed in both years. These results indicate that airborne hyperspectral imagery in conjunction with SVM and ML classification techniques is effective for detecting giant reed. Numéro de notice : A2012-371 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2011.643321 Date de publication en ligne : 04/01/2012 En ligne : https://doi.org/10.1080/10106049.2011.643321 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31817
in Geocarto international > vol 27 n° 5 (August 2012) . - pp 413 - 424[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2012051 RAB Revue Centre de documentation En réserve L003 Disponible Classification of urban tree species using hyperspectral imagery / R. Jensen in Geocarto international, vol 27 n° 5 (August 2012)
PermalinkEvaluating classification techniques for mapping vertical geology using field-based hyperspectral sensors / R.J. Murphy in IEEE Transactions on geoscience and remote sensing, vol 50 n° 8 (August 2012)
PermalinkFusion of feature selection and optimized immune networks for hyperspectral image classification of urban landscapes / J. Im in Geocarto international, vol 27 n° 5 (August 2012)
PermalinkHyperspectral band clustering and band selection for urban land cover classification / H. Su in Geocarto international, vol 27 n° 5 (August 2012)
PermalinkLocal coregistration adjustment for anomalous change detection / J. Theiler in IEEE Transactions on geoscience and remote sensing, vol 50 n° 8 (August 2012)
PermalinkMemory-based cluster sampling for remote sensing image classification / Michele Volpi in IEEE Transactions on geoscience and remote sensing, vol 50 n° 8 (August 2012)
PermalinkDetecting and correcting motion blur from images shot with channel-dependent exposure time / Lâmân Lelégard in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol I-3 (2012)
PermalinkMonitoring water stress and fruit quality in an orange orchard under regulated deficit irrigation using narrow-band structural and physiological remote sensing indices / S. Stagakis in ISPRS Journal of photogrammetry and remote sensing, vol 71 (July 2012)
PermalinkRepresentative multiple Kernel learning for classification in hyperspectral imagery / Y. Gu in IEEE Transactions on geoscience and remote sensing, vol 50 n° 7 Tome 2 (July 2012)
PermalinkEstimating tropical forest biomass with a combination of SAR image texture and Landsat TM data: An assessment of predictions between regions / M. Cutler in ISPRS Journal of photogrammetry and remote sensing, vol 70 (June 2012)
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