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Generalized composite kernel framework for hyperspectral image classification / J. Li in IEEE Transactions on geoscience and remote sensing, vol 51 n° 9 (September 2013)
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Titre : Generalized composite kernel framework for hyperspectral image classification Type de document : Article/Communication Auteurs : J. Li, Auteur ; Prashanth Reddy Marpu, Auteur ; Antonio Plaza, Auteur ; José M. Bioucas-Dias, Auteur ; et al., Auteur Année de publication : 2013 Conférence : MicroRad 2012, 12th specialist meeting on microwave radiometry and remote sensing applications 05/03/2012 09/03/2012 Rome Italie Proceedings IEEE Article en page(s) : pp 4816 - 4829 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification dirigée
[Termes IGN] données localisées
[Termes IGN] image AVIRIS
[Termes IGN] image hyperspectrale
[Termes IGN] image ROSIS
[Termes IGN] méthode fondée sur le noyau
[Termes IGN] régression logistique
[Termes IGN] séparateur à vaste margeRésumé : (Auteur) This paper presents a new framework for the development of generalized composite kernel machines for hyperspectral image classification. We construct a new family of generalized composite kernels which exhibit great flexibility when combining the spectral and the spatial information contained in the hyperspectral data, without any weight parameters. The classifier adopted in this work is the multinomial logistic regression, and the spatial information is modeled from extended multiattribute profiles. In order to illustrate the good performance of the proposed framework, support vector machines are also used for evaluation purposes. Our experimental results with real hyperspectral images collected by the National Aeronautics and Space Administration Jet Propulsion Laboratory's Airborne Visible/Infrared Imaging Spectrometer and the Reflective Optics Spectrographic Imaging System indicate that the proposed framework leads to state-of-the-art classification performance in complex analysis scenarios. Numéro de notice : A2013-536 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2230268 En ligne : https://doi.org/10.1109/TGRS.2012.2230268 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32673
in IEEE Transactions on geoscience and remote sensing > vol 51 n° 9 (September 2013) . - pp 4816 - 4829[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2013091 RAB Revue Centre de documentation En réserve L003 Disponible Hyperspectral image noise reduction based on rank-1 tensor decomposition / Xian Guoa in ISPRS Journal of photogrammetry and remote sensing, vol 83 (September 2013)
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Titre : Hyperspectral image noise reduction based on rank-1 tensor decomposition Type de document : Article/Communication Auteurs : Xian Guoa, Auteur ; Xian Guo, Auteur ; Xin Huang, Auteur ; Liangpei Zhanga, Auteur ; Lefei Zhang, Auteur Année de publication : 2013 Article en page(s) : pp 50 - 63 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] calcul tensoriel
[Termes IGN] décomposition spatiale
[Termes IGN] filtrage du bruit
[Termes IGN] image hyperspectrale
[Termes IGN] tenseur
[Termes IGN] valeur propre
[Termes IGN] voxelRésumé : (Auteur) In this study, a novel noise reduction algorithm for hyperspectral imagery (HSI) is proposed based on high-order rank-1 tensor decomposition. The hyperspectral data cube is considered as a three-order tensor that is able to jointly treat both the spatial and spectral modes. Subsequently, the rank-1 tensor decomposition (R1TD) algorithm is applied to the tensor data, which takes into account both the spatial and spectral information of the hyperspectral data cube. A noise-reduced hyperspectral image is then obtained by combining the rank-1 tensors using an eigenvalue intensity sorting and reconstruction technique. Compared with the existing noise reduction methods such as the conventional channel-by-channel approaches and the recently developed multidimensional filter, the spatial–spectral adaptive total variation filter, experiments with both synthetic noisy data and real HSI data reveal that the proposed R1TD algorithm significantly improves the HSI data quality in terms of both visual inspection and image quality indices. The subsequent image classification results further validate the effectiveness of the proposed HSI noise reduction algorithm. Numéro de notice : A2013-488 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.06.001 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.06.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32626
in ISPRS Journal of photogrammetry and remote sensing > vol 83 (September 2013) . - pp 50 - 63[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2013091 RAB Revue Centre de documentation En réserve L003 Disponible A new method for automatic large scale map updating using mobile mapping imagery / Jianliang Ou in Photogrammetric record, vol 28 n° 143 (September - November 2013)
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Titre : A new method for automatic large scale map updating using mobile mapping imagery Type de document : Article/Communication Auteurs : Jianliang Ou, Auteur ; Gang Qiao, Auteur ; Feng Bao, Auteur ; et al., Auteur Année de publication : 2013 Article en page(s) : pp 240 - 260 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] acquisition de données
[Termes IGN] appariement de lignes
[Termes IGN] grande échelle
[Termes IGN] image terrestre
[Termes IGN] mise à jour cartographique
[Termes IGN] point de fuite
[Termes IGN] Ransac (algorithme)
[Termes IGN] séquence d'images
[Termes IGN] stéréoscopie
[Termes IGN] système de numérisation mobile
[Termes IGN] transformation de HoughRésumé : (Auteur) Land-based mobile mapping system (MMS) technology provides an innovative way to update geospatial information, characterised by rapid data collection and direct georeferencing. This paper presents a new method for automatically updating large scale maps using sequential MMS imagery. A major component of the technique is the extraction of vertical lines from such imagery through an improved Hough transform and vanishing point classification. These lines are stereomatched using an epipolar constraint and random sample consensus (RANSAC) strategy, with object space positions obtained by photogrammetric intersection. Buildings are recognised based on these vertical lines in conjunction with basic polygon templates, allowing maps to be updated. Numéro de notice : A2013-500 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1111/phor.12018 Date de publication en ligne : 26/04/2013 En ligne : https://doi.org/10.1111/phor.12018 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32638
in Photogrammetric record > vol 28 n° 143 (September - November 2013) . - pp 240 - 260[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 106-2013031 RAB Revue Centre de documentation En réserve L003 Disponible Advances in Geographic Object-Based Image Analysis with ontologies: A review of main contributions and limitations from a remote sensing perspective / Damien Arvor in ISPRS Journal of photogrammetry and remote sensing, vol 82 (August 2013)
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Titre : Advances in Geographic Object-Based Image Analysis with ontologies: A review of main contributions and limitations from a remote sensing perspective Type de document : Article/Communication Auteurs : Damien Arvor, Auteur ; Laurent Durieux, Auteur ; Samuel Andrés, Auteur ; Marie-Angélique Laporte, Auteur Année de publication : 2013 Article en page(s) : pp 125 - 137 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse d'image orientée objet
[Termes IGN] interprétation automatique
[Termes IGN] objet géographique
[Termes IGN] occupation du sol
[Termes IGN] ontologie
[Termes IGN] photo-interprétation assistée par ordinateurRésumé : (Auteur) Geographic Object-Based Image Analysis (GEOBIA) represents the most innovative new trend for processing remote sensing images that has appeared during the last decade. However, its application is mainly based on expert knowledge, which consequently highlights important scientific issues with respect to the robustness of the methods applied in GEOBIA. In this paper, we argue that GEOBIA would benefit from another technical enhancement involving knowledge representation techniques such as ontologies. Although the role of ontologies in Geographical Information Sciences (GISciences) is not a new topic, few works have discussed how ontologies, considered from the perspective of a remote sensing specialist, can contribute to advancing remote sensing science. We summarize the main applications of ontologies in GEOBIA, especially for data discovery, automatic image interpretation, data interoperability, workflow management and data publication. Finally, we discuss the major issues related to the construction of ontologies suitable for remote sensing applications and outline long-term future advances that can be expected for the remote sensing community. Numéro de notice : A2013-414 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.05.003 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.05.003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32552
in ISPRS Journal of photogrammetry and remote sensing > vol 82 (August 2013) . - pp 125 - 137[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2013081 RAB Revue Centre de documentation En réserve L003 Disponible Non-linear partial least square regression increases the estimation accuracy of grass nitrogen and phosphorus using in situ hyperspectral and environmental data / Abel Ramoelo in ISPRS Journal of photogrammetry and remote sensing, vol 82 (August 2013)
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Titre : Non-linear partial least square regression increases the estimation accuracy of grass nitrogen and phosphorus using in situ hyperspectral and environmental data Type de document : Article/Communication Auteurs : Abel Ramoelo, Auteur ; Andrew K. Skidmore, Auteur ; Moses Azong Cho, Auteur ; et al., Auteur Année de publication : 2013 Article en page(s) : pp 27 - 40 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] Afrique du sud (état)
[Termes IGN] azote
[Termes IGN] données environnementales
[Termes IGN] herbe
[Termes IGN] image hyperspectrale
[Termes IGN] indice de végétation
[Termes IGN] parc naturel national
[Termes IGN] parcours
[Termes IGN] phosphore
[Termes IGN] régression non linéaire
[Termes IGN] savaneRésumé : (Auteur) Grass nitrogen (N) and phosphorus (P) concentrations are direct indicators of rangeland quality and provide imperative information for sound management of wildlife and livestock. It is challenging to estimate grass N and P concentrations using remote sensing in the savanna ecosystems. These areas are diverse and heterogeneous in soil and plant moisture, soil nutrients, grazing pressures, and human activities. The objective of the study is to test the performance of non-linear partial least squares regression (PLSR) for predicting grass N and P concentrations through integrating in situ hyperspectral remote sensing and environmental variables (climatic, edaphic and topographic). Data were collected along a land use gradient in the greater Kruger National Park region. The data consisted of: (i) in situ-measured hyperspectral spectra, (ii) environmental variables and measured grass N and P concentrations. The hyperspectral variables included published starch, N and protein spectral absorption features, red edge position, narrow-band indices such as simple ratio (SR) and normalized difference vegetation index (NDVI). The results of the non-linear PLSR were compared to those of conventional linear PLSR. Using non-linear PLSR, integrating in situ hyperspectral and environmental variables yielded the highest grass N and P estimation accuracy (R2 = 0.81, root mean square error (RMSE) = 0.08, and R2 = 0.80, RMSE = 0.03, respectively) as compared to using remote sensing variables only, and conventional PLSR. The study demonstrates the importance of an integrated modeling approach for estimating grass quality which is a crucial effort towards effective management and planning of protected and communal savanna ecosystems. Numéro de notice : A2013-409 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.04.012 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.04.012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32547
in ISPRS Journal of photogrammetry and remote sensing > vol 82 (August 2013) . - pp 27 - 40[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2013081 RAB Revue Centre de documentation En réserve L003 Disponible Partial iterates for symmetrizing non-parametric color correction / Bruno Vallet in ISPRS Journal of photogrammetry and remote sensing, vol 82 (August 2013)
PermalinkUsing hyperspectral reflectance data to assess biocontrol damage of giant salvinia / James H. Everitt in Geocarto international, vol 28 n° 5-6 (August - October 2013)
PermalinkBuilding a forward-mode three-dimensional reflectance model for topographic normalization of High-Resolution (1–5 m) imagery: validation phase in a forested environment / Stéphane Couturier in IEEE Transactions on geoscience and remote sensing, vol 51 n° 7 Tome 1 (July 2013)
PermalinkComparaison entre les méthodes J-SEG et MeanShift : application sur des données THRS / Rabia Sarah Cheriguene in Revue Française de Photogrammétrie et de Télédétection, n° 203 (Juillet 2013)
PermalinkDeblurring and sparse unmixing for hyperspectral images / Xi-Le Zhao in IEEE Transactions on geoscience and remote sensing, vol 51 n° 7 Tome 1 (July 2013)
PermalinkDevelopment of a 3-D urbanization index using digital terrain models for surface urban heat island effects / Chih-Da Wu in ISPRS Journal of photogrammetry and remote sensing, vol 81 (July 2013)
PermalinkEffects of national forest inventory plot location error on forest carbon stock estimation using k-nearest neighbor algorithm / Jaehoon Jung in ISPRS Journal of photogrammetry and remote sensing, vol 81 (July 2013)
PermalinkGraph-regularized low-rank representation for destriping of hyperspectral images / Xiaoqiang Lu in IEEE Transactions on geoscience and remote sensing, vol 51 n° 7 Tome 1 (July 2013)
PermalinkIndependent two-step thresholding of binary images in inter-annual land cover change/no-change identification / Priyakant Sinha in ISPRS Journal of photogrammetry and remote sensing, vol 81 (July 2013)
PermalinkMissing-area reconstruction in multispectral images under a compressive sensing perspective / Luca Lorenzi in IEEE Transactions on geoscience and remote sensing, vol 51 n° 7 Tome 1 (July 2013)
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