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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)
[article]
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]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2013091 RAB Revue Centre de documentation En réserve L003 Disponible Assessing the veracity of methods for extracting place semantics from Flickr tags / William A Mackaness in Transactions in GIS, vol 17 n° 4 (August 2013)
[article]
Titre : Assessing the veracity of methods for extracting place semantics from Flickr tags Type de document : Article/Communication Auteurs : William A Mackaness, Auteur ; Omair Chaudhry, Auteur Année de publication : 2013 Article en page(s) : pp 544 - 562 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées
[Termes IGN] base de données d'images
[Termes IGN] données spatiotemporelles
[Termes IGN] exploration de données
[Termes IGN] grand public
[Termes IGN] image Flickr
[Termes IGN] inférence
[Termes IGN] information sémantique
[Termes IGN] milieu urbain
[Termes IGN] régression logistique
[Termes IGN] segmentation sémantiqueRésumé : (Auteur) The volume and potential value of user generated content (UGC) is ever growing. Multiply sourced, its value is greatly increased by the inclusion of metadata that adequately and accurately describes that content – particularly if such data are to be integrated with more formal data sets. Typically, digital photography is tagged with location and attribute information that variously describe the location, events or objects in the image. Often inconsistent and incomplete, these attributes reflect concepts at a range of geographic scales. From a spatial data integration perspective, the information relating to “place” is of primary interest. The challenge therefore is in selecting the most appropriate tags that best describe the geography of the image. This article presents a methodology based on an information retrieval technique that separates out “place related tags” from the remainder of the tags. Different scales of geography are identified by varying the size of the sampling area within which the imagery falls. This is applied in the context of urban environments, using Flickr imagery. Empirical analysis is then used to assess the correctness of the chosen tags (i.e. whether the tag correctly describes the geographic region in which the image was taken). Logistic regression and Bayesian inference are used to attach a probability value to each place tag. The high correlation values achieved indicate that this methodology can be used to automatically select place tags for any urban region and thus hierarchically structure UGC in order that it can be semantically integrated with other data sources. Numéro de notice : A2013-471 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12043 Date de publication en ligne : 28/05/2013 En ligne : https://doi.org/10.1111/tgis.12043 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32609
in Transactions in GIS > vol 17 n° 4 (August 2013) . - pp 544 - 562[article]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)
[article]
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]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 081-2013081 RAB Revue Centre de documentation En réserve L003 Disponible Retrieval of tropical forest biomass information from ALOS PALSAR data / Mahmudur Rahman in Geocarto international, vol 28 n° 5-6 (August - October 2013)
[article]
Titre : Retrieval of tropical forest biomass information from ALOS PALSAR data Type de document : Article/Communication Auteurs : Mahmudur Rahman, Auteur ; Josaphat Tetuko Sri Sumantyo, Auteur Année de publication : 2013 Article en page(s) : pp 382 - 403 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] bande L
[Termes IGN] Bangladesh
[Termes IGN] biomasse
[Termes IGN] forêt tropicale
[Termes IGN] image ALOS-PALSAR
[Termes IGN] image radar moirée
[Termes IGN] régression
[Termes IGN] rétrodiffusionRésumé : (Auteur) Advanced Land Observing Satellite Phased Array L-band Synthetic Aperture Radar (ALOS PALSAR) data from different observation modes were analysed to determine (1) which observation mode most accurately retrieves tropical forest biomass information and (2) whether different modes, when considered together, yield improved results in comparison to identical data-sets analysed independently. We performed regression analysis to estimate above-ground forest biomass using PALSAR backscatter data for natural and planted forests in south-eastern Bangladesh. The coefficient of determination (r 2) was lower or equal to 0.499 (n = 70) when PALSAR data from different observation modes were separately considered, but increased sharply when one class (rubber) is dropped and average backscatter of fine beam single (FBS) and polarimetric (PLR) modes are used in the analysis. The results presented in this article are useful for both regional and global forest biomass inventories and fixing acquisition modes for planned L-band SAR missions. Numéro de notice : A2013-547 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2012.710652 Date de publication en ligne : 04/09/2012 En ligne : https://doi.org/10.1080/10106049.2012.710652 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32683
in Geocarto international > vol 28 n° 5-6 (August - October 2013) . - pp 382 - 403[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 059-2013031 RAB Revue Centre de documentation En réserve L003 Disponible Missing-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)
[article]
Titre : Missing-area reconstruction in multispectral images under a compressive sensing perspective Type de document : Article/Communication Auteurs : Luca Lorenzi, Auteur ; Farid Melgani, Auteur ; Grégoire Mercier, Auteur Année de publication : 2013 Article en page(s) : pp 3998 - 4008 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification par algorithme génétique
[Termes IGN] équation linéaire
[Termes IGN] image Formosat/COSMIC
[Termes IGN] image SPOT 5
[Termes IGN] nébulosité
[Termes IGN] nuage
[Termes IGN] régressionRésumé : (Auteur) The intent of this paper is to propose new methods for the reconstruction of areas obscured by clouds. They are based on compressive sensing (CS) theory, which allows finding sparse signal representations in underdetermined linear equation systems. In particular, two common CS solutions are adopted for our reconstruction problem: the basis pursuit and the orthogonal matching pursuit methods. A novel alternative CS solution is also proposed through a formulation within a multiobjective genetic optimization scheme. To illustrate the performances of the proposed methods, a thorough experimental analysis on FORMOsa SATellite-2 and Satellite Pour l'Observation de la Terre-5 multispectral images is reported and discussed. It includes a detailed simulation study that aims at assessing the accuracy of the methods in different qualitative and quantitative cloud-contamination conditions. Compared with state-of-the-art techniques for cloud removal, the proposed methods show a clear superiority, which makes them a promising tool in cleaning images in the presence of clouds. Numéro de notice : A2013-372 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2227329 En ligne : https://doi.org/10.1109/TGRS.2012.2227329 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32510
in IEEE Transactions on geoscience and remote sensing > vol 51 n° 7 Tome 1 (July 2013) . - pp 3998 - 4008[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2013071A RAB Revue Centre de documentation En réserve L003 Disponible Semisupervised self-learning for hyperspectral image classification / Immaculada Dopido in IEEE Transactions on geoscience and remote sensing, vol 51 n° 7 Tome 1 (July 2013)PermalinkImproved topographic mapping through high-resolution SAR interferometry with atmospheric effect removal / Mingsheng Liao in ISPRS Journal of photogrammetry and remote sensing, vol 80 (June 2013)PermalinkCommercial tree species discrimination using airborne AISA Eagle hyperspectral imagery and partial least squares discriminant analysis (PLS-DA) in KwaZulu–Natal, South Africa / Kabir Yunus Peerbhay in ISPRS Journal of photogrammetry and remote sensing, vol 79 (May 2013)PermalinkSensitivity of spectral reflectance values to different burn and vegetation ratios: A multi-scale approach applied in a fire affected area / Magdalini Pleniou in ISPRS Journal of photogrammetry and remote sensing, vol 79 (May 2013)PermalinkGSICS inter-calibration of infrared channels of geostationary imagers using Metop-IASI / Tim J. Hewison in IEEE Transactions on geoscience and remote sensing, vol 51 n° 3 Tome 1 (March 2013)PermalinkOn weighted total least-squares with linear and quadratic constraints / Vahid Mahboub in Journal of geodesy, vol 87 n° 3 (March 2013)PermalinkAirborne GNSS-R wind retrievals using delay–Doppler maps / N. Rodriguez-Alvarez in IEEE Transactions on geoscience and remote sensing, vol 51 n° 1 Tome 2 (January 2013)PermalinkAssessment of regression kriging for spatial interpolation: comparisons of seven GIS interpolation methods / Qingmin Meng in Cartography and Geographic Information Science, vol 40 n° 1 (January 2013)PermalinkPermalinkPermalink