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Local manifold learning-based k-Nearest-Neighbor for hyperspectral image classification / Li Ma in IEEE Transactions on geoscience and remote sensing, vol 48 n° 11 (November 2010)
[article]
Titre : Local manifold learning-based k-Nearest-Neighbor for hyperspectral image classification Type de document : Article/Communication Auteurs : Li Ma, Auteur ; Jing Tian, Auteur Année de publication : 2010 Article en page(s) : pp 1099 - 4109 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] classification barycentrique
[Termes IGN] image AVIRIS
[Termes IGN] image EO1-Hyperion
[Termes IGN] image hyperspectraleRésumé : (Auteur) Approaches to combine local manifold learning (LML) and the k -nearest-neighbor (kNN) classifier are investigated for hyperspectral image classification. Based on supervised LML (SLML) and kNN, a new SLML-weighted kNN (SLML-W kNN) classifier is proposed. This method is appealing as it does not require dimensionality reduction and only depends on the weights provided by the kernel function of the specific ML method. Performance of the proposed classifier is compared to that of unsupervised LML (ULML) and SLML for dimensionality reduction in conjunction with the kNN (ULML- kNN and SLML-k NN). Three LML methods, locally linear embedding (LLE), local tangent space alignment (LTSA), and Laplacian eigenmaps, are investigated with these classifiers. In experiments with Hyperion and AVIRIS hyperspectral data, the proposed SLML-WkNN performed better than ULML- kNN and SLML-k NN, and the highest accuracies were obtained using weights provided by supervised LTSA and LLE. Numéro de notice : A2010-479 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2010.2055876 Date de publication en ligne : 23/08/2010 En ligne : https://doi.org/10.1109/TGRS.2010.2055876 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30672
in IEEE Transactions on geoscience and remote sensing > vol 48 n° 11 (November 2010) . - pp 1099 - 4109[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2010111 RAB Revue Centre de documentation En réserve L003 Disponible Multiple Spectral–Spatial Classification Approach for Hyperspectral Data / Yuliya Tarabalka in IEEE Transactions on geoscience and remote sensing, vol 48 n° 11 (November 2010)
[article]
Titre : Multiple Spectral–Spatial Classification Approach for Hyperspectral Data Type de document : Article/Communication Auteurs : Yuliya Tarabalka, Auteur ; Jon Atli Benediktsson, Auteur ; Jocelyn Chanussot, Auteur ; James C. Tilton, Auteur Année de publication : 2010 Article en page(s) : pp 4122 - 4132 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification multibande
[Termes IGN] image aérienne
[Termes IGN] image hyperspectrale
[Termes IGN] segmentation d'imageRésumé : (Auteur) A new multiple-classifier approach for spectral-spatial classification of hyperspectral images is proposed. Several classifiers are used independently to classify an image. For every pixel, if all the classifiers have assigned this pixel to the same class, the pixel is kept as a marker, i.e., a seed of the spatial region with a corresponding class label. We propose to use spectral-spatial classifiers at the preliminary step of the marker-selection procedure, each of them combining the results of a pixelwise classification and a segmentation map. Different segmentation methods based on dissimilar principles lead to different classification results. Furthermore, a minimum spanning forest is built, where each tree is rooted on a classification-driven marker and forms a region in the spectral-spatial classification map. Experimental results are presented for two hyperspectral airborne images. The proposed method significantly improves classification accuracies when compared with previously proposed classification techniques. Numéro de notice : A2010-480 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2010.2062526 Date de publication en ligne : 13/09/2010 En ligne : https://doi.org/10.1109/TGRS.2010.2062526 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30673
in IEEE Transactions on geoscience and remote sensing > vol 48 n° 11 (November 2010) . - pp 4122 - 4132[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2010111 RAB Revue Centre de documentation En réserve L003 Disponible vol 48 n° 11 - November 2010 - Special issue on hyperspectral image and signal processing (Bulletin de IEEE Transactions on geoscience and remote sensing) / Geoscience and remote sensing society
[n° ou bulletin]
est un bulletin de IEEE Transactions on geoscience and remote sensing / IEEE Geoscience and remote sensing society (Etats-Unis) (1986 -)
Titre : vol 48 n° 11 - November 2010 - Special issue on hyperspectral image and signal processing Type de document : Périodique Auteurs : Geoscience and remote sensing society, Auteur Année de publication : 2010 Importance : 250 p. Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Télédétection
[Termes IGN] compression d'image
[Termes IGN] étalonnage
[Termes IGN] image hyperspectrale
[Termes IGN] traitement d'imageNuméro de notice : 065-201011 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Numéro de périodique En ligne : http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=5607221&punumber=36 Format de la ressource électronique : URL sommaire Permalink : https://documentation.ensg.eu/index.php?lvl=bulletin_display&id=13574 [n° ou bulletin]Contient
- Local manifold learning-based k-Nearest-Neighbor for hyperspectral image classification / Li Ma in IEEE Transactions on geoscience and remote sensing, vol 48 n° 11 (November 2010)
- Multiple Spectral–Spatial Classification Approach for Hyperspectral Data / Yuliya Tarabalka in IEEE Transactions on geoscience and remote sensing, vol 48 n° 11 (November 2010)
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Code-barres Cote Support Localisation Section Disponibilité 065-2010111 RAB Revue Centre de documentation En réserve L003 Disponible Status and future of laser scanning, synthetic aperture radar and hyperspectral remote sensing data for forest biomass assessment / B. Koch in ISPRS Journal of photogrammetry and remote sensing, vol 65 n° 6 (November - December 2010)
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Titre : Status and future of laser scanning, synthetic aperture radar and hyperspectral remote sensing data for forest biomass assessment Type de document : Article/Communication Auteurs : B. Koch, Auteur Année de publication : 2010 Article en page(s) : pp 581 - 590 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] biomasse (combustible)
[Termes IGN] cartographie thématique
[Termes IGN] données laser
[Termes IGN] données polarimétriques
[Termes IGN] forêt
[Termes IGN] image hyperspectrale
[Termes IGN] image radar moirée
[Termes IGN] polarimétrie radarRésumé : (Auteur) This is a review of the latest developments in different fields of remote sensing for forest biomass mapping. The main fields of research within the last decade have focused on the use of small footprint airborne laser scanning systems, polarimetric synthetic radar interferometry and hyperspectral data. Parallel developments in the field of digital airborne camera systems, digital photogrammetry and very high resolution multispectral data have taken place and have also proven themselves suitable for forest mapping issues. Forest mapping is a wide field and a variety of forest parameters can be mapped or modelled based on remote sensing information alone or combined with field data. The most common information required about a forest is related to its wood production and environmental aspects. In this paper, we will focus on the potential of advanced remote sensing techniques to assess forest biomass. This information is especially required by the REDD (reducing of emission from avoided deforestation and degradation) process. For this reason, new types of remote sensing data such as fullwave laser scanning data, polarimetric radar interferometry (polarimetric synthetic aperture interferometry, PolInSAR) and hyperspectral data are the focus of the research. In recent times, a few state-of-the-art articles in the field of airborne laser scanning for forest applications have been published. The current paper will provide a state-of-the-art review of remote sensing with a particular focus on biomass estimation, including new findings with fullwave airborne laser scanning, hyperspectral and polarimetric synthetic aperture radar interferometry. A synthesis of the actual findings and an outline of future developments will be presented. Numéro de notice : A2010-490 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2010.09.001 En ligne : https://doi.org/10.1016/j.isprsjprs.2010.09.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30683
in ISPRS Journal of photogrammetry and remote sensing > vol 65 n° 6 (November - December 2010) . - pp 581 - 590[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2010061 SL Revue Centre de documentation Revues en salle Disponible Open source water quality analysis / A. Lo Tauro in GEO: Geoconnexion international, vol 9 n° 8 (september 2010)
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Titre : Open source water quality analysis Type de document : Article/Communication Auteurs : A. Lo Tauro, Auteur Année de publication : 2010 Article en page(s) : pp 24 - 27 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] érosion côtière
[Termes IGN] image EO1-Hyperion
[Termes IGN] image hyperspectrale
[Termes IGN] image MIVIS
[Termes IGN] logiciel libre
[Termes IGN] positionnement par GPS
[Termes IGN] qualité des eaux
[Termes IGN] surveillance du littoral
[Termes IGN] système d'information géographiqueRésumé : (Auteur) A project to monitor, simulate and control water quality parameters has the aim of helping managers and authorities of coastal areas to select the most suitable environmental indicators, taking into account the data available. Hyperspectral analysis of water quality has the potential of enhancing the abilities of resource managers to monitor water bodies in a timely and cost-effective manner. Numéro de notice : A2010-342 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30536
in GEO: Geoconnexion international > vol 9 n° 8 (september 2010) . - pp 24 - 27[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 062-2010081 SL Revue Centre de documentation Revues en salle Disponible Minimum dispersion constrained nonnegative matrix factorization to unmix hyperspectral data / A. Huck in IEEE Transactions on geoscience and remote sensing, vol 48 n° 6 (June 2010)PermalinkSuperresolution enhancement of hyperspectral CHRIS/Proba images with a thin-plate spline nonrigid transform model / J. Chan in IEEE Transactions on geoscience and remote sensing, vol 48 n° 6 (June 2010)PermalinkUse of derivative calculations and minimum noise fraction transform for detecting and correcting the spectral curvature effect (Smile) in Hyperion Images / A. Dadon in IEEE Transactions on geoscience and remote sensing, vol 48 n° 6 (June 2010)PermalinkMerging hyperspectral and panchromatic image data: qualitative and quantitative analysis / M. Cetin in International Journal of Remote Sensing IJRS, vol 30 n° 7 (April 2009)PermalinkEvaluating AISA+ hyperspectral imagery for mapping black mangrove along the South Texas gulf coast / C. Yang in Photogrammetric Engineering & Remote Sensing, PERS, vol 75 n° 4 (April 2009)PermalinkRemote sensing of soil salinization / G. Metternicht (2009)PermalinkNeuro-fuzzy based analysis of hyperspectral imagery / F. Qiu in Photogrammetric Engineering & Remote Sensing, PERS, vol 74 n° 10 (October 2008)Permalinkvol 46 n° 10 Tome 1 - October 2008 - Special issue on the 2007 International Geoscience and Remote Sensing Symposium (IGARSS'07): sensing and understanding our planet, [actes], Barcelona, July 23-27, 2007. Part 1 of two parts (Bulletin de IEEE Transactions on geoscience and remote sensing) / A. CampsPermalinkvol 46 n° 10 Tome 2 - October 2008 - Special issue on the 2007 International Geoscience and Remote Sensing Symposium (IGARSS'07): sensing and understanding our planet, [actes], Barcelona, July 23-27, 2007. Part 2 of two parts (Bulletin de IEEE Transactions on geoscience and remote sensing) / A. CampsPermalinkPotential accuracy of image orientation of small satellites: a case study of CHRIS/Proba data / Ahmed Shaker in Photogrammetric record, vol 23 n° 123 (September - November 2008)PermalinkIntegration of Hyperion satellite data and a household social survey to caracterize the causes and consequences of reforestation patterns in the Northern Ecuadorian Amazon / S.J. Walsh in Photogrammetric Engineering & Remote Sensing, PERS, vol 74 n° 6 (June 2008)PermalinkUnsupervised Image Segmentation based on Texems for Hyperspectral data / Adolfo Martinez-Uso (2008)PermalinkDetermination of robust spectral features for identification of urban surface materials in hyperspectral remote sensing data / U. Heiden in Remote sensing of environment, vol 111 n° 4 (28/12/2007)PermalinkApplications de l'imagerie hyperspectrale à l'étude des planètes du système solaire : le cas de Mars et de Titan / S. Le Mouelic in Photo interprétation, vol 43 n° 4 (Décembre 2007)PermalinkBorder vector detection and adaptation for classification of multispectral and hyperspectral remote sensing images / N.G. Kasapoglu in IEEE Transactions on geoscience and remote sensing, vol 45 n° 12 Tome 1 (December 2007)PermalinkN-FindR method versus independent component analysis for lithological identification in hyperspectral imagery / C. Gomez in International Journal of Remote Sensing IJRS, vol 28 n°23-24 (December 2007)PermalinkA time-efficient method for anomaly detection in hyperspectral images / O. Duran in IEEE Transactions on geoscience and remote sensing, vol 45 n° 12 Tome 1 (December 2007)PermalinkCharacterizing patterns of plant distribution in a southern California salt marsh using remotely sensed topographic and hyperspectral data and local tidal fluctuations / S. Sadro in Remote sensing of environment, vol 110 n° 2 (28/09/2007)PermalinkDerniers développements en télédétection hyperspectrale / V. Carrere in Photo interprétation, vol 43 n° 3 (Septembre 2007)PermalinkFeature extraction of hyperspectral images using wavelet and matching pursuit / Pai-Hui Hsu in ISPRS Journal of photogrammetry and remote sensing, vol 62 n° 2 (June 2007)Permalink