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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 Insight is in the details: 8-band multispectral imagery / I. Gilbert in Geoinformatics, vol 13 n° 7 (01/10/2010)
[article]
Titre : Insight is in the details: 8-band multispectral imagery Type de document : Article/Communication Auteurs : I. Gilbert, Auteur Année de publication : 2010 Article en page(s) : pp 18 - 23 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s)
[Termes IGN] capteur multibande
[Termes IGN] image à très haute résolution
[Termes IGN] image multibande
[Termes IGN] image panchromatique
[Termes IGN] image WorldviewRésumé : (Auteur) The detailed information provided by high-resolution 8-band multispectral imagery from Digitalglobe improves the segmentation and classification of land and aquatic features beyond any other space-based remote sensing platform. Numéro de notice : A2010-457 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30650
in Geoinformatics > vol 13 n° 7 (01/10/2010) . - pp 18 - 23[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 262-2010071 SL Revue Centre de documentation Revues en salle Disponible Uncertainty analysis for the classification of multispectral satellite images using SVMs and SOMs / F. Giacco in IEEE Transactions on geoscience and remote sensing, vol 48 n° 10 (October 2010)PermalinkAutomatic detection of residential building using LIDAR data and multispectral imagery / M. Awrangjeb in ISPRS Journal of photogrammetry and remote sensing, vol 65 n° 5 (September - October 2010)PermalinkOpen source water quality analysis / A. Lo Tauro in GEO: Geoconnexion international, vol 9 n° 8 (september 2010)PermalinkMinimum 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)PermalinkPan-sharpening and geometric correction WorldView-2 satellite / Penggen Cheng in Geoinformatics, vol 13 n° 4 (01/06/2010)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)PermalinkThe future of aerial archaeology in Europe / W.S. Hanson in Photo interprétation, European journal of applied remote sensing, vol 46 n° 1 (mars 2010)PermalinkPermalinkApplication of satellite image processing techniques for Talakadu a unique archaeological landscape in India / M.B. Rajani in Photo interprétation, European journal of applied remote sensing, vol 45 n° 4 (novembre 2009)Permalink