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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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Crop yield estimation based on unsupervised linear unmixing of multidate hyperspectral imagery / B. Luo in IEEE Transactions on geoscience and remote sensing, vol 51 n° 1 Tome 1 (January 2013)
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[article]
Titre : Crop yield estimation based on unsupervised linear unmixing of multidate hyperspectral imagery Type de document : Article/Communication Auteurs : B. Luo, Auteur ; C. Yang, Auteur ; Jocelyn Chanussot, Auteur ; et al., Auteur Année de publication : 2013 Article en page(s) : pp 162 - 173 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] classification non dirigée
[Termes IGN] image hyperspectrale
[Termes IGN] image multitemporelle
[Termes IGN] rendement agricole
[Termes IGN] sorgho (céréale)Résumé : (Auteur) Hyperspectral imagery, which contains hundreds of spectral bands, has the potential to better describe the biological and chemical attributes on the plants than multispectral imagery and has been evaluated in this paper for the purpose of crop yield estimation. The spectrum of each pixel in a hyperspectral image is considered as a linear combinations of the spectra of the vegetation and the bare soil. Recently developed linear unmixing approaches are evaluated in this paper, which automatically extracts the spectra of the vegetation and bare soil from the images. The vegetation abundances are then computed based on the extracted spectra. In order to reduce the influences of this uncertainty and obtain a robust estimation results, the vegetation abundances extracted on two different dates on the same fields are then combined. The experiments are carried on the multidate hyperspectral images taken from two grain sorghum fields. The results show that the correlation coefficients between the vegetation abundances obtained by unsupervised linear unmixing approaches are as good as the results obtained by supervised methods, where the spectra of the vegetation and bare soil are measured in the laboratory. In addition, the combination of vegetation abundances extracted on different dates can improve the correlations (from 0.6 to 0.7). Numéro de notice : A2013-012 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2198826 Date de publication en ligne : 19/06/2012 En ligne : https://doi.org/10.1109/TGRS.2012.2198826 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32150
in IEEE Transactions on geoscience and remote sensing > vol 51 n° 1 Tome 1 (January 2013) . - pp 162 - 173[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2013011A RAB Revue Centre de documentation En réserve L003 Disponible
Titre : Fusion de données lidar et multispectrales : Etude des techniques de segmentation et de classification de données LiDAR, d’images multispectrales et de leur fusion, Proposition d’une nouvelle technique de traitement de la fusion des données et analyse des résultats Type de document : Mémoire Auteurs : Ophélie Sinagra, Auteur Editeur : Strasbourg : Institut National des Sciences Appliquées INSA Strasbourg Année de publication : 2013 Importance : 113 p. Format : 21 x 30 cm Note générale : Bibliographie
Mémoire de soutenance de Diplôme d'Ingénieur INSA, Spécialité TopographieLangues : Français (fre) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] classification dirigée
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] fusion de données
[Termes IGN] image multibande
[Termes IGN] image Quickbird
[Termes IGN] indice de végétation
[Termes IGN] matrice de confusion
[Termes IGN] occupation du sol
[Termes IGN] semis de points
[Termes IGN] StrasbourgIndex. décimale : INSAS Mémoires d'ingénieur de l'INSA Strasbourg - Topographie, ex ENSAIS Résumé : (Auteur) Cette étude a pour but de fusionner des données LiDAR et multispectrales afin de procéder à une classification en trois catégories en utilisant un algorithme supervisé. Un nuage de points LiDAR et une image satellite QuickBird comprenant les bandes Rouge, Vert, Bleue et Proche-Infrarouge acquis au-dessus de la ville de Strasbourg, France, ont été traités afin d’effectuer la fusion et d’estimer la précision de la méthode de classification proposée. Tout d’abord, l’image multispectrale a permis de calculer trois images représentant l’indice de végétation normalisé (NDVI), l’indice de végétation ajusté pour le sol (SAVI) et l’indice de contrôle environnemental global (GEMI). Puis, le nuage de points a lui permis de calculer la hauteur des éléments situés au-dessus du sol et l’information tridimensionnelle a été convertie en raster. Ces quatre rasters ont étés assemblés afin d’obtenir des rasters de deux, trois ou quatre couches afin d’effectuer différents tests. L’algorithme Support Vector Machine (SVM), permettant une classification supervisée, a été utilisé afin de classer ces rasters en trois classes : végétation, bâtiments et voirie. La matrice de confusions de la classification indique que la précision est améliorée lorsque les données LiDAR sont intégrées au calcul. Note de contenu : Introduction
1- Etat de l'art
2- Changement dans le sujet
3- Traitement de la fusion des données
4- Résultats finaux
5- Analyse
6- Discussion
ConclusionNuméro de notice : 11802 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Mémoire ingénieur INSAS Organisme de stage : University of New South Wales Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=49749 Documents numériques
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11802_mem_insas_2013__sinagra.pdfAdobe Acrobat PDFMapping the distribution of ferric iron minerals on a vertical mine face using derivative analysis of hyperspectral imagery (430–970 nm) / R. Murphy in ISPRS Journal of photogrammetry and remote sensing, vol 75 (January 2013)
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Titre : Mapping the distribution of ferric iron minerals on a vertical mine face using derivative analysis of hyperspectral imagery (430–970 nm) Type de document : Article/Communication Auteurs : R. Murphy, Auteur ; S. Monteiro, Auteur Année de publication : 2013 Article en page(s) : pp 29 - 39 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Advanced Visible & Near Infrared Radiometer
[Termes IGN] analyse d'image numérique
[Termes IGN] Australie occidentale (Australie)
[Termes IGN] dérivée
[Termes IGN] image hyperspectrale
[Termes IGN] mine de fer
[Termes IGN] photographie infrarouge couleur
[Termes IGN] rayonnement proche infrarouge
[Termes IGN] réflectance spectraleRésumé : (Auteur) Hyperspectral imagery is used to map the distribution of iron and separate iron ore from shale (a waste product) on a vertical mine face in an open-pit mine in the Pilbara, Western Australia. Vertical mine faces have complex surface geometries which cause large spatial variations in the amount of incident and reflected light. Methods used to analyse imagery must minimise these effects whilst preserving any spectral variations between rock types and minerals. Derivative analysis of spectra to the 1st-, 2nd- and 4th-order is used to do this. To quantify the relative amounts and distribution of iron, the derivative spectrum is integrated across the visible and near infrared spectral range (430–970 nm) and over those wavelength regions containing individual peaks and troughs associated with specific iron absorption features. As a test of this methodology, results from laboratory spectra acquired from representative rock samples were compared with total amounts of iron minerals from X-ray diffraction (XRD) analysis. Relationships between derivatives integrated over the visible near-infrared range and total amounts (% weight) of iron minerals were strongest for the 4th- and 2nd-derivative (R2 = 0.77 and 0.74, respectively) and weakest for the 1st-derivative (R2 = 0.56). Integrated values of individual peaks and troughs showed moderate to strong relationships in 2nd- (R2 = 0.68–0.78) and 4th-derivative (R2 = 0.49–0.78) spectra. The weakest relationships were found for peaks or troughs towards longer wavelengths. The same derivative methods were then applied to imagery to quantify relative amounts of iron minerals on a mine face. Before analyses, predictions were made about the relative abundances of iron in the different geological zones on the mine face, as mapped from field surveys. Integration of the whole spectral curve (430–970 nm) from the 2nd- and 4th-derivative gave results which were entirely consistent with predictions. Conversely, integration of the 1st-derivative gave results that did not fit with predictions nor distinguish between zones with very large and small amounts of iron oxide. Classified maps of ore and shale were created using a simple level-slice of the 1st-derivative reflectance at 702, 765 and 809 nm. Pixels classified as shale showed a similar distribution to kaolinite (an indicator of shales in the region), as mapped by the depth of the diagnostic kaolinite absorption feature at 2196 nm. Standard statistical measures of classification performance (accuracy, precision, recall and the Kappa coefficient of agreement) indicated that nearly all of the pixels were classified correctly using 1st-derivative reflectance at 765 and 809 nm. These results indicate that data from the VNIR (430–970 nm) can be used to quantify, without a priori knowledge, the total amount of iron minerals and to distinguish ore from shale on vertical mine faces. Numéro de notice : A2013-030 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2012.09.014 En ligne : https://doi.org/10.1016/j.isprsjprs.2012.09.014 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32168
in ISPRS Journal of photogrammetry and remote sensing > vol 75 (January 2013) . - pp 29 - 39[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2013011 RAB Revue Centre de documentation En réserve L003 Disponible Material reflectance retrieval in urban tree shadows with physics-based empirical atmospheric correction / Karine R.M. Adeline (2013)
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Titre : Material reflectance retrieval in urban tree shadows with physics-based empirical atmospheric correction Type de document : Article/Communication Auteurs : Karine R.M. Adeline, Auteur ; Nicolas Paparoditis , Auteur ; Jean-Philippe Gastellu-Etchegorry, Auteur
Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2013 Conférence : JURSE 2013, Joint Urban Remote Sensing Event 21/04/2013 23/04/2013 Sao Paulo Brésil Proceedings IEEE Importance : pp 279 - 283 Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] arbre urbain
[Termes IGN] correction atmosphérique
[Termes IGN] houppier
[Termes IGN] image hyperspectrale
[Termes IGN] milieu urbain
[Termes IGN] modélisation 3D
[Termes IGN] ombre
[Termes IGN] rayonnement incident
[Termes IGN] rayonnement solaire
[Termes IGN] réflectance végétaleRésumé : (auteur) Material reflectance retrieval from high spatial resolution acquisitions over urban areas requires an accurate modeling of the signal accounting for the 3D environment. Especially in tree shadows, the solar radiation incident to the ground contributing to the estimation of the reflectance has many origins linked to the plant structure and its composition. In this paper, the 3D atmospheric correction code, ICARE, limited to opaque structures like buildings, is improved thanks to an empirical correction factor taking into account the porosity of a tree crown. The validation of this method is assessed through a dataset combining a hyperspectral image and a 3D model of the scene. Numéro de notice : C2013-006 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Communication DOI : 10.1109/JURSE.2013.6550719 Date de publication en ligne : 01/07/2013 En ligne : http://dx.doi.org/10.1109/JURSE.2013.6550719 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80181 Predicting surface fuel models and fuel metrics using Lidar and CIR imagery in a dense, mountainous forest / Marek Jakubowksi in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 1 (January 2013)
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Titre : Predicting surface fuel models and fuel metrics using Lidar and CIR imagery in a dense, mountainous forest Type de document : Article/Communication Auteurs : Marek Jakubowksi, Auteur ; Quinhua Guo, Auteur ; Brandon Collins, Auteur ; Scott Stephens, Auteur ; Maggi Kelly, Auteur Année de publication : 2013 Article en page(s) : pp 37 - 49 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] biomasse (combustible)
[Termes IGN] Californie (Etats-Unis)
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] forêt
[Termes IGN] image multibande
[Termes IGN] image optique
[Termes IGN] interpolation inversement proportionnelle à la distance
[Termes IGN] lutte contre l'incendie
[Termes IGN] montagne
[Termes IGN] PinophytaRésumé : (Auteur) We compared the ability of several classification and regression algorithms to predict forest stand structure metrics and standard surface fuel models. Our study area spans a dense, topographically complex Sierra Nevada mixed-conifer forest. We used clustering, regression trees, and support vector machine algorithms to analyze high density (average 9 pulses/m2), discrete return, small-footprint lidar data, along with multispectral imagery. Stand structure metric predictions generally decreased with increased canopy penetration. For example, from the top of canopy, we predicted canopy height (r2 ! 0.87), canopy cover (r2 ! 0.83), basal area (r2 ! 0.82), shrub cover (r2 ! 0.62), shrub height (r2 ! 0.59), combined fuel loads (r2 ! 0.48), and fuel bed depth (r2 ! 0.35). While the general fuel types were predicted accurately, specific surface fuel model predictions were poor (76 percent and "50 percent correct classification, respectively) using all algorithms. These fuel components are critical inputs for wildfire behavior modeling, which ultimately support forest management decisions. This comprehensive examination of the relative utility of lidar and optical imagery will be useful for forest science and management. Numéro de notice : A2013-004 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.14358/PERS.79.1.37 En ligne : http://kellylab.berkeley.edu/storage/papers/2013-Jakubowski-etal-PERS.pdf Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32142
in Photogrammetric Engineering & Remote Sensing, PERS > vol 79 n° 1 (January 2013) . - pp 37 - 49[article]Semisupervised learning of hyperspectral data with unknown land-cover classes / G. Jun in IEEE Transactions on geoscience and remote sensing, vol 51 n° 1 Tome 1 (January 2013)
PermalinkSemisupervised local discriminant analysis for feature extraction in hyperspectral images / W. Liao in IEEE Transactions on geoscience and remote sensing, vol 51 n° 1 Tome 1 (January 2013)
PermalinkTree species discrimination in tropical forests using airborne imaging spectroscopy / Jean-Baptiste Féret in IEEE Transactions on geoscience and remote sensing, vol 51 n° 1 Tome 1 (January 2013)
PermalinkVery high resolution urban land cover extraction using airborne hyperspectral images / Arnaud Le Bris (April 2013)
PermalinkEdge-guided multiscale segmentation of satellite multispectral imagery / J. Chen in IEEE Transactions on geoscience and remote sensing, vol 50 n° 11 Tome 1 (November 2012)
PermalinkHYPXIM, an innovative spectroimager for science, security and defence requirements / M.J. Lefevre-Fonollosa in Revue Française de Photogrammétrie et de Télédétection, n° 200 (Novembre 2012)
PermalinkTotal variation spatial regularization for sparse hyperspectral unmixing / M. Iordache in IEEE Transactions on geoscience and remote sensing, vol 50 n° 11 Tome 1 (November 2012)
PermalinkTriangular factorization-based simplex algorithms for hyperspectral unmixing / W. Xia in IEEE Transactions on geoscience and remote sensing, vol 50 n° 11 Tome 1 (November 2012)
PermalinkA vector sift detector for interest point detection in hyperspectral imagery / L. Dorado-Munoz in IEEE Transactions on geoscience and remote sensing, vol 50 n° 11 Tome 1 (November 2012)
PermalinkDimensionality reduction of hyperspectral data using spectral fractal feature / K. Mukherjee in Geocarto international, vol 27 n° 6 (October 2012)
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