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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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Automated geometric correction of multispectral images from high resolution CCD Camera (HRCC) on-board CBERS-2 and CBERS-2B / Chabitha Devarj in ISPRS Journal of photogrammetry and remote sensing, vol 89 (March 2014)
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[article]
Titre : Automated geometric correction of multispectral images from high resolution CCD Camera (HRCC) on-board CBERS-2 and CBERS-2B Type de document : Article/Communication Auteurs : Chabitha Devarj, Auteur ; Chintan A. Shah, Auteur Année de publication : 2014 Article en page(s) : pp 13 - 24 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] chambre DTC
[Termes IGN] correction géométrique
[Termes IGN] géoréférencement direct
[Termes IGN] image à haute résolution
[Termes IGN] image CBERS
[Termes IGN] image Landsat-TM
[Termes IGN] image multibande
[Termes IGN] orthorectificationRésumé : (Auteur) China–Brazil Earth Resource Satellite (CBERS) imagery is identified as one of the potential data sources for monitoring Earth surface dynamics in the event of a Landsat data gap. Currently available multispectral images from the High Resolution CCD (Charge Coupled Device) Camera (HRCC) on-board CBERS satellites (CBERS-2 and CBERS-2B) are not precisely geo-referenced and orthorectified. The geometric accuracy of the HRCC multispectral image product is found to be within 2–11 km. The use of CBERS-HRCC multispectral images to monitor Earth surface dynamics therefore necessitates accurate geometric correction of these images. This paper presents an automated method for geo-referencing and orthorectifying the multispectral images from the HRCC imager on-board CBERS satellites. Landsat Thematic Mapper (TM) Level 1T (L1T) imagery provided by the U.S. Geological Survey (USGS) is employed as reference for geometric correction. The proposed method introduces geometric distortions in the reference image prior to registering it with the CBERS-HRCC image. The performance of the geometric correction method was quantitatively evaluated using a total of 100 images acquired over the Andes Mountains and the Amazon rainforest, two areas in South America representing vastly different landscapes. The geometrically corrected HRCC images have an average geometric accuracy of 17.04 m (CBERS-2) and 16.34 m (CBERS-2B). While the applicability of the method for attaining sub-pixel geometric accuracy is demonstrated here using selected images, it has potential for accurate geometric correction of the entire archive of CBERS-HRCC multispectral images. Numéro de notice : A2014-121 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.12.012 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.12.012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33026
in ISPRS Journal of photogrammetry and remote sensing > vol 89 (March 2014) . - pp 13 - 24[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2014031 RAB Revue Centre de documentation En réserve L003 Disponible Semi-automated registration of close-range hyperspectral scans using oriented digital camera imagery and a 3D model / Alessandra A. Sima in Photogrammetric record, vol 29 n° 145 (March - May 2014)
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Titre : Semi-automated registration of close-range hyperspectral scans using oriented digital camera imagery and a 3D model Type de document : Article/Communication Auteurs : Alessandra A. Sima, Auteur ; Simon J. Buckley, Auteur ; Tobias H. Kurz, Auteur ; Danilo Schneider, Auteur Année de publication : 2014 Article en page(s) : pp 10 - 29 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] compensation par faisceaux
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] image hyperspectrale
[Termes IGN] image panoramique
[Termes IGN] image terrestre
[Termes IGN] orientation externe
[Termes IGN] points homologues
[Termes IGN] SIFT (algorithme)
[Termes IGN] superposition d'imagesRésumé : (Auteur) Diverse applications can benefit from the integration of data acquired by a new generation of close-range imaging sensors with high-resolution three-dimensional (3D) geometric data. However, such integration requires increased automation and efficiency of image-data registration to guarantee adoption by users beyond the geomatics community. This paper presents a semi-automated method for registering terrestrial panoramic hyperspectral imagery with lidar models and conventional digital photography. The method relies on finding corresponding points between images acquired in significantly different parts of the electromagnetic spectrum, from different viewpoints, and with different spatial resolution and geometric projections. Optimisation of the scale invariant feature transform (SIFT) operator was required to ensure a sufficient number of homologous points, as well as a routine for eliminating false matches. A band selection routine maximises the number of points found while minimising the input data for SIFT. Three-dimensional object coordinates were derived in the lidar model and used as control points in a bundle block adjustment to determine the hyperspectral exterior orientation and intrinsic camera parameters. The method developed was applied to two datasets with different characteristics, and the results indicate that the proposed method is a time-saving alternative to manual approaches. Numéro de notice : A2014-153 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1111/phor.12049 Date de publication en ligne : 13/02/2014 En ligne : https://doi.org/10.1111/phor.12049 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33058
in Photogrammetric record > vol 29 n° 145 (March - May 2014) . - pp 10 - 29[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 106-2014011 RAB Revue Centre de documentation En réserve L003 Disponible UL-Isomap based nonlinear dimensionality reduction for hyperspectral imagery classification / Weiwei Sun in ISPRS Journal of photogrammetry and remote sensing, vol 89 (March 2014)
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Titre : UL-Isomap based nonlinear dimensionality reduction for hyperspectral imagery classification Type de document : Article/Communication Auteurs : Weiwei Sun, Auteur ; Avner Halevy, Auteur ; John J. Benedetto, Auteur ; Chun Liu, Auteur ; et al., Auteur Année de publication : 2014 Article en page(s) : pp 25 - 36 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] carte isoplèthe
[Termes IGN] classification barycentrique
[Termes IGN] graphe
[Termes IGN] image hyperspectrale
[Termes IGN] isoligne
[Termes IGN] point de repère
[Termes IGN] précision de la classification
[Termes IGN] réduction géométrique
[Termes IGN] valeur propreRésumé : (Auteur) The paper proposes an upgraded landmark-Isometric mapping (UL-Isomap) method to solve the two problems of landmark selection and computational complexity in dimensionality reduction using Landmark Isometric mapping (LIsomap) for hyperspectral imagery (HSI) classification. First, the vector quantization method is introduced to select proper landmarks for HSI data. The approach considers the variations in local density of pixels in the spectral space. It locates the unique landmarks representing the geometric structures of HSI data. Then, random projections are used to reduce the bands of HSI data. After that, the new method incorporates the Recursive Lanczos Bisection (RLB) algorithm to construct the fast approximate k-nearest neighbor graph. The RLB algorithm accompanied with random projections improves the speed of neighbor searching in UL-Isomap. After constructing the geodesic distance graph between landmarks and all pixels, the method uses a fast randomized low-rank approximate method to speed up the eigenvalue decomposition of the inner-product matrix in multidimensional scaling. Manifold coordinates of landmarks are then computed. Manifold coordinates of non-landmarks are computed through the pseudo inverse transformation of landmark coordinates. Five experiments on two different HSI datasets are run to test the new UL-Isomap method. Experimental results show that UL-Isomap surpasses LIsomap, both in the overall classification accuracy (OCA) and in computational speed, with a speed over 5 times faster. Moreover, the UL-Isomap method, when compared against the Isometric mapping (Isomap) method, obtains only slightly lower OCAs. Numéro de notice : A2014-122 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.12.003 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.12.003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33027
in ISPRS Journal of photogrammetry and remote sensing > vol 89 (March 2014) . - pp 25 - 36[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2014031 RAB Revue Centre de documentation En réserve L003 Disponible
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Titre : Un vaste champ d'applications Type de document : Article/Communication Auteurs : Françoise de Blomac, Auteur Année de publication : 2014 Article en page(s) : pp 09 - 11 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Télédétection
[Termes IGN] agriculture
[Termes IGN] capteur hyperspectral
[Termes IGN] couvert forestier
[Termes IGN] couvert végétal
[Termes IGN] défense nationale
[Termes IGN] désert
[Termes IGN] données localisées
[Termes IGN] fond marin
[Termes IGN] géologie
[Termes IGN] image hyperspectraleRésumé : (Auteur) Analyse des pigments dans un tableau ancien, étude des grottes ornées, examens médicaux ... l'hyperspectral, en tant que technique non invasive, n'est pas réservé à l'observation de la Terre, loin s'en faut. Mais dans notre domaine, les données hyperspectrales peuvent se mettre au service de nombreuses thématiques. Tour d'horizon des domaines d'application. Numéro de notice : A2014-150 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33055
in DécryptaGéo le mag > n° 155 (01/03/2014) . - pp 09 - 11[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 286-2014031 RAB Revue Centre de documentation En réserve L003 Disponible Estimation of higher chlorophylla concentrations using field spectral measurement and HJ-1A hyperspectral satellite data in Dianshan Lake, China / Liguo Zhou in ISPRS Journal of photogrammetry and remote sensing, vol 88 (February 2014)
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Titre : Estimation of higher chlorophylla concentrations using field spectral measurement and HJ-1A hyperspectral satellite data in Dianshan Lake, China Type de document : Article/Communication Auteurs : Liguo Zhou, Auteur ; Dar A. Roberts, Auteur ; Weichun Ma, Auteur ; Hao Zhang, Auteur ; Lin Tang, Auteur Année de publication : 2014 Article en page(s) : pp 41 - 47 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Chine
[Termes IGN] chlorophylle
[Termes IGN] image HJ-1A
[Termes IGN] image hyperspectrale
[Termes IGN] lacRésumé : (Auteur) Based on in situ water sampling and field spectral measurements in Dianshan Lake, a semi-analytical three-band algorithm was used to estimate Chlorophylla (Chla) content in case II waters. The three bands selected to estimate Chla for high concentrations included 653, 691 and 748 nm. An equation, based on the difference in reciprocal reflectance between 653 and 691 nm, multiplied by reflectance at 748 nm as [Rrs-1(653) - Rrs-1(691)] Rrs(748), explained 85.57% of variance in Chla concentration with a root mean square error (RMSE) of Numéro de notice : A2014-084 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.11.016 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.11.016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32989
in ISPRS Journal of photogrammetry and remote sensing > vol 88 (February 2014) . - pp 41 - 47[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2014021 RAB Revue Centre de documentation En réserve L003 Disponible A GIHS-based spectral preservation fusion method for remote sensing images using edge restored spectral modulation / Xiran Zhou in ISPRS Journal of photogrammetry and remote sensing, vol 88 (February 2014)
PermalinkNonlinear unmixing of hyperspectral data using semi-nonnegative matrix factorization / Naoto Yokoya in IEEE Transactions on geoscience and remote sensing, vol 52 n° 2 (February 2014)
PermalinkStructured sparse method for hyperspectral unmixing / Feiyun Zhu in ISPRS Journal of photogrammetry and remote sensing, vol 88 (February 2014)
PermalinkAssessing the performance of two unsupervised dimensionality reduction techniques on hyperspectral APEX data for high resolution urban land-cover mapping / Luca Demarchi in ISPRS Journal of photogrammetry and remote sensing, vol 87 (January 2014)
PermalinkCaractérisation et cartographie de la structure forestière à partir d'images satellitaires à très haute résolution spatiale / Benoit Beguet (2014)
PermalinkCollaborative sparse regression for hyperspectral unmixing / Marian-Daniel Iordache in IEEE Transactions on geoscience and remote sensing, vol 52 n° 1 tome 1 (January 2014)
PermalinkCombining top-down and bottom-up approaches for building detection in a single very high resolution satellite image / Mahmoud Mohammed Sidi Youssef (2014)
PermalinkComparaison de méthodes d'extraction automatique à partir d'images multispectrales / Valerio Baiocchi in Géomatique expert, n° 96 (01/01/2014)
PermalinkPermalinkDétection de bâtiments à partir d’une image satellitaire par combinaison d’approches ascendante et descendante / Mohamed Mahmoud Sidi Yousseff (2014)
PermalinkHyperspectral image classification using nearest feature line embedding approach / Yang-Lang Chang in IEEE Transactions on geoscience and remote sensing, vol 52 n° 1 tome 1 (January 2014)
PermalinkMapping a priori defined plant associations using remotely sensed vegetation characteristics / Hans D. Rölofsen in Remote sensing of environment, vol 140 (January 2014)
PermalinkUse intermediate results of wrapper band selection methods: A first step toward the optimization of spectral configuration for land cover classifications / Arnaud Le Bris (2014)
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PermalinkAutomated detection of buildings from single VHR multispectral images using shadow information and graph cuts / Ali Ozgun Ok in ISPRS Journal of photogrammetry and remote sensing, vol 86 (December 2013)
PermalinkGeneration and validation of high-resolution DEMs from Worldview-2 stereo data / Umut Gunes Sefercik in Photogrammetric record, vol 28 n° 144 (December 2013 - February 2014)
PermalinkTemperature and emissivity separation from Thermal Airborne Hyperspectral Imager (TASI) data / Yang Hang in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 12 (December 2013)
PermalinkThe use of single-date MODIS imagery for estimating large-scale urban impervious surface fraction with spectral mixture analysis and machine learning techniques / Chengbin Deng in ISPRS Journal of photogrammetry and remote sensing, vol 86 (December 2013)
PermalinkMéthode de sélection des bandes à base de l'analyse en composantes indépendantes appliquée aux images hyperspectrales de télédétection / Seloua Chouaf in Revue Française de Photogrammétrie et de Télédétection, n° 204 (Octobre 2013)
PermalinkAutomatic extraction of building roofs using LIDAR data and multispectral imagery / Mohammad Awrangjeb in ISPRS Journal of photogrammetry and remote sensing, vol 83 (September 2013)
PermalinkGeneralized 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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