IEEE Transactions on geoscience and remote sensing / IEEE Geoscience and remote sensing society (Etats-Unis) . vol 44 n° 8Paru le : 01/08/2006 ISBN/ISSN/EAN : 0196-2892 |
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Ajouter le résultat dans votre panierAerosol optical depth and land surface reflectance from multiangle AATSR measurements: global validation and intersensor comparisons / W.M.F. Grey in IEEE Transactions on geoscience and remote sensing, vol 44 n° 8 (August 2006)
[article]
Titre : Aerosol optical depth and land surface reflectance from multiangle AATSR measurements: global validation and intersensor comparisons Type de document : Article/Communication Auteurs : W.M.F. Grey, Auteur ; P.R.J. North, Auteur ; S.O. Los, Auteur ; R.M. Mitchell, Auteur Année de publication : 2006 Article en page(s) : pp 2184 - 2197 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] aérosol
[Termes IGN] analyse comparative
[Termes IGN] diffusion du rayonnement
[Termes IGN] épaisseur optique
[Termes IGN] image Envisat-AATSR
[Termes IGN] image Terra-MISR
[Termes IGN] image Terra-MODIS
[Termes IGN] Total ozone mapping spectrometerRésumé : (Auteur) This paper presents the results and satellite intercomparisons for the retrieval of aerosol optical depth (AOD) and land surface bidirectional reflectance using the Multiangle Advanced Along-Track Scanning Radiometer (AATSR). The algorithm developed is based on inversion of a physical model of light scattering that requires no a priori knowledge of the land surface. The model is evaluated for a number of sites around the world to test its operation over a range of aerosol types and land covers including dark and bright surfaces. Validation is performed using Aerosol Robotic Network ground-based sun-photometer measurements and by intercomparison with independent estimates of AOD derived from spaceborne instruments including Multiangle Imaging Spectroradiometer (MISR), Moderate Resolution Imaging Spectroradiometer (MODIS), and Total Ozone Mapping Spectrometer (TOMS) aerosol products. Results show good agreement (Pearson's correlation coefficient r2=0.70 for all sites combined) between the AATSR-derived estimates of AOD and the sun-photometer measurements. There is also a high correlation (r2=0.84) between the AATSR- and MISR-derived AOD estimates, but the correlations of the AATSR-derived AOD with MODIS-derived AOD and TOMS aerosol index are lower. In addition, the ability of the sensor to discriminate between different aerosol types is evaluated. Moreover, the estimates of the aerosol properties are used for atmospheric correction of the top-of-atmosphere reflectance. The AATSR surface reflectances are compared with the MODIS bidirectional reflectance distribution function/Albedo and MISR surface products and are shown to correspond with root-mean-square errors of 0.03 and 0.06 or better, respectively. The retrieval method is applied on an image basis resulting in an image of surface reflectance and a separate map of AOD. A map of AOD at 550 nm covering the Sahel and southern Sahara region is presented to demonstrate operation at regional and potentially global scales. Copyright IEEE Numéro de notice : A2006-394 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2006.872079 En ligne : https://ieeexplore.ieee.org/document/1661807 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28118
in IEEE Transactions on geoscience and remote sensing > vol 44 n° 8 (August 2006) . - pp 2184 - 2197[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-06081 RAB Revue Centre de documentation En réserve L003 Disponible On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance / F. Gao in IEEE Transactions on geoscience and remote sensing, vol 44 n° 8 (August 2006)
[article]
Titre : On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance Type de document : Article/Communication Auteurs : F. Gao, Auteur ; J. Masek, Auteur ; M. Schwaller, Auteur ; D. Gramond, Auteur Année de publication : 2006 Article en page(s) : pp 2207 - 2218 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] accentuation d'image
[Termes IGN] fusion d'images
[Termes IGN] image Landsat-ETM+
[Termes IGN] image Terra-MODIS
[Termes IGN] précision géométrique (imagerie)
[Termes IGN] réflectance du solRésumé : (Auteur) The 16-day revisit cycle of Landsat has long limited its use for studying global biophysical processes, which evolve rapidly during the growing season. In cloudy areas of the Earth, the problem is compounded, and researchers are fortunate to get two to three clear images per year. At the same time, the coarse resolution of sensors such as the Advanced Very High Resolution Radiometer and Moderate Resolution Imaging Spectroradiometer (MODIS) limits the sensors' ability to quantify biophysical processes in heterogeneous landscapes. In this paper, the authors present a new spatial and temporal adaptive reflectance fusion model (STARFM) algorithm to blend Landsat and MODIS surface reflectance. Using this approach, high-frequency temporal information from MODIS and high-resolution spatial information from Landsat can be blended for applications that require high resolution in both time and space. The MODIS daily 500-m surface reflectance and the 16-day repeat cycle Landsat Enhanced Thematic Mapper Plus (ETM+) 30-m surface reflectance are used to produce a synthetic "daily" surface reflectance product at ETM+ spatial resolution. The authors present results both with simulated (model) data and actual Landsat/MODIS acquisitions. In general, the STARFM accurately predicts surface reflectance at an effective resolution close to that of the ETM+. However, the performance depends on the characteristic patch size of the landscape and degrades somewhat when used on extremely heterogeneous fine-grained landscapes. Copyright IEEE Numéro de notice : A2006-395 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2006.872081 En ligne : https://doi.org/10.1109/TGRS.2006.872081 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28119
in IEEE Transactions on geoscience and remote sensing > vol 44 n° 8 (August 2006) . - pp 2207 - 2218[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-06081 RAB Revue Centre de documentation En réserve L003 Disponible A support vector method for anomaly detection in hyperspectral imagery / Amit Banerjee in IEEE Transactions on geoscience and remote sensing, vol 44 n° 8 (August 2006)
[article]
Titre : A support vector method for anomaly detection in hyperspectral imagery Type de document : Article/Communication Auteurs : Amit Banerjee, Auteur ; Philippe Burlina, Auteur ; Chris Diehl, Auteur Année de publication : 2006 Article en page(s) : pp 2282 - 2291 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] aide à la décision
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] détection d'erreur
[Termes IGN] détection de cible
[Termes IGN] image hyperspectrale
[Termes IGN] méthode fondée sur le noyau
[Termes IGN] test statistiqueRésumé : (Auteur) This paper presents a method for anomaly detection in hyperspectral images based on the support vector data description (SVDD), a kernel method for modeling the support of a distribution. Conventional anomaly-detection algorithms are based upon the popular Reed-Xiaoli detector. However, these algorithms typically suffer from large numbers of false alarms due to the assumptions that the local background is Gaussian and homogeneous. In practice, these assumptions are often violated, especially when the neighborhood of a pixel contains multiple types of terrain. To remove these assumptions, a novel anomaly detector that incorporates a nonparametric background model based on the SVDD is derived. Expanding on prior SVDD work, a geometric interpretation of the SVDD is used to propose a decision rule that utilizes a new test statistic and shares some of the properties of constant false-alarm rate detectors. Using receiver operating characteristic curves, the authors report results that demonstrate the improved performance and reduction in the false-alarm rate when using the SVDD-based detector on wide-area airborne mine detection (WAAMD) and hyperspectral digital imagery collection experiment (HYDICE) imagery. Copyright IEEE Numéro de notice : A2006-396 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2006.873019 En ligne : https://doi.org/10.1109/TGRS.2006.873019 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28120
in IEEE Transactions on geoscience and remote sensing > vol 44 n° 8 (August 2006) . - pp 2282 - 2291[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-06081 RAB Revue Centre de documentation En réserve L003 Disponible Comparison of computational intelligence based classification techniques for remotely sensed optical image classification / D. Stathakis in IEEE Transactions on geoscience and remote sensing, vol 44 n° 8 (August 2006)
[article]
Titre : Comparison of computational intelligence based classification techniques for remotely sensed optical image classification Type de document : Article/Communication Auteurs : D. Stathakis, Auteur ; A. Vasilakos, Auteur Année de publication : 2006 Article en page(s) : pp 2305 - 2318 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s)
[Termes IGN] analyse comparative
[Termes IGN] classification dirigée
[Termes IGN] classification floue
[Termes IGN] classification par algorithme génétique
[Termes IGN] classification par réseau neuronal
[Termes IGN] image optique
[Termes IGN] occupation du solRésumé : (Auteur) Several computational intelligence components, namely neural networks (NNs), fuzzy sets, and genetic algorithms (GAs), have been applied separately or in combination to the process of remotely sensed data classification. By applying computational intelligence, we expect increased accuracy through the use of NNs, optimal NN structure and parameter determination via GAs, and transparency using fuzzy sets is expected. This paper systematically reviews and compares several configurations in the particular context of remote sensing for land cover. In addition, some of the configurations used here, such as NEFCASS and CANFIS, have few previous applications in the field. A comparison of the configurations is achieved by testing the different methods with exactly the same case-study data. A thorough assessment of results is performed by constructing an accuracy matrix for each training and testing data set. The evaluation of different methods is not only based on accuracy but also on compactness, completeness, and consistency. The architecture, produced rule set, and training parameters for the specific classification task are presented. Some comments and directions for future work are given. Copyright IEEE Numéro de notice : A2006-397 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2006.872903 En ligne : https://doi.org/10.1109/TGRS.2006.872903 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28121
in IEEE Transactions on geoscience and remote sensing > vol 44 n° 8 (August 2006) . - pp 2305 - 2318[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-06081 RAB Revue Centre de documentation En réserve L003 Disponible