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Emissivity maps to retrieve land-surface temperature from MSG/SEVIRI / L.F. Peres in IEEE Transactions on geoscience and remote sensing, vol 43 n° 8 (August 2005)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 43 n° 8 (August 2005) . - pp 1834 - 1844 Titre : Emissivity maps to retrieve land-surface temperature from MSG/SEVIRI Type de document : Article/Communication Auteurs : L.F. Peres, Auteur ; C.C. Dacamara, Auteur Année de publication : 2005 Article en page(s) : pp 1834 - 1844 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse comparative [Termes IGN] bande infrarouge [Termes IGN] couvert végétal [Termes IGN] emissivité [Termes IGN] image MSG-SEVIRI [Termes IGN] image Terra-MODIS [Termes IGN] température au sol Résumé : (Auteur) Retrieval of land-surface temperature (LST) using data from the METEOSAT Second Generation-1 (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI) requires adequate estimates of land-surface emissivity (LSE). In this context, LSE maps for SEVIRI channels IR3.9, IR8.7, IR10.8, and IR12.0 were developed based on the vegetation cover method. A broadband LSE map (3-14 um) was also developed for estimating longwave surface fluxes that may prove to be useful in both energy balance and climate modeling studies. LSE is estimated from conventional static land-cover classifications, LSE spectral data for each land cover, and fractional vegetation cover (FVC) information. Both International Geosphere-Biosphere Program (IGBP) Data and Information System (DIS) and Moderate Resolution Imaging Spectrometer (MODIS) MOD12Q1 land-cover products were used to build the LSE maps. Data on LSE were obtained from the Johns Hopkins University and Jet Propulsion Laboratory spectral libraries included in the Advanced Spaceborne Thermal Emission and Reflection Radiometer spectral library, as well as from the MODIS University of California-Santa Barbara spectral library. FVC data for each pixel were derived based on the normalized differential vegetation index. Depending on land cover, the LSE errors for channels IR3.9 and IR8.7 spatially vary from +0.6% to +24% and +0.1% to +33%, respectively, whereas the broadband spectrum errors lie between +0.3% and +7%. In the case of channels IR10.8 and IR12.0,73% of the land surfaces within the MSG disk present relative errors less than +1.5%, and almost all (26%) of the remaining areas have relative errors of +2.0 %. Developed LSE maps provide a first estimate of the ranges of LSE in SEVIRI channels for each surface type, and obtained results may be used to assess the sensitivity of algorithms where an a priori knowledge of LSE is required. Numéro de notice : A2005-392 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2005.851172 En ligne : https://doi.org/10.1109/TGRS.2005.851172 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27528 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-05081	RAB	Revue	Centre de documentation	En réserve L003	Disponible

A statistical self-organizing learning system for remote sensing classification / H.M. Chi in IEEE Transactions on geoscience and remote sensing, vol 43 n° 8 (August 2005)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 43 n° 8 (August 2005) . - pp 1890 - 1900 Titre : A statistical self-organizing learning system for remote sensing classification Type de document : Article/Communication Auteurs : H.M. Chi, Auteur ; O.K. Ersoy, Auteur Année de publication : 2005 Article en page(s) : pp 1890 - 1900 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage automatique [Termes IGN] carte de Kohonen [Termes IGN] classification par réseau neuronal [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] image hyperspectrale [Termes IGN] méthode des moindres carrés [Termes IGN] noeud [Termes IGN] système expert [Termes IGN] transformation non linéaire Résumé : (Auteur) A new learning system called a statistical self-organizing learning system (SSOLS), combining functional-link neural networks, statistical hypothesis testing, and self-organization of a number of enhancement nodes, is introduced for remote sensing applications. Its structure consists of two stages, a mapping stage and a learning stage. The input training vectors are initially mapped to the enhancement vectors in the mapping stage by multiplying with a random matrix, followed by pointwise nonlinear transformations. Starting with only one enhancement node, the enhancement layer incrementally adds an extra node in each iteration. The optimum dimension of the enhancement layer is determined by using an efficient leave-one-out cross-validation method. In this way, the number of enhancement nodes is also learned automatically. A t-test algorithm can also be applied to the mapping stage to mitigate the effect of overfitting and to further reduce the number of enhancement nodes required, resulting in a more compact network. In the learning stage, both the input vectors and the enhancement vectors are fed into a least squares learning module to obtain the estimated output vectors. This is made possible by choosing the output layer linear. In addition, several SSOLSs can be trained independently in parallel to form a consensual SSOLS, whose final output is a linear combination of the outputs of each SSOLS module. The SSOLS is simple, fast to compute, and suitable for remote sensing applications, especially with hyperspectral image data of high dimensionality. Numéro de notice : A2005-393 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2005.851188 En ligne : https://doi.org/10.1109/TGRS.2005.851188 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27529 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-05081	RAB	Revue	Centre de documentation	En réserve L003	Disponible