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Robust multiple estimator systems for the analysis of biophysical parameters from remotely sensed data / Lorenzo Bruzzone in IEEE Transactions on geoscience and remote sensing, vol 43 n° 1 (January 2005)
[article]
Titre : Robust multiple estimator systems for the analysis of biophysical parameters from remotely sensed data Type de document : Article/Communication Auteurs : Lorenzo Bruzzone, Auteur ; F. Melgani, Auteur Année de publication : 2005 Article en page(s) : pp 159 - 174 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s)
[Termes IGN] estimateur
[Termes IGN] Perceptron multicouche
[Termes IGN] régression
[Termes IGN] réseau neuronal artificiel
[Termes IGN] télédétection spatiale
[Termes IGN] variable biophysique (végétation)Résumé : (Auteur) In this paper, an approach based on multiple estimator systems (MESs) for the estimation of biophysical parameters from remotely sensed data is proposed. The rationale behind the proposed approach is to exploit the peculiarities of an ensemble of different estimators in order to improve the robustness (and in some cases the accuracy) of the estimation process. The proposed MESs can be implemented in two conceptually different ways. One extends the use of an approach previously proposed in the regression literature to the estimation of biophysical parameters from remote sensing data. This approach integrates the estimates obtained from the different regression algorithms making up the ensemble by a direct linear combination (combination-based approach). The other consists of a novel approach that provides as output the estimate obtained by the regression algorithm (included in the ensemble) characterized by the highest expected accuracy in the region of the feature space associated with the considered pattern (selection-based approach). This estimator is identified based on a proper partition of the feature space. The effectiveness of the proposed approach has been assessed on the problem of estimating water quality parameters from multispectral remote sensing data. In particular, the presented MES-based approach has been evaluated by considering different operational conditions where the single estimators included in the ensemble are: 1) based on the same or on different regression methods; 2) characterized by different tradeoffs between correlated errors and accuracy of the estimates; 3) trained on samples affected or not by measurement errors. In the definition of the ensemble particular attention is devoted to support vector machines (SVMs), which are a promising approach to the solution of regression problems. In particular, a detailed experimental analysis on the effectiveness of SVMs for solving the considered estimation problem is presented. The experimental results point out that the SVM method is effective and that the proposed MES approach is capable of increasing both the robustness and accuracy of the estimation process. Numéro de notice : A2005-060 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2004.839818 En ligne : https://doi.org/10.1109/TGRS.2004.839818 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27198
in IEEE Transactions on geoscience and remote sensing > vol 43 n° 1 (January 2005) . - pp 159 - 174[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-05011 RAB Revue Centre de documentation En réserve L003 Disponible Vicarious radiometric calibration of satellite ocean colour sensors / D. Antoine (01/09/2004)
contenu dans La recherche scientifique spatiale en France, Rapport 2004 au Cospar, Comité Mondial de la Recherche Spatiale, 35e assemblée scientifique, 18-25 juillet 2004 / Yves d' Escatha (2004)
Titre : Vicarious radiometric calibration of satellite ocean colour sensors Titre original : Etalonnage radiométrique indirect des capteurs satellite couleur de l'océan Type de document : Article/Communication Auteurs : D. Antoine, Auteur ; Malik Chami, Auteur Editeur : Paris, Toulouse, Kourou [France] : Centre National d'Etudes Spatiales CNES Année de publication : 01/09/2004 Conférence : COSPAR 2004, 35th scientific assembly of the world committee for space research 18/07/2004 25/07/2004 Paris France Importance : pp 106 - 107 Langues : Français (fre) Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s)
[Termes IGN] couleur de l'océan
[Termes IGN] étalonnage radiométrique
[Termes IGN] neurone artificiel
[Termes IGN] photométrie
[Termes IGN] polarisation
[Termes IGN] radianceRésumé : (Auteur) The basic principle of the so-called vicarious calibration of ocean colour satellite observations is first briefly recalled. A new method is then presented, which is providing elements for this vicarious calibration, starting from sun-photometer ground measurements of sky radiances and degree of polarisation, and based on a neural network approach for the inversion of these measurements in terms of aerosol optical properties. Numéro de notice : C2004-002 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Communication DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=65013 A split model for extraction of subpixel impervious surface information / Y. Wang in Photogrammetric Engineering & Remote Sensing, PERS, vol 70 n° 7 (July 2004)
[article]
Titre : A split model for extraction of subpixel impervious surface information Type de document : Article/Communication Auteurs : Y. Wang, Auteur ; X. Zhang, Auteur Année de publication : 2004 Article en page(s) : pp 821 - 828 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] banlieue
[Termes IGN] classification par réseau neuronal
[Termes IGN] données multicapteurs
[Termes IGN] image Landsat-TM
[Termes IGN] milieu urbain
[Termes IGN] réseau neuronal artificiel
[Termes IGN] surface imperméable
[Termes IGN] valeur radiométriqueRésumé : (Auteur) This paper introduces a Subpixel Proportional Land cover Information Transformation (SPLIT) model to extract proportions of impervious surfaces in urban and suburban areas. High spatial resolution airborne Digital Multispectral Videography (Dmsv) data provided subpixel information for Landsat TM data. The SPLIT model employed a Modularized Artificial Neural Network (MANN) to integrate multi-sensor remote sensing data and to extract proportions of impervious surfaces and other types of land cover within TM pixels. Through a control unit, the MANN was able to decompose a complex task into multiple subtasks by using a group of sub-networks. The SPLIT model identified spectral relations between TM pixel values and the corresponding DMSV subpixel patterns. The established relationship allows extrapolation of the SPLIT model to the areas beyond DMSV data coverage. We applied five intervals, i.e., 81 percent, to map the subpixel proportions of land cover types. We extrapolated the SPLIT model from training sites that have both TM and DMSV coverage into the entire DuPage County with TM data as the input. The extrapolation received 82.9 percent overall accuracyfor the extracted proportions of urban impervious surface. Numéro de notice : A2004-274 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.70.7.821 En ligne : https://doi.org/10.14358/PERS.70.7.821 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26801
in Photogrammetric Engineering & Remote Sensing, PERS > vol 70 n° 7 (July 2004) . - pp 821 - 828[article]Artificial neural network-based techniques for the retrieval of SWE [snow water equivalent] and snow depth from SSM/I data / Marco Tedesco in Remote sensing of environment, vol 90 n° 1 (15/03/2004)
[article]
Titre : Artificial neural network-based techniques for the retrieval of SWE [snow water equivalent] and snow depth from SSM/I data Type de document : Article/Communication Auteurs : Marco Tedesco, Auteur ; J.T. Pulliainen, Auteur ; M. Takala, Auteur ; M.T. Hallikainen, Auteur ; P. Pampaloni, Auteur Année de publication : 2004 Article en page(s) : pp 76 - 85 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] analyse comparative
[Termes IGN] Finlande
[Termes IGN] neige
[Termes IGN] réalité de terrain
[Termes IGN] réseau neuronal artificiel
[Termes IGN] Special sensor microwave imager
[Termes IGN] télédétection en hyperfréquenceRésumé : (Auteur) The retrieval of snow water equivalent (SWE) and snow depth is performed by inverting Special Sensor Microwave Imager (SSM/I) brightness temperatures at 19 and 37 GHz using artificial neural network ANN-based techniques. The SSM/I used data, which consist of Pathfinder Daily EASE-Grid brightness temperatures, were supplied by the National Snow and Ice Data Centre (NSIDC). They were gathered during the period of time included between the beginning of 1996 and the end of 1999 all over Finland. A ground snow data set based on observations of the Finnish Environment Institute (SYKE) and the Finnish Meteorological Institute (FMI) was used to estimate the performances of the technique. The ANN results were confronted with those obtained using the spectral polarization difference (SPD) algorithm, the HUT model-based iterative inversion and the Chang algorithm, by comparing the RMSE, the R , and the regression coefficients. In general, it was observed that the results obtained through ANN-based technique are better than, or comparable to, those obtained through other approaches, when trained with simulated data. Performances were very good when the ANN were trained with experimental data. Numéro de notice : A2004-129 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2003.12.002 En ligne : https://doi.org/10.1016/j.rse.2003.12.002 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26656
in Remote sensing of environment > vol 90 n° 1 (15/03/2004) . - pp 76 - 85[article]Integrating imaging spectroscopy and neural networks to map grass quality in the Kruger National Park, South Africa / Onisimo Mutanga in Remote sensing of environment, vol 90 n° 1 (15/03/2004)
[article]
Titre : Integrating imaging spectroscopy and neural networks to map grass quality in the Kruger National Park, South Africa Type de document : Article/Communication Auteurs : Onisimo Mutanga, Auteur ; Andrew K. Skidmore, Auteur Année de publication : 2004 Article en page(s) : pp 104 - 115 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Afrique du sud (état)
[Termes IGN] analyse comparative
[Termes IGN] azote
[Termes IGN] bande infrarouge
[Termes IGN] bande visible
[Termes IGN] erreur moyenne quadratique
[Termes IGN] herbe
[Termes IGN] image aérienne
[Termes IGN] image HYMAP
[Termes IGN] image hyperspectrale
[Termes IGN] régression linéaire
[Termes IGN] régression multiple
[Termes IGN] réseau neuronal artificiel
[Termes IGN] savaneRésumé : (Auteur) A new integrated approach, involving continuum-removed absorption features, the red edge position and neural networks, is developed and applied to map grass nitrogen concentration in an African savanna rangeland. Nitrogen, which largely determines the nutritional quality of grasslands, is commonly the most limiting nutrient for grazers. Therefore, the remote sensing of foliar nitrogen concentration in savanna rangelands is important for an improved understanding of the distribution and feeding patterns of wildlife. Continuum removal was applied on two absorption features located in the visible (R 550-757) and the SWIR (R 2015-2199) from an atmospherically corrected HYMAP MK1 image. A feature selection algorithm was used to select wavelength variables from the absorption features. Selected band depths from the absorption features as well as the red edge position (REP) were input into a backpropagation neural network. The best-trained neural network was used to map nitrogen concentration over the whole study area. Results indicate that the new integrated approach could explain 60% of the variation in savanna grass nitrogen concentration on an independent test data set, with a root mean square error (rmse) of 0. 13 (+ 8.3 0% of the mean observed nitrogen concentration). This result is better compared to the result obtained using multiple linear regression, which yielded an R of 38%, with a RMSE of 0.16 (+ 10.30% of the mean observed nitrogen concentration) on an independent test data set. The study demonstrates the potential of airborne hyperspectral data and neural networks to estimate and ultimately to map nitrogen concentration in the mixed species environments of Southern Africa. Numéro de notice : A2004-130 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2003.12.004 En ligne : https://doi.org/10.1016/j.rse.2003.12.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26657
in Remote sensing of environment > vol 90 n° 1 (15/03/2004) . - pp 104 - 115[article]A hybrid texture segmentation method for mapping urban land use / Nezamoddin N. Kachouie in Geomatica, vol 58 n° 1 (March 2004)PermalinkAn artificial neural network approach for landslide hazard zonation in the Bhagirathi (Ganga) Valley, Himalayas / M.K. Arora in International Journal of Remote Sensing IJRS, vol 25 n° 3 (February 2004)PermalinkToward universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements / G. Le Maire in Remote sensing of environment, vol 89 n° 1 (15/01/2004)PermalinkModeling reality: how computers mirror life / Iwo Bialynicki-Birula (2004)PermalinkClassification of wheat crop with multi-temporal images: performance of maximum likelihood and artificial neural networks / C.S. Murthy in International Journal of Remote Sensing IJRS, vol 24 n° 23 (December 2003)PermalinkTraining a neural network with a canopy reflectance model to estimate crop leaf area index / F. Mark Danson in International Journal of Remote Sensing IJRS, vol 24 n° 23 (December 2003)PermalinkA cognitive pyramid for contextual classification of remote sensing images / E. Binaghi in IEEE Transactions on geoscience and remote sensing, vol 41 n° 12 (December 2003)PermalinkKnowledge discovery from soil maps using inductive learning / F. Qi in International journal of geographical information science IJGIS, vol 17 n° 8 (december 2003)PermalinkA neural adaptive model for feature extraction and recognition in high resolution remote sensing imagery / E. Binaghi in International Journal of Remote Sensing IJRS, vol 24 n° 20 (October 2003)PermalinkSimulation of development alternatives using neural networks, cellular automata, and GIS for urban planning / A.G. Yeh in Photogrammetric Engineering & Remote Sensing, PERS, vol 69 n° 9 (September 2003)Permalink