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Prediction of the error induced by topography in satellite microwave radiometric observations / Luca Pulvirenti in IEEE Transactions on geoscience and remote sensing, vol 49 n° 9 (September 2011)
[article]
Titre : Prediction of the error induced by topography in satellite microwave radiometric observations Type de document : Article/Communication Auteurs : Luca Pulvirenti, Auteur ; Nazzareno Pierdicca, Auteur ; F. Silvio, Auteur Année de publication : 2011 Article en page(s) : pp 3180 - 3188 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] Alpes
[Termes IGN] analyse comparative
[Termes IGN] angle d'incidence
[Termes IGN] bande C
[Termes IGN] bande L
[Termes IGN] classification par réseau neuronal
[Termes IGN] humidité du sol
[Termes IGN] image radar
[Termes IGN] image satellite
[Termes IGN] montagne
[Termes IGN] plaine
[Termes IGN] régression
[Termes IGN] relief
[Termes IGN] sol nu
[Termes IGN] télédétection en hyperfréquence
[Termes IGN] topographie
[Termes IGN] valeur radiométriqueRésumé : (Auteur) A numerical simulator of satellite microwave radiometric observations of mountainous scenes, developed in a previous study, has been used to predict the relief effects on the measurements of a spaceborne radiometer. For this purpose, the trends of the error due to topography, i.e., the difference between the antenna temperature calculated for a topographically variable surface and that computed for a flat terrain versus the parameters representing the relief, have been analyzed. The analysis has been mainly performed for a mountainous area in the Alps by assuming a simplified land-cover scenario consisting of bare terrain with two roughness conditions (smooth and rough soils) and considering L- and C-bands, i.e., those most suitable for soil moisture retrieval. The results have revealed that the error in satellite microwave radiometric observations is particularly correlated to the mean values of the height and slope of the radiometric pixel, as well as to the standard deviations of the aspect angle and local incidence angle. Both a regression analysis and a neural-network approach have been applied to estimate the error as a function of the parameters representing the relief, using the simulator to build training and test sets. The prediction of the topography effects and their correction in radiometric images have turned out to be feasible, at least for the scenarios considered in this study. Numéro de notice : A2011-361 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2010.2096514 Date de publication en ligne : 06/01/2011 En ligne : https://doi.org/10.1109/TGRS.2010.2096514 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31140
in IEEE Transactions on geoscience and remote sensing > vol 49 n° 9 (September 2011) . - pp 3180 - 3188[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2011091 RAB Revue Centre de documentation En réserve L003 Disponible Use of high-resolution satellite imagery in an integrated model to predict the distribution of shade coffee tree hybrid zones / C. Gomez in Remote sensing of environment, vol 114 n° 11 (15/11/2010)
[article]
Titre : Use of high-resolution satellite imagery in an integrated model to predict the distribution of shade coffee tree hybrid zones Type de document : Article/Communication Auteurs : C. Gomez, Auteur ; M. Mangeas, Auteur ; Marcel Petit, Auteur ; Christina Corbane, Auteur ; et al., Auteur Année de publication : 2010 Article en page(s) : pp 2731 - 2744 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse texturale
[Termes IGN] carte thématique
[Termes IGN] classification dirigée
[Termes IGN] classification par arbre de décision
[Termes IGN] classification par réseau neuronal
[Termes IGN] Coffea (genre)
[Termes IGN] couvert forestier
[Termes IGN] image à haute résolution
[Termes IGN] image panchromatique
[Termes IGN] image Quickbird
[Termes IGN] modèle numérique de surface
[Termes IGN] Nouvelle-Calédonie
[Termes IGN] ombre
[Termes IGN] pansharpening (fusion d'images)
[Termes IGN] prédictionRésumé : (Auteur) In New Caledonia (21°S, 165°E), shade-grown coffee plantations were abandoned for economic reasons in the middle of the 20th century. Coffee species (Coffea arabica, C. canephora and C. liberica) were introduced from Africa in the late 19th century, they survived in the wild and spontaneously cross-hybridized. Coffee species were originally planted in native forest in association with leguminous trees (mostly introduced species) to improve their growth. Thus the canopy cover over rustic shade coffee plantations is heterogeneous with a majority of large crowns, attributed to leguminous trees. The aim of this study was to identify suitable areas for coffee inter-specific hybridization in New Caledonia using field based environmental parameters and remotely sensed predictors. Due to the complex structure of tropical vegetation, remote sensing imagery needs to be spatially accurate and to have the appropriate bands for monitoring vegetation cover. Quickbird panchromatic (black and white) imagery at 0.6 to 0.7 m spatial resolutions and multispectral imagery at 2.4 m spatial resolution were pansharpened and used for this study. The two most suitable remotely sensed indicators, canopy heterogeneity and tree crown size, were acquired by the sequential use of tree crown detection (neural network), image processing (such as textural analysis) and classification. All models were supervised and trained on learning data determined by human expertise. The final model has two remotely sensed indicators and three physical parameters based on the Digital Elevation Model: elevation, slope and water flow accumulation. Using these five predictive variables as inputs, two modelling methods, a decision tree and a neural network, were implemented. The decision tree, which showed 96.9% accuracy on the test set, revealed the involvement of ecological parameters in the hybridization of Coffea species. We showed that hybrid zones could be characterized by combinations of modalities, underlining the complexity of the environment concerned. For instance, forest heterogeneity and large crown size, steep slopes (> 53.5%) and elevation between 194 and 429 m asl, are favourable factors for Coffea inter-specific hybridization. The application of the neural network on the whole area gave a predictive map that distinguished the most suitable areas by means of a nonlinear continuous indicator. The map provides a confidence level for each area. The most favourable areas were geographically localized, providing a clue for the detection and conservation of favourable areas for Coffea species neo-diversity. Numéro de notice : A2010-402 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2010.06.007 En ligne : https://doi.org/10.1016/j.rse.2010.06.007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30595
in Remote sensing of environment > vol 114 n° 11 (15/11/2010) . - pp 2731 - 2744[article]Effect of SRTM resolution on morphometric feature identification using neural network - self organizing map / A. Ehsani in Geoinformatica, vol 14 n° 4 (October 2010)
[article]
Titre : Effect of SRTM resolution on morphometric feature identification using neural network - self organizing map Type de document : Article/Communication Auteurs : A. Ehsani, Auteur ; F. Quiel, Auteur ; A. Malekian, Auteur Année de publication : 2010 Article en page(s) : pp 405 - 424 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] aire protégée
[Termes IGN] Carpates
[Termes IGN] carte de Kohonen
[Termes IGN] classification par réseau neuronal
[Termes IGN] détection de changement
[Termes IGN] données topographiques
[Termes IGN] géomorphométrie
[Termes IGN] image SIR-C-X-SAR
[Termes IGN] MNS SRTMRésumé : (Auteur) In this study, we present a semi-automatic procedure using Neural Networks—Self Organizing Map—and Shuttle Radar Topography Mission DEMs to characterize morphometric features of the landscape in the Man and Biosphere Reserve “Eastern Carpathians”. We investigate specially the effect of two resolutions, SIR-C with 3 arc seconds and X-SAR with 1 arc second for morphometric feature identification. Specifically we investigate how the SRTM/C band data with 30 m interpolated grid, corresponding to SRTM/X band 30 m, affect the morphometric characterization and topography derivatives. To reduce misregistration between the DEMs, spatial co-registration was performed and a RMSE of 0.48 pixel was achieved. Morphometric parameters such as slope, maximum curvature, minimum curvature and cross-sectional curvature are derived using a bivariate quadratic approximation on 90 m, 30 m and interpolated 30 m DEMs. Self Organizing Map (SOM) is used for the classification of morphometric parameters into ten exclusive and exhaustive classes. These classes were analyzed as morphometric features such as ridge, channel, crest line and planar for all data sets based on feature space (scatter plot), morphometric signatures and 3D inspection of the area. The map quality is analyzed by oblique views with contour lines overlaid. Using the X band DEM with 30 m grid as benchmark, a change detection technique was used to quantify differences in morphometric features and to assess the scale effect going from a 90 m (C-band) DEM to an interpolated 30 m DEM. The same procedure is used to study the effect of different resolutions on morphometric features. Morphometric parameters were computed by a moving window size 5 x 5 (corresponding to 450 m on the ground) over SRTM- 90 m. To cover the same ground area, a moving window size of 15 x 15 is used for the 30 m DEM. The change analysis showed the amount of resolution dependency of morphometric features. Overall, the results showed that the introduced method is very useful for identification of morphometric features based on SRTM resolution. Decreasing the grid size from 90 m to 30 m reveals considerably more detailed information emphasizing local conditions. Comparison between results from DEM-30 m as reference data set and interpolated 30 m, showed a rate of change of 31.5% which is negligible. About 17% of this rate correspond to classes with mean slope > 10°. Of the morphometric parameters, the cross sectional curvature is most sensitive to DEM resolution. Increasing spatial resolution reduces the main constrains for morphometric analysis with SRTM 90 m data, such as unrealistic features and isolated single elements in the output map. So in case of lack of high resolution data, the SRTM 90 m data could be interpolated and used for further geomorphic analysis. Copyright Springer Numéro de notice : A2010-302 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s10707-009-0085-4 Date de publication en ligne : 29/04/2009 En ligne : https://doi.org/10.1007/s10707-009-0085-4 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30496
in Geoinformatica > vol 14 n° 4 (October 2010) . - pp 405 - 424[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 057-2010041 RAB Revue Centre de documentation En réserve L003 Disponible Rewiew of non-parametric models for dam deformation analysis in China / N. Deng in Geomatica, vol 63 n° 3 (September 2009)
[article]
Titre : Rewiew of non-parametric models for dam deformation analysis in China Type de document : Article/Communication Auteurs : N. Deng, Auteur ; Y. Zhang, Auteur ; Szostak-chrzanowski, Auteur ; J.G. Wang, Auteur Année de publication : 2009 Article en page(s) : 9 p. ; pp 211 - 219 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques
[Termes IGN] analyse de données
[Termes IGN] barrage
[Termes IGN] Chine
[Termes IGN] classificateur non paramétrique
[Termes IGN] classification par réseau neuronal
[Termes IGN] déformation d'édifice
[Termes IGN] méthode des moindres carrés
[Termes IGN] modèle de déformation tectonique
[Termes IGN] régression
[Termes IGN] surveillance d'ouvrageRésumé : (Auteur) L'analyse de la déformation d'un barrage est l'une des composantes essentielles de la surveillance et de la gestion de la sécurité du barrage. En général, les modèles dynamiques utilisés pour l'analyse de la déformation du barrage englobent des modèles paramétriques et non paramétriques. En Chine, une grande partie de l'effort de recherche en matière d'analyse de données a été dirigée vers le développement de nouveaux modèles non paramétriques comme la « régression pas à pas », la « régression partielle par moindres carrés », le « réseau neuronal artificiel », la « série chronologique », le « système Grey » et leurs combinaisons. Ces méthodes ont été utilisées pour effectuer des analyses de déformation de barrage et ont obtenu des résultats satisfaisants tant pour la modélisation que pour la prédiction des déformations. Cet article débute par une discussion sur la sélection des variables environnementales pour la modélisation de la déformation. Les principes des différentes méthodes des modèles non paramétriques utilisés en Chine sont ensuite présentés accompagnés d'un examen des applications de ces modèles pour la surveillance de la sécurité du barrage. Enfin, l'intégration des différents modèles pour la surveillance du barrage est évaluée. Copyright Geomatica Numéro de notice : A2009-459 Affiliation des auteurs : non IGN Thématique : MATHEMATIQUE Nature : Article DOI : 10.5623/geomat-2009-0030 En ligne : https://cdnsciencepub.com/doi/abs/10.5623/geomat-2009-0030 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30088
in Geomatica > vol 63 n° 3 (September 2009) . - 9 p. ; pp 211 - 219[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 035-09031 RAB Revue Centre de documentation En réserve L003 Disponible Potentiality of feed-forward neural networks for classifying dark formations to oil spills and look-alikes / Konstantinos Topouzelis in Geocarto international, vol 24 n° 3 (June - July 2009)
[article]
Titre : Potentiality of feed-forward neural networks for classifying dark formations to oil spills and look-alikes Type de document : Article/Communication Auteurs : Konstantinos Topouzelis, Auteur ; V. Karathanassi, Auteur ; P. Pavlaskis, Auteur ; D. Rokos, Auteur Année de publication : 2009 Article en page(s) : pp 179 - 191 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] classification par réseau neuronal
[Termes IGN] détection
[Termes IGN] fonction de base radiale
[Termes IGN] hydrocarbure
[Termes IGN] image radar
[Termes IGN] marée noire
[Termes IGN] Perceptron multicouche
[Termes IGN] pollution des mers
[Termes IGN] rétrodiffusionRésumé : (Auteur) Radar backscatter values from oil spills are very similar to backscatter values from very calm sea areas and other ocean phenomena. Several studies aiming at oil spill detection have been conducted. Most of these studies rely on the detection of dark areas, which have high Bayesian probability of being oil spills. The drawback of these methods is a complex process, mainly because non-linearly separable datasets are introduced in statistically based decisions. The use of neural networks (NNs) in remote sensing has increased significantly, as NNs can simultaneously handle non-linear data of a multidimensional input space. In this article, we investigate the ability of two commonly used feed-forward NN models: multilayer perceptron (MLP) and radial basis function (RBF) networks, to classify dark formations in oil spills and look-alike phenomena. The appropriate training algorithm, type and architecture of the optimum network are subjects of research. Inputs to the networks are the original synthetic aperture radar image and other images derived from it. MLP networks are recognized as more suitable for oil spill detection. Numéro de notice : A2009-186 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106040802488526 Date de publication en ligne : 19/05/2009 En ligne : https://doi.org/10.1080/10106040802488526 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29816
in Geocarto international > vol 24 n° 3 (June - July 2009) . - pp 179 - 191[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-09031 RAB Revue Centre de documentation En réserve L003 Disponible Carto-Som: Cartogram creation using self-organizing maps / R. Henriques in International journal of geographical information science IJGIS, vol 23 n°3-4 (march - april 2009)PermalinkNeuro-fuzzy based analysis of hyperspectral imagery / F. Qiu in Photogrammetric Engineering & Remote Sensing, PERS, vol 74 n° 10 (October 2008)PermalinkGeneralization-oriented road line classification by means of an artificial neural network / J.L. Garcia Balboa in Geoinformatica, vol 12 n° 3 (September - November 2008)PermalinkLand-cover classification using ASTER: multi-band combinations based on wavelet fusion and SOM neural network / H. Bagan in Photogrammetric Engineering & Remote Sensing, PERS, vol 74 n° 3 (March 2008)PermalinkMultisource classification using Support Vector Machines: an empirical comparison with Decision Tree and Neural Network classifiers / P. Watanachaturaporn in Photogrammetric Engineering & Remote Sensing, PERS, vol 74 n° 2 (February 2008)PermalinkMultispectral land use classification using neural networks and support vector machines: one or the other, or both? / B. Dixon in International Journal of Remote Sensing IJRS, vol 29 n°3-4 (February 2008)PermalinkThe application of artificial neural networks to the analysis of remotely sensed data / J.F. Mas in International Journal of Remote Sensing IJRS, vol 29 n°3-4 (February 2008)PermalinkGlobal elevation ancillary data for land-use classification using granular neural networks / D. Stathakis in Photogrammetric Engineering & Remote Sensing, PERS, vol 74 n° 1 (January 2008)PermalinkVisual analysis of network traffic – interactive monitoring, detection, and interpretation of security threats / Florian Mansmann (ca 2008)PermalinkBorder vector detection and adaptation for classification of multispectral and hyperspectral remote sensing images / N.G. Kasapoglu in IEEE Transactions on geoscience and remote sensing, vol 45 n° 12 Tome 1 (December 2007)PermalinkFeature selection by genetic algorithms in object-based classification of Ikonos imagery for forest mapping in Flanders, Belgium / F.M.B. Van Coillie in Remote sensing of environment, vol 110 n° 4 (30/10/2007)PermalinkMultispectral image classification: a supervised neural computation approach based on rough-fuzzy membership function and weak fuzzy similarity relation / A. Agrawal in International Journal of Remote Sensing IJRS, vol 28 n°19-20 (October 2007)PermalinkDetection and discrimination between oil spills and look-alike phenomena through neural networks / Konstantinos Topouzelis in ISPRS Journal of photogrammetry and remote sensing, vol 62 n° 4 (September 2007)PermalinkLe WI-FI pour le positionnement et la navigation en intérieur / A. Betremieux in XYZ, n° 111 (juin - août 2007)PermalinkAtmospheric correction algorithm for MERIS above case-2 waters / Th. Schroeder in International Journal of Remote Sensing IJRS, vol 28 n°7-8 (April 2007)PermalinkImproving land-cover classification using recognition threshold neural networks / M.J. Aitkenhead in Photogrammetric Engineering & Remote Sensing, PERS, vol 73 n° 4 (April 2007)PermalinkMapping land cover from detailed aerial photography data using textural and neural network analysis / R. Cots-Folch in International Journal of Remote Sensing IJRS, vol 28 n°7-8 (April 2007)PermalinkAn operational MISR pixel classifier using support vector machines / D. Mazzoni in Remote sensing of environment, vol 107 n° 1-2 (15 March 2007)PermalinkA data-mining approach to associating MISR smoke plume heights with MODIS fire measurements / D. Mazzoni in Remote sensing of environment, vol 107 n° 1-2 (15 March 2007)PermalinkNeural network estimation of LAI, fAPAR, fCover and LAI*Cab, from top of canopy MERIS reflectance data: principles and validation / Cédric Bacour in Remote sensing of environment, vol 105 n° 4 (30/12/2006)PermalinkMultiple support vector machines for land cover change detection: an application for mapping urban extensions / H. Nemmour in ISPRS Journal of photogrammetry and remote sensing, vol 61 n° 2 (November 2006)PermalinkSatellite image classification using granular neural networks / D. Stathakis in International Journal of Remote Sensing IJRS, vol 27 n°18 - 19 - 20 (October 2006)PermalinkComparison 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)PermalinkTree cover and height estimation in the Fennoscandian tundra-taiga transition zone using multiangular MISR data / J. Heiskanen in Remote sensing of environment, vol 103 n° 1 (15 July 2006)PermalinkSome issues in the classification of DAIS hyperspectral data / M. Pal in International Journal of Remote Sensing IJRS, vol 27 n°12-13-14 (July 2006)PermalinkArtificial neural networks for mapping regional-scale upland vegetation from high spatial resolution imagery / H. Mills in International Journal of Remote Sensing IJRS, vol 27 n° 11 (June 2006)PermalinkIntegrating LIDAR elevation data, multi-spectral imagery and neural network modelling for marsh characterization / J.T. Morris in International Journal of Remote Sensing IJRS, vol 26 n° 23 (December 2005)PermalinkReconstructing spatiotemporal trajectories from sparse data / P. Partsinevelos in ISPRS Journal of photogrammetry and remote sensing, vol 60 n° 1 (December 2005 - March 2006)PermalinkClassifying and mapping wildfire severity: a comparison of methods / C.K. Brewer in Photogrammetric Engineering & Remote Sensing, PERS, vol 71 n° 11 (November 2005)PermalinkA 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)PermalinkNouvelle approche du réseau ARTMAP flou : application à la classification multi-spectrale des images SPOT XS de la baie d'Alger / F. Alilat in Revue Française de Photogrammétrie et de Télédétection, n° 177 (Juin 2005)PermalinkRadial basis function neural networks classification using very high spatial resolution satellite imagery: an application to the habitat area of Lake Kerkini (Greece) / Iphigenia Keramitsoglou in International Journal of Remote Sensing IJRS, vol 26 n° 9 (May 2005)PermalinkRepresenting and reducing error in natural-resource classification using model combination / Zhi Huang in International journal of geographical information science IJGIS, vol 19 n° 5 (may 2005)PermalinkNested hyper-rectangle learning model for remote sensing: land-cover classification / L. Chen in Photogrammetric Engineering & Remote Sensing, PERS, vol 71 n° 3 (March 2005)PermalinkSparse grids: a new predictive modelling method for the analysis of geographic data / S.W. Laffan in International journal of geographical information science IJGIS, vol 19 n° 3 (march 2005)PermalinkMapping tropical forest structure in south-eastern Madagascar using remote sensing and artificial neural networks / J.C. Ingram in Remote sensing of environment, vol 94 n° 4 (28/02/2005)PermalinkEstimation and monitoring of bare soil/vegetation ratio with SPOT vegetation and HRVIR / Grégoire Mercier in IEEE Transactions on geoscience and remote sensing, vol 43 n° 2 (February 2005)PermalinkUncertainty and confidence in land cover classification using a hybrid classifier approach / W. Liu in Photogrammetric Engineering & Remote Sensing, PERS, vol 70 n° 8 (August 2004)PermalinkA split model for extraction of subpixel impervious surface information / Y. Wang in Photogrammetric Engineering & Remote Sensing, PERS, vol 70 n° 7 (July 2004)PermalinkChange detection techniques / Dong Lu in International Journal of Remote Sensing IJRS, vol 25 n° 12 (June 2004)PermalinkAn advanced system for the automatic classification of multitemporal SAR images / Lorenzo Bruzzone in IEEE Transactions on geoscience and remote sensing, vol 42 n° 6 (June 2004)PermalinkSub-pixel mapping and sub-pixel sharpening using neural network predicted wavelet coefficients / K.C. Mertens in Remote sensing of environment, vol 91 n° 2 (30/05/2004)PermalinkEvaluation comparative en cartographie forestière de l'analyse de texture et de la transformée en paquets d'ondelettes par le moyen d'un classifieur / A. Hammouch in Photo interprétation, vol 40 n° 1 (Mars 2004)PermalinkApproaches to fractional land cover and continuous field mapping: a comparative assessment over the BOREAS [BOReal Ecosystem Atmosphere Study] study region / R. Fernandes in Remote sensing of environment, vol 89 n° 2 (30/01/2004)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)PermalinkData fusion and feature extraction in the wavelet domain / Magnus Orn Ulfarsson in International Journal of Remote Sensing IJRS, vol 24 n° 20 (October 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)PermalinkIncreasing the spatial resolution of agricultural land cover maps using a Hopfield neural network / A.J. Tatem in International journal of geographical information science IJGIS, vol 17 n° 7 (october 2003)PermalinkMultitemporal/multiband SAR classification of urban areas using spatial analysis: statistical versus neural kernel-based approach / T. Macri Pellizzei in IEEE Transactions on geoscience and remote sensing, vol 41 n° 10 (October 2003)PermalinkComparing ARTMAP neural network with the maximum-likelihood classifier for detecting urban change / K.C. Seto in Photogrammetric Engineering & Remote Sensing, PERS, vol 69 n° 9 (September 2003)PermalinkImprovements to urban area characterization using multitemporal and multiangle SAR images / F. Dell'acqua in IEEE Transactions on geoscience and remote sensing, vol 41 n° 9 (September 2003)PermalinkThe use of fully polarimetric information for the fuzzy neural classification of SAR images / C.T. Chen in IEEE Transactions on geoscience and remote sensing, vol 41 n° 9 (September 2003)PermalinkA very quick neural network algorithm for cloud detection / K.R. Al-Rawi in Geocarto international, vol 18 n° 1 (March - May 2003)PermalinkPermalinkLand cover classification models using Shuttle Imaging Radar (SIR-C) data: a case study in New Hampshire, USA / R. Narayanan in Geocarto international, vol 17 n° 3 (September - November 2002)PermalinkA multiple-cascade-classifier system for a robust and partially unsupervised updating of land-cover maps / Lorenzo Bruzzone in IEEE Transactions on geoscience and remote sensing, vol 40 n° 9 (September 2002)PermalinkIntegration of classification methods for improvement of land-cover map accuracy / XiaoHang Liu in ISPRS Journal of photogrammetry and remote sensing, vol 56 n° 4 (July - August 2002)PermalinkReconnaissance de patterns par réseaux de neurones / M.K. Allouche in Revue internationale de géomatique, vol 11 n° 2 (juin - aout 2001)PermalinkGeoComputational modelling / Manfred M. Fischer (2001)PermalinkSpot panchromatic imagery and neural networks for information extraction in a complex mountain environment / M.P. Bishop in Geocarto international, vol 14 n° 2 (June - August 1999)PermalinkAdvances in remote sensing and GIS analysis, [selected papers from a meeting held at the University of Southampton, July 25, 1996] / P.M. Atkinson (1999)PermalinkCoopération et fusion d'opérateurs : application au recalage automatique d'objets cartographiques / Pierre Dhérété (1999)PermalinkRSS 99 Earth observation / P. Pan (1999)PermalinkIntegration von Form- und Spektralmerkmalen durch künstliche neuronale Netze bei der Satellitenbildklassifizierung / Karl Segl (1996)PermalinkCaractérisation neuronale des propriétés texturales des images radar à synthèse d'ouverture ERS1 et JERS1 / Philippe Mainguenaud in Bulletin [Société Française de Photogrammétrie et Télédétection], n° 140 (Octobre 1995)PermalinkCaractérisation des textures d'images radar par réseaux de neurones / Philippe Mainguenaud (1995)PermalinkImage and signal processing for remote sensing 2 / Jacky Desachy (1995)PermalinkApport de la fusion d'images satellitaires multicapteurs au niveau pixel en télédétection et photo-interprétation / M. Mangolini (1994)PermalinkUne architecture d'aide à la construction de croquis d'interprétation géographique / Mauro Gaio (1994)PermalinkImage and signal processing for remote sensing, 26-30 september 1994, Rome, Italy / Jacky Desachy (1994)PermalinkImage processing / G. Vernazza (1993)PermalinkContribution à la représentation des connaissances et à leur utilisation pour l'interprétation automatique des images satellite / E. Zahzah (1992)PermalinkArtificial neural network classification using a minimal training set : comparison to conventional supervised classification / G.F. Hepner in Photogrammetric Engineering & Remote Sensing, PERS, vol 56 n° 4 (april 1990)PermalinkNeural networks / Association des entretiens de Lyon (1990)PermalinkClassification of merged AVHRR and SMMR arctic data with neural networks / J. Key in Photogrammetric Engineering & Remote Sensing, PERS, vol 55 n° 9 (september 1989)Permalink