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Training 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)
[article]
Titre : Training a neural network with a canopy reflectance model to estimate crop leaf area index Type de document : Article/Communication Auteurs : F. Mark Danson, Auteur ; C.S. Rowland, Auteur Année de publication : 2003 Article en page(s) : pp 4891 - 4905 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] betterave sucrière
[Termes IGN] classification par réseau neuronal
[Termes IGN] indice de végétation
[Termes IGN] Leaf Area Index
[Termes IGN] neurone artificiel
[Termes IGN] réflectance végétaleRésumé : (Auteur) This paper outlines the strategies available for estimating the biophysical properties of crop canopies from remotely sensed data. Spectral reflectance and biophysical data were obtained over 132 plots of sugar beet (Beta vulgaris L.) and in the first part of the paper the strength of the relationships between vegetation indices (VI) and leaf area index (LAI) are examined. In the second part, an approach is tested in which a canopy reflectance model is used to generate simulated spectra for a wide range of biophysical conditions and these data are used to train an artificial neural network (ANN). The advantage of the second approach is that a priori knowledge of the measurement conditions including soil reflectance, canopy architecture and solar position can be included explicitly in the modelling. The results show that the estimation of sugar beet LAI using a trained neural network is more reliable than the use of VI and has the potential to replace the use of VI for operational applications. The use of a priori data on the variation in soil spectral reflectance gave rise to a small increase in LAI estimation accuracy. Numéro de notice : A2003-315 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/0143116031000070319 En ligne : https://doi.org/10.1080/0143116031000070319 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22611
in International Journal of Remote Sensing IJRS > vol 24 n° 23 (December 2003) . - pp 4891 - 4905[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 080-03231 RAB Revue Centre de documentation En réserve L003 Exclu du prêt A 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)
[article]
Titre : A cognitive pyramid for contextual classification of remote sensing images Type de document : Article/Communication Auteurs : E. Binaghi, Auteur ; I. Gallo, Auteur ; M. Pepe, Auteur Année de publication : 2003 Article en page(s) : pp 2906 - 2922 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification contextuelle
[Termes IGN] classification dirigée
[Termes IGN] classification par réseau neuronal
[Termes IGN] extraction automatique
[Termes IGN] image aérienne
[Termes IGN] image panchromatique
[Termes IGN] image satellite
[Termes IGN] Perceptron multicouche
[Termes IGN] reconnaissance d'objets
[Termes IGN] résolution multipleRésumé : (Auteur) Many cases of remote sensing classification present complicated patterns that cannot he identified on the basis of spectral data alone, but require contextual methods that base class discrimination on the spatial relationships between the individual pixel and local and global configurations of neighboring pixels. However, the use of contextual classification is still limited by critical issues, such as complexity and problem dependency. We propose here a contextual classification strategy for object recognition in remote sensing images in an attempt to solve recognition tasks operatively. The salient characteristics of the strategy are the definition of a multiresolution feature extraction procedure exploiting human perception and the use of soft neural classification based on the multilayer perceptron model. Three experiments were conducted to evaluate the performance of the methodology, one in an easily controlled domain using synthetic images, the other two in real domains involving builtup pattern recognition in panchromatic aerial photographs and high-resolution satellite images. Numéro de notice : A2003-385 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2003.815409 En ligne : https://doi.org/10.1109/TGRS.2003.815409 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26465
in IEEE Transactions on geoscience and remote sensing > vol 41 n° 12 (December 2003) . - pp 2906 - 2922[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-03121 RAB Revue Centre de documentation En réserve L003 Disponible Data fusion and feature extraction in the wavelet domain / Magnus Orn Ulfarsson in International Journal of Remote Sensing IJRS, vol 24 n° 20 (October 2003)
[article]
Titre : Data fusion and feature extraction in the wavelet domain Type de document : Article/Communication Auteurs : Magnus Orn Ulfarsson, Auteur ; Jon Atli Benediktsson, Auteur ; Johannes R. Sveinsson, Auteur Année de publication : 2003 Article en page(s) : pp 3933 - 3945 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] classification par réseau neuronal
[Termes IGN] extraction automatique
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] fusion de données
[Termes IGN] transformation en ondelettesRésumé : (Auteur) This paper concentrates on data fusion, feature extraction, feature selection and neural network classification for multi-source remote sensing and geographical data. The considered feature extraction method is based on the discrete wavelet transformation (DWT). The original data are transformed using DWT and then a feature selection mechanism is applied to select features from the full feature set in the wavelet domain. The feature selection mechanism is a binary genetic algorithm which selects the best features to be used in a neural network classification. In experiments on two datasets, the proposed data fusion and feature extraction method performed well in terms of overall accuracies as compared to results obtained with other wellknown feature extraction methods. Numéro de notice : A2003-284 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/0143116031000103790 En ligne : https://doi.org/10.1080/0143116031000103790 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22579
in International Journal of Remote Sensing IJRS > vol 24 n° 20 (October 2003) . - pp 3933 - 3945[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 080-03201 RAB Revue Centre de documentation En réserve L003 Exclu du prêt A 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)
[article]
Titre : A neural adaptive model for feature extraction and recognition in high resolution remote sensing imagery Type de document : Article/Communication Auteurs : E. Binaghi, Auteur ; I. Gallo, Auteur ; M. Pepe, Auteur Année de publication : 2003 Article en page(s) : pp 3947 - 3959 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] appariement de formes
[Termes IGN] classification dirigée
[Termes IGN] classification par réseau neuronal
[Termes IGN] extraction automatique
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] filtrage numérique d'image
[Termes IGN] image à haute résolution
[Termes IGN] Perceptron multicouche
[Termes IGN] reconnaissance de formes
[Termes IGN] système expert
[Termes IGN] variation d'échelleRésumé : (Auteur) Contextual classification methods, which require the extraction of complex spatial information over a range of scales, from fine details in local areas to large features that extend across the image, are necessary in many remote sensing image classification studies. This work presents a supervised adaptive object recognition model which integrates scale-space filtering techniques for feature extraction within a Multilayer Perceptron neural network and the back-propagation learning task of the search of the most adequate filter parameters. The experimental evaluation of the method has been conducted in an easily controlled domain using synthetic imagery, and in the real domain coping with object recognition in high-resolution remote sensing imagery. To investigate whether the strategy can be considered an alternative to conventional procedures the results were compared with those obtained by a well known contextual classification scheme. Numéro de notice : A2003-285 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/0143116031000103808 En ligne : https://doi.org/10.1080/0143116031000103808 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22580
in International Journal of Remote Sensing IJRS > vol 24 n° 20 (October 2003) . - pp 3947 - 3959[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 080-03201 RAB Revue Centre de documentation En réserve L003 Exclu du prêt Increasing 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)
[article]
Titre : Increasing the spatial resolution of agricultural land cover maps using a Hopfield neural network Type de document : Article/Communication Auteurs : A.J. Tatem, Auteur ; H.G. Lewis, Auteur ; P.M. Atkinson, Auteur ; M.S. Nixon, Auteur Année de publication : 2003 Article en page(s) : pp 647 - 672 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] carte agricole
[Termes IGN] carte d'occupation du sol
[Termes IGN] classification par réseau neuronal
[Termes IGN] erreur moyenne quadratique
[Termes IGN] Grèce
[Termes IGN] image Landsat-TM
[Termes IGN] image satellite
[Termes IGN] incertitude géométrique
[Termes IGN] limite de résolution géométrique
[Termes IGN] occupation du sol
[Termes IGN] précision infrapixellaireRésumé : (Auteur) Land cover class composition of remotely sensed image pixels can be estimated using soft classification techniques increasingly available in many GIS packages. However, their output provides no indication of how such classes are distributed spatially within the instantaneous field of view represented by the pixel. Techniques that attempt to provide an improved spatial representation of land cover have been developed, but not tested on the difficult task of mapping from real satellite imagery. The authors investigated the use of a Hopfield neural network technique to map the spatial distributions of classes reliably using information of pixel composition determined from soft classification previously. The approach involved designing the energy function to produce a 'best guess' prediction of the spatial distribution of class components in each pixel. In previous studies, the authors described the application of the technique to target identification, pattern prediction and land cover mapping at the subpixel scale, but only for simulated imagery. We now show how the approach can be applied to Landsat Thematic Mapper (TM) agriculture imagery to derive accurate estimates of land cover and reduce the uncertainty inherent in such imagery. The technique was applied to Landsat TM imagery of smallscale agriculture in Greece and largescale agriculture near Leicester, UK. The resultant maps provided an accurate and improved representation of the land covers studied, with RMS errors for the Landsat imagery of the order of 0.1 in the new fine resolution map recorded. The results showed that the neural network represents a simple efficient tool for mapping land cover from operational satellite sensor imagery and can deliver requisite results and improvements over traditional techniques for the GIS analysis of pratical remotly sensed imagery at the sub pixel scale. Numéro de notice : A2003-258 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/1365881031000135519 En ligne : https://doi.org/10.1080/1365881031000135519 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22553
in International journal of geographical information science IJGIS > vol 17 n° 7 (october 2003) . - pp 647 - 672[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-03071 RAB Revue Centre de documentation En réserve L003 Disponible 079-03072 RAB Revue Centre de documentation En réserve L003 Disponible Multitemporal/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