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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)
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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 vol 29 n° 6 - 01/12/2003 - Applications de la télédétection en hydrologie (Bulletin de Canadian journal of remote sensing) / R. Granger
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Titre : vol 29 n° 6 - 01/12/2003 - Applications de la télédétection en hydrologie Type de document : Périodique Auteurs : R. Granger, Éditeur scientifique ; A. Pietroniro, Éditeur scientifique ; Canadian remote sensing society, Auteur Année de publication : 2003 Langues : Anglais (eng) Français (fre) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] hydrologie
[Termes IGN] télédétectionNuméro de notice : 117-0306 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Numéro de périodique Permalink : https://documentation.ensg.eu/index.php?lvl=bulletin_display&id=5021 [n° ou bulletin]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 117-03061 SL Revue Centre de documentation Revues en salle Disponible Combining metric aerial photography and near-infrared videography to define within-field soil sampling frameworks / G.G. Wright in Geocarto international, vol 18 n° 4 (December 2003 - February 2004)
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Titre : Combining metric aerial photography and near-infrared videography to define within-field soil sampling frameworks Type de document : Article/Communication Auteurs : G.G. Wright, Auteur ; K.B. Matthews, Auteur Année de publication : 2003 Article en page(s) : pp 13 - 20 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] classification
[Termes IGN] échantillonnage d'image
[Termes IGN] émulsion fausse couleur
[Termes IGN] ERDAS Imagine
[Termes IGN] fusion de données
[Termes IGN] image vidéo
[Termes IGN] photo-interprétation
[Termes IGN] photographie aérienne
[Termes IGN] rayonnement proche infrarouge
[Termes IGN] terrainRésumé : (Auteur) This paper investigates the combination of metric aerial photography and near-infrared (NIR) videography data to improve the design of field-survey sampling frameworks. Spatial data collection can contribute up to 80% of the cost of deploying a Geographic Information System (GIS) based Decision Support System (DSS). The use of remotely sensed information, field survey using differential Global Positioning System (dGPS) and geostatistical interpolation methods maximises data quality for a given rate of sampling.
Medium-format colour aerial photography and NIR videography were orthorectified to the national map base and mosaiced using ERDAS Imagine. The green and red layers of the aerial photography were combined with the NIR videography to form a false-colour composite image. Two sampling strategies were tested. The first stratified sampling on a per field basis, creating four points per hectare, randomly located within each field. The second strategy used the remotely sensed information to identify within-field variability classes for each field, using red-green difference or normalised difference vegetation index (NDVI) models. These variability classes were used as a sub-stratification framework with each class sampled at the same rate of 4 per hectare. For both strategies the sample points were generated within ESRI ArcView and were located in the field using dGPS. Maps of stone content were created using geostatistical methods and validated against samples collected on a 100 metre grid. It was concluded that combining the two image sources to create a within-field stratification framework improved the precision of the results obtained from field-survey.Numéro de notice : A2003-376 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106040308542285 Date de publication en ligne : 02/01/2008 En ligne : https://doi.org/10.1080/10106040308542285 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26456
in Geocarto international > vol 18 n° 4 (December 2003 - February 2004) . - pp 13 - 20[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-03041 RAB Revue Centre de documentation En réserve L003 Disponible Fast SAR image restoration, segmentation, and detection of high-reflectance regions / E. Bratsolis in IEEE Transactions on geoscience and remote sensing, vol 41 n° 12 (December 2003)
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Titre : Fast SAR image restoration, segmentation, and detection of high-reflectance regions Type de document : Article/Communication Auteurs : E. Bratsolis, Auteur ; M. Sigelle, Auteur Année de publication : 2003 Article en page(s) : pp 2890 - 2899 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] champ aléatoire de Markov
[Termes IGN] chatoiement
[Termes IGN] classification
[Termes IGN] filtre numérique
[Termes IGN] histogramme
[Termes IGN] image ERS-SAR
[Termes IGN] itération
[Termes IGN] réflectance
[Termes IGN] restauration d'image
[Termes IGN] segmentation d'imageRésumé : (Auteur) An iterative filter that can be used for speckle reduction and restoration of synthetic aperture radar (SAR) images is presented here. This method can be considered as a first step in the extraction of other important information. The second step is the detection of high-reflectance regions and continues with the segmentation of the total image. We have worked in three-look simulated and real European Remote Sensing 1 satellite amplitude images. The iterative filter is based on a membrane model Markov random field approximation optimized by a synchronous local iterative method. The final form of restoration gives a total sum-preserving regularization for the pixel values of our image. The high-reflectance regions are defined as the brightest regions of the restored image. After the separation of this extreme class, we give a fast segmentation method using the histogram of the restored image. Numéro de notice : A2003-383 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2003.817222 En ligne : https://doi.org/10.1109/TGRS.2003.817222 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26463
in IEEE Transactions on geoscience and remote sensing > vol 41 n° 12 (December 2003) . - pp 2890 - 2899[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-03121 RAB Revue Centre de documentation En réserve L003 Disponible Knowledge discovery from soil maps using inductive learning / F. Qi in International journal of geographical information science IJGIS, vol 17 n° 8 (december 2003)
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Titre : Knowledge discovery from soil maps using inductive learning Type de document : Article/Communication Auteurs : F. Qi, Auteur ; A - Xing Zhu, Auteur Année de publication : 2003 Article en page(s) : pp 771 - 795 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] analyse comparative
[Termes IGN] apprentissage dirigé
[Termes IGN] arbre de décision
[Termes IGN] carte pédologique
[Termes IGN] cartographie géologique
[Termes IGN] découverte de connaissances
[Termes IGN] échantillonnage d'image
[Termes IGN] filtrage du bruit
[Termes IGN] histogramme
[Termes IGN] intelligence artificielle
[Termes IGN] réseau neuronal artificiel
[Termes IGN] restauration d'imageRésumé : (Auteur) This paper develops a knowledge discovery procedure for extracting knowledge of soil-landscape models from a soil map. It has broad relevance to knowledge discovery from other natural resource maps. The procedure consists of four major steps: data preparation, data preprocessing, pattern extraction, and knowledge consolidation. In order to recover true expert knowledge from the error-prone soil maps, our study pays specific attention to the reduction of representation noise in soil maps. The data preprocessing step has exhibited an important role in obtaining greater accuracy. A specific method for sampling pixels based on modes of environmental histograms has proven to be effective in terms of reducing noise and constructing representative sample sets. Three inductive learning algorithms, the See5 decision tree algorithm, Naïve Bayes, and artificial neural network, are investigated for a comparison concerning learning accuracy and result comprehensibility. See5 proves to be an accurate method and produces the most comprehensible results, which are consistent with the rules (expert knowledge) used in producing the soil map. The incorporation of spatial information into the knowledge discovery process is found not only to improve the accuracy of the extracted knowledge, but also to add to the explicitness and extensiveness of the extracted soil-landscape model. Numéro de notice : A2003-299 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/13658810310001596049 En ligne : https://doi.org/10.1080/13658810310001596049 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22595
in International journal of geographical information science IJGIS > vol 17 n° 8 (december 2003) . - pp 771 - 795[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-03081 RAB Revue Centre de documentation En réserve L003 Disponible vol 65 n° 6 - November 2003 (Bulletin de Graphical models)
PermalinkStatistical and operational performance assessment of multitemporal SAR image filtering / Emmanuel Trouvé in IEEE Transactions on geoscience and remote sensing, vol 41 n° 11 (November 2003)
PermalinkStrategies for integrating information from multiple resolutions into land-use/land-cover classification routines / D.M. Chen in Photogrammetric Engineering & Remote Sensing, PERS, vol 69 n° 11 (November 2003)
PermalinkBayesian classification by data augmentation / B. Regguzoni in International Journal of Remote Sensing IJRS, vol 24 n° 20 (October 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)
PermalinkDétermination de classes de relief à l'aide de données ERS1 sur des bassins versants tropicaux de Guyane / Marc Lointier in Bulletin [Société Française de Photogrammétrie et Télédétection], n° 172 (Octobre 2003)
Permalinkn° 172 - Octobre 2003 - Hydrosystèmes et télédétection à haute résolution (Bulletin de Bulletin [Société Française de Photogrammétrie et Télédétection]) / Société française de photogrammétrie et de télédétection
PermalinkA Markov random field-based approach to decision-level fusion for remote sensing image classification / Ryuei Nishii in IEEE Transactions on geoscience and remote sensing, vol 41 n° 10 (October 2003)
PermalinkPath processing and block adjustment with RadarSat-1 SAR images / Thierry Toutin in IEEE Transactions on geoscience and remote sensing, vol 41 n° 10 (October 2003)
Permalinkvol 29 n° 5 - 01/10/2003 - La télédétection LIDAR de la structure de la forêt et du Terrain (Bulletin de Canadian journal of remote sensing) / Michael A. Wulder
PermalinkICEAGE: interactive clustering and exploration of large and high-dimensional geodata / D. Guo in Geoinformatica, vol 7 n° 3 (September - November 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)
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)
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)
Permalinkn° 171 - Juillet 2003 (Bulletin de Bulletin [Société Française de Photogrammétrie et Télédétection]) / Société française de photogrammétrie et de télédétection
Permalinkvol 65 n° 5 - July 2003 - International conference of shape modeling (SMI) 2002, [actes], Banff, Canada, May 2002 (Bulletin de Graphical models) / B. Wyvill
Permalinkvol 65 n° 4 - July 2003 - Pacific graphics (PG 2002), [actes], Beijing, 9 - 11 October 2002 (Bulletin de Graphical models) / S. Hu
Permalinkvol 29 n° 3 - 01/06/2003 - Lidar (Bulletin de Canadian journal of remote sensing) / Richard A. Fournier
PermalinkRough and fuzzy geographical data integration / K. Oukbir in International journal of geographical information science IJGIS, vol 17 n° 3 (may 2003)
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