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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 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)
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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 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 Existe-t-il une "mémoire" de l'espace en Roumanie post-communiste ? / Simona Niculescu in Revue internationale de géomatique, vol 13 n° 4 (décembre 2003 – février 2004)
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Titre : Existe-t-il une "mémoire" de l'espace en Roumanie post-communiste ? Type de document : Article/Communication Auteurs : Simona Niculescu, Auteur Année de publication : 2003 Article en page(s) : pp 493 - 509 Note générale : Bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse comparative
[Termes IGN] analyse diachronique
[Termes IGN] cadastre ancien
[Termes IGN] histoire
[Termes IGN] image Landsat-TM
[Termes IGN] image SPOT
[Termes IGN] morphologie mathématique
[Termes IGN] paysage agricole
[Termes IGN] Roumanie
[Termes IGN] structure agraire
[Termes IGN] structure spatialeRésumé : (Auteur) en concevant la morphologie des paysages agricoles comme une expression des différentes formes spatiales, on se propose, dans cet article, de donner des explications géographiques diachrono-comparatives sur toute une série de " générations " des systèmes agraires roumains à partir des cadastres de 1856, d'images satellites Landsat de 1989 (du régime communiste), de 1997 (du régime post-communiste) et d'images SPOT de 1998 (du régime post-communiste). On s'y interroge aussi sur le poids du passé dans ce sens que toute génération humaine exerce son action sur un espace différencié, " lourd " d'héritage. Il s'agit de l'héritage de la mémoire de l'espace, une mémoire enregistrée par l'inscription spatiale des actions de transformation et d'aménagement de la terre. Il faut comprendre cet espace reçu comme le produit à la fois de processus naturels et des actions des générations antérieures. Numéro de notice : A2003-398 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3166/rig.13.479-492 En ligne : https://doi.org/10.3166/rig.13.479-492 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26478
in Revue internationale de géomatique > vol 13 n° 4 (décembre 2003 – février 2004) . - pp 493 - 509[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 047-03041 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 Mapping urban areas by fusing multiple sources of coarse resolution remotely sensed data / A.M. Schneider in Photogrammetric Engineering & Remote Sensing, PERS, vol 69 n° 12 (December 2003)
PermalinkMapping forest degradation in the Eastern Amazon SPOT 4 through spectral mixture models / Cristiano B. Souza in Remote sensing of environment, vol 87 n° 4 (15/11/2003)
PermalinkImprovements in land use mapping for irrigated agriculture from satellite sensor data using a multi-stage maximum likelihood classification / I.A. El-Magd in International Journal of Remote Sensing IJRS, vol 24 n° 21 (November 2003)
PermalinkMangrove research and coastal ecosystem studies with SPOT-4 HRVIR and TERRA ASTER in the Arabian Gulf / Hideo Saito in International Journal of Remote Sensing IJRS, vol 24 n° 21 (November 2003)
PermalinkStudy of urban spatial patterns from SPOT panchromatic imagery using textural analysis / Qian Zhang in International Journal of Remote Sensing IJRS, vol 24 n° 21 (November 2003)
PermalinkAutomated change detection for updates of digital map databases / T. Knudsen in Photogrammetric Engineering & Remote Sensing, PERS, vol 69 n° 11 (November 2003)
PermalinkA credit assignment approach to fusing classifiers of multiseason hyperspectral imagery / C. Bachmann in IEEE Transactions on geoscience and remote sensing, vol 41 n° 11 (November 2003)
PermalinkLinear features extraction in rain forest context from interferometric SAR images by fusion of coherence and amplitude information / V.P. Onana in IEEE Transactions on geoscience and remote sensing, vol 41 n° 11 (November 2003)
PermalinkA Markov random field approach to spatio-temporal contextual image classification / F. Melgani in IEEE Transactions on geoscience and remote sensing, vol 41 n° 11 (November 2003)
PermalinkA new maximum-likelihood joint segmentation technique for multitemporal SAR and multiband optical images / P. Lombardo 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)
PermalinkA combined fuzzy pixel-based and object-based approach for classification of high-resolution multispectral data over urban areas / A.K. Shackelford in IEEE Transactions on geoscience and remote sensing, vol 41 n° 10 (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)
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)
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)
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)
PermalinkMapping of the tropical forest cover of insular Southeast Asia from SPOT-4 Vegetation images / Hans-Jürgen Stibig in International Journal of Remote Sensing IJRS, vol 24 n° 18 (September 2003)
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