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Classification 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)
[article]
Titre : Classification of wheat crop with multi-temporal images: performance of maximum likelihood and artificial neural networks Type de document : Article/Communication Auteurs : C.S. Murthy, Auteur ; P.V. Raju, Auteur ; K.V.S. Badrinath, Auteur Année de publication : 2003 Article en page(s) : pp 4871 - 4890 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] agriculture
[Termes IGN] analyse diachronique
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] classification par réseau neuronal
[Termes IGN] cultures
[Termes IGN] image multitemporelle
[Termes IGN] neurone artificielRésumé : (Auteur) the need for multi-temporal data analysis for delineation of wheat crop has been demonstrated first. It is found that Maximum Likelihood Classification (MLC) with the composite data of multi-temporal images is limited by the problem of large null set containing crop pixels. Therefore, for effective classification of multi-temporal images, two approaches are evaluated : (1) MLC with different strategies-sequential MLC (s_MLC), MLC with Principal Components (pca_MLC) and iterative MLC (i_MLC) ; and (2) Artificial Neural Network (ANN) with back-propagation method. These classifiers were applied on multi-temporal Indian Remote-Sending satellite (IRS)-1 B images to classify wheat crop in two areas of India, one with dominant wheat and the other with less dominant wheat cultivation. Among the three strategies of MLC, i_MLC has resulted in relatively better classification of wheat. However, the correctness of labelling of wheat pixels. The performance of ANN is proved to be better, in both the situations of dominant wheat and less dominant wheat cultivation. Numéro de notice : A2003-314 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/0143116031000070490 En ligne : https://doi.org/10.1080/0143116031000070490 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22610
in International Journal of Remote Sensing IJRS > vol 24 n° 23 (December 2003) . - pp 4871 - 4890[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 080-03231 RAB Revue Centre de documentation En réserve L003 Exclu du prêt 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 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 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 Simulation 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)PermalinkWater quality retrievals from combined Landsat TM data and ERS-2 SAR data in the Gulf of Finland / Y. Zhang in IEEE Transactions on geoscience and remote sensing, vol 41 n° 3 (March 2003)PermalinkLe boosting : une méthode de classification non paramétrique / Michel Arnaud in Revue internationale de géomatique, vol 12 n° 4 (décembre 2002 – février 2003)PermalinkCalibration of stochastic cellular automata: the application to rural-urban land conversions / F. Wu in International journal of geographical information science IJGIS, vol 16 n° 8 (december 2002)PermalinkNeural-network-based cellular automata for simulating multiple land use changes using GIS / X. Li in International journal of geographical information science IJGIS, vol 16 n° 4 (june 2002)PermalinkArtificial neural networks as a method of spatial interpolation for digital elevation models / D.A. Merwin in Cartography and Geographic Information Science, vol 29 n° 2 (April 2002)PermalinkECAI 2002, 15th European Conference on Artificial Intelligence, July 21-26, Lyon, France / Frank Van Harmelen (2002)PermalinkGénéralisation et représentation multiple / Anne Ruas (2002)PermalinkRetrieval of sea water optically active parameters from hyperspectral data by means of generalized radial basis function neural networks / P. Cipollini in IEEE Transactions on geoscience and remote sensing, vol 39 n° 7 (July 2001)PermalinkArtificial neural networks as a tool for spatial interpolation / J.P. Rigol in International journal of geographical information science IJGIS, vol 15 n° 4 (june 2001)Permalink