Descripteur
Termes IGN > mathématiques > statistique mathématique > analyse de données > classification > classification par réseau neuronal
classification par réseau neuronalVoir aussi |
Documents disponibles dans cette catégorie (563)
Ajouter le résultat dans votre panier
Visionner les documents numériques
Affiner la recherche Interroger des sources externes
Etendre la recherche sur niveau(x) vers le bas
Mapping 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)
[article]
Titre : Mapping tropical forest structure in south-eastern Madagascar using remote sensing and artificial neural networks Type de document : Article/Communication Auteurs : J.C. Ingram, Auteur ; T.P. Dawson, Auteur ; R.J. Whittaker, Auteur Année de publication : 2005 Article en page(s) : pp 491 - 507 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse des correspondances
[Termes IGN] analyse multibande
[Termes IGN] biodiversité
[Termes IGN] carte de la végétation
[Termes IGN] classification par réseau neuronal
[Termes IGN] densité
[Termes IGN] forêt tropicale
[Termes IGN] image Landsat-ETM+
[Termes IGN] Madagascar
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] radiance
[Termes IGN] troncRésumé : (Auteur) Tropical forest condition has important implications for biodiversity, climate change and human needs. Structural features of forests can serve as useful indicators of forest condition and have the potential to be assessed with remotely sensed imagery, which can provide quantitative information on forest ecosystems at high temporal and spatial resolutions. Herein, we investigate the utility of remote sensing for assessing, predicting and mapping two important forest structural features, stem density and basal area, in tropical, littoral forests in southeastern Madagascar. We analysed the relationships of basal area and stem density measurements to the Normalised Difference Vegetation Index (NDVI) and radiance measurements in bands 3, 4, 5 and 7 from the Landsat Enhanced Thematic Mapper Plus (ETM+). Strong relationships were identified among all of the individual bands and field based measurements of basal area (p Numéro de notice : A2005-069 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2004.12.001 En ligne : https://doi.org/10.1016/j.rse.2004.12.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27207
in Remote sensing of environment > vol 94 n° 4 (28/02/2005) . - pp 491 - 507[article]Estimation 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)
[article]
Titre : Estimation and monitoring of bare soil/vegetation ratio with SPOT vegetation and HRVIR Type de document : Article/Communication Auteurs : Grégoire Mercier, Auteur ; Fernand Verger, Auteur ; et al., Auteur Année de publication : 2005 Article en page(s) : pp 348 - 354 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] agriculture
[Termes IGN] analyse multiéchelle
[Termes IGN] carte de Kohonen
[Termes IGN] classification par réseau neuronal
[Termes IGN] estimation statistique
[Termes IGN] image à haute résolution
[Termes IGN] image SPOT-HRVIR
[Termes IGN] image SPOT-Végétation
[Termes IGN] occupation du sol
[Termes IGN] pollution des eaux
[Termes IGN] réalité de terrain
[Termes IGN] sol nu
[Termes IGN] surface cultivée
[Termes IGN] surveillance agricole
[Termes IGN] végétationRésumé : (Auteur) Covering soils with vegetation during the fallow and planting seasons is one of the main ways to reduce water pollution, by restricting pollutant fluxes to aquatic systems. The bare soil/vegetation ratio monitoring can be carried out daily with a coarse spatial resolution using SPOT VEGETATION (1 km). Nevertheless, land-cover changes detected at a regional scale with this ratio may be due to winter vegetation cover changes as well as the influence of climatic events. Therefore, observed changes have to be validated from a local-scale analysis with higher spatial resolution data. The aim of this study is to develop a technique that allows high or low variations detected at a regional scale to be assessed from SPOT VEGETATION images with data acquired at a higher scale, SPOT High Resolution Visible and Infrared images in our case. In this study, the link between the images from the two sensors is achieved from the design of an artificial neural network method based on a Kohonen self-organizing map. The originality of this method lies in the use of external knowledge from ground observations and the use of temporal behavior to solve such a change of scale. Results of testing this method by using a potential change map based on the last few years' land-cover observations have shown a good correspondence between the observed and predicted bare soil/vegetation balance with regards to the spatial resolution difference between the two sensors. Numéro de notice : A2005-102 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/IGARSS.2003.1294745 En ligne : https://doi.org/10.1109/IGARSS.2003.1294745 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27240
in IEEE Transactions on geoscience and remote sensing > vol 43 n° 2 (February 2005) . - pp 348 - 354[article]Réservation
Réserver ce documentExemplaires(2)
Code-barres Cote Support Localisation Section Disponibilité 065-05022 RAB Revue Centre de documentation En réserve L003 Disponible 065-05021 RAB Revue Centre de documentation En réserve L003 Disponible Uncertainty 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)
[article]
Titre : Uncertainty and confidence in land cover classification using a hybrid classifier approach Type de document : Article/Communication Auteurs : W. Liu, Auteur ; Sucharita Gopal, Auteur ; Curtis E. Woodcock, Auteur Année de publication : 2004 Article en page(s) : pp 963 - 971 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] Amérique du nord
[Termes IGN] classification hybride
[Termes IGN] classification par arbre de décision
[Termes IGN] classification par réseau neuronal
[Termes IGN] image NOAA-AVHRR
[Termes IGN] incertitude des données
[Termes IGN] occupation du solRésumé : (Auteur) Traditional methods of land cover classification and mapping are limited in providing spatial data on the uncertainty of map labels. In this paper, we present a hybrid classifier approach using Decision Tree (DT) and ARTMAP neural network to providing confidence or uncertainty information via majority voting and other rules. The hybrid classifier is tested with AVHRR data to mapping land cover of North America. The two classifiers (DT and ARTMAP) tend to make predictive errors in different contexts. They show 68% agreement in classifying land cover of North America. A set of rules is developed to assign class labels for pixels where the two classifiers disagree. Levels of confidence in the hybrid classification derived from their individual voting (ARTmAP) and probability (DT) are used to assign confidence. The approach outlined in this paper produces two products a hybrid classification map as well as a confidence map based on the two classification schemes. The hybrid approach seems suitable to tackle a variety of classification problems in remote sensing and may ultimately aid map users in making more informed decisions. Numéro de notice : A2004-309 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.70.8.963 En ligne : https://doi.org/10.14358/PERS.70.8.963 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26836
in Photogrammetric Engineering & Remote Sensing, PERS > vol 70 n° 8 (August 2004) . - pp 963 - 971[article]A split model for extraction of subpixel impervious surface information / Y. Wang in Photogrammetric Engineering & Remote Sensing, PERS, vol 70 n° 7 (July 2004)
[article]
Titre : A split model for extraction of subpixel impervious surface information Type de document : Article/Communication Auteurs : Y. Wang, Auteur ; X. Zhang, Auteur Année de publication : 2004 Article en page(s) : pp 821 - 828 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] banlieue
[Termes IGN] classification par réseau neuronal
[Termes IGN] données multicapteurs
[Termes IGN] image Landsat-TM
[Termes IGN] milieu urbain
[Termes IGN] réseau neuronal artificiel
[Termes IGN] surface imperméable
[Termes IGN] valeur radiométriqueRésumé : (Auteur) This paper introduces a Subpixel Proportional Land cover Information Transformation (SPLIT) model to extract proportions of impervious surfaces in urban and suburban areas. High spatial resolution airborne Digital Multispectral Videography (Dmsv) data provided subpixel information for Landsat TM data. The SPLIT model employed a Modularized Artificial Neural Network (MANN) to integrate multi-sensor remote sensing data and to extract proportions of impervious surfaces and other types of land cover within TM pixels. Through a control unit, the MANN was able to decompose a complex task into multiple subtasks by using a group of sub-networks. The SPLIT model identified spectral relations between TM pixel values and the corresponding DMSV subpixel patterns. The established relationship allows extrapolation of the SPLIT model to the areas beyond DMSV data coverage. We applied five intervals, i.e., 81 percent, to map the subpixel proportions of land cover types. We extrapolated the SPLIT model from training sites that have both TM and DMSV coverage into the entire DuPage County with TM data as the input. The extrapolation received 82.9 percent overall accuracyfor the extracted proportions of urban impervious surface. Numéro de notice : A2004-274 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.70.7.821 En ligne : https://doi.org/10.14358/PERS.70.7.821 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26801
in Photogrammetric Engineering & Remote Sensing, PERS > vol 70 n° 7 (July 2004) . - pp 821 - 828[article]Change detection techniques / Dong Lu in International Journal of Remote Sensing IJRS, vol 25 n° 12 (June 2004)
[article]
Titre : Change detection techniques Type de document : Article/Communication Auteurs : Dong Lu, Auteur ; P. Mausel, Auteur ; et al., Auteur Année de publication : 2004 Article en page(s) : pp 2365 - 2407 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] analyse comparative
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] analyse en composantes principales
[Termes IGN] capteur (télédétection)
[Termes IGN] classification dirigée
[Termes IGN] classification par réseau neuronal
[Termes IGN] détection de changement
[Termes IGN] limite de résolution géométrique
[Termes IGN] seuillage d'image
[Termes IGN] système d'information géographique
[Termes IGN] traitement d'imageRésumé : (Auteur) Timely and accurate change detection of Earth's surface features is extremely important for understanding relationships and interactions between human and natural phenomena in order to promote better decision making. Remote sensing data are primary sources extensively used for change detection in recent decades. Many change detection techniques have been developed. This paper summarizes and reviews these techniques. Previous literature has shown that image differencing, principal component analysis and post-classification comparison are the most common methods used for change detection. In recent years, spectral mixture analysis, artificial neural networks and integration of geographical information system and remote sensing data have become important techniques for change detection applications. Different change detection algorithms have their own merits and no single approach is optimal and applicable to all cases. In practice, different algorithms are often compared to find the best change detection results for a specific application. Research of change detection techniques is still an active topic and new techniques are needed to effectively use the increasingly diverse and complex remotely sensed data available or projected to be soon available from satellite and airborne sensors. This paper is a comprehensive exploration of all the major change detection approaches implemented as found in the literature. Numéro de notice : A2004-223 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/0143116031000139863 En ligne : https://doi.org/10.1080/0143116031000139863 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26750
in International Journal of Remote Sensing IJRS > vol 25 n° 12 (June 2004) . - pp 2365 - 2407[article]Réservation
Réserver ce documentExemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 080-04101 RAB Revue Centre de documentation En réserve L003 Disponible An 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)Permalink