Centre de documentation
scientifique

Dépouillements

Artificial neural networks for mapping regional-scale upland vegetation from high spatial resolution imagery / H. Mills in International Journal of Remote Sensing IJRS, vol 27 n° 11 (June 2006)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 27 n° 11 (June 2006) . - pp 2177 - 2195 Titre : Artificial neural networks for mapping regional-scale upland vegetation from high spatial resolution imagery Type de document : Article/Communication Auteurs : H. Mills, Auteur ; M.E. Cutler, Auteur ; David Fairbairn, Auteur Année de publication : 2006 Article en page(s) : pp 2177 - 2195 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] carte de la végétation [Termes IGN] classification par réseau neuronal [Termes IGN] données de terrain [Termes IGN] image à résolution métrique [Termes IGN] image Ikonos [Termes IGN] montagne [Termes IGN] Perceptron multicouche [Termes IGN] Royaume-Uni Résumé : (Auteur) Upland vegetation represents an important resource that requires frequent monitoring. However, the heterogeneous nature of upland vegetation and lack of ground data require classification techniques that have a high degree of generalization ability. This study investigates the use of artificial neural networks as a means of mapping upland vegetation from remotely sensed data. First, the optimum size of support to map upland vegetation was estimated as being less than 4 m, which suggested that soft classification techniques and high spatial resolution IKONOS imagery were required. The use of high spatial resolution imagery for regional-scale areas has introduced new challenges to the remote sensing community, such as using limited ground data and mapping land-cover dynamics and variation over large areas. This work then investigated the utility of artificial neural networks (ANN) for regional-scale upland vegetation from IKONOS imagery using limited ground data and to map unseen data from remote geographical locations. A Multiple Layer Perceptron was trained with pixels from an IKONOS image using early stopping; however, despite high classification accuracies when calculated for pixels from an area where training pixels were extracted, the networks did not produce high accuracies when applied to unseen data from a remote area. Copyright Taylor & Francis. Numéro de notice : A2006-299 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160500396501 En ligne : https://doi.org/10.1080/01431160500396501 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28026 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
080-06061	RAB	Revue	Centre de documentation	En réserve L003	Exclu du prêt

Localized soft classification for super-resolution mapping of the shoreline / Aidy M. Muslim in International Journal of Remote Sensing IJRS, vol 27 n° 11 (June 2006)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 27 n° 11 (June 2006) . - pp 2271 - 2285 Titre : Localized soft classification for super-resolution mapping of the shoreline Type de document : Article/Communication Auteurs : Aidy M. Muslim, Auteur ; Giles M. Foody, Auteur ; P.M. Atkinson, Auteur Année de publication : 2006 Article en page(s) : pp 2271 - 2285 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse infrapixellaire [Termes IGN] carte topographique [Termes IGN] histogramme [Termes IGN] image Ikonos [Termes IGN] Malaisie [Termes IGN] trait de côte Résumé : (Auteur) The Malaysian shoreline is dynamic and constantly changing in location. Although the shoreline may be mapped accurately from fine spatial resolution imagery, this is an impractical approach for use over large areas. An alternative approach using coarse spatial resolution satellite sensor imagery is to fit a shoreline boundary at sub-pixel scale. This paper evaluates the use of soft classification and super-resolution mapping techniques to accurately map the shoreline. A localized soft classification approach was used to provide an accurate prediction of the thematic composition of each image pixel. This involves the use of training statistics derived locally rather than globally in the classification. Using the derived class proportion information the shoreline boundary was determined within the pixels using super-resolution techniques. Results show that by using a localized approach in the prediction of the pixel's thematic class composition, the accuracy of shoreline prediction was increased. Notably, the use of the localized approach resulted in the shoreline with an rms error of Numéro de notice : A2006-300 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160500396741 En ligne : https://doi.org/10.1080/01431160500396741 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28027 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
080-06061	RAB	Revue	Centre de documentation	En réserve L003	Exclu du prêt

SeaWIFS discrimination of harmful algal bloom evolution / P.I. Miller in International Journal of Remote Sensing IJRS, vol 27 n° 11 (June 2006)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 27 n° 11 (June 2006) . - pp 2287 - 2301 Titre : SeaWIFS discrimination of harmful algal bloom evolution Type de document : Article/Communication Auteurs : P.I. Miller, Auteur ; J.D. Shutler, Auteur ; G.F. Moore, Auteur ; S.B. Groom, Auteur Année de publication : 2006 Article en page(s) : pp 2287 - 2301 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] algue [Termes IGN] analyse diachronique [Termes IGN] analyse multivariée [Termes IGN] Baltique, mer [Termes IGN] couleur de l'océan [Termes IGN] image Seawifs [Termes IGN] Manche (mer) [Termes IGN] Nord, mer du [Termes IGN] série temporelle [Termes IGN] température de surface de la mer Résumé : (Auteur) The discrimination of harmful algal blooms (HABs) from space would benefit both the capability of early warning systems and the study of environmental factors affecting the initiation of blooms. Unfortunately, there are no published techniques using global monitoring satellite sensors to distinguish the resulting subtle changes in ocean colour, so in situ sampling is needed to identify the species in any observed bloom. This paper investigates multivariate classification as an objective means to discriminate harmful and harmless algae and monitor their dynamics using ocean colour data derived from satellite sensors. The classifier is trained and tested using Sea-viewing Wide Field-of-view Sensor (SeaWiFS) data, though the method is designed to be generic for other sensors. Time-series results are presented using the new HAB likelihood index and suggest that SeaWiFS has some capability for observing the dynamic evolution of harmful blooms of Karenia mikimotoi, Chattonella verruculosa and cyanobacteria. Further, a multi-band spatial subtraction algorithm is described to automate the identification of bloom regions and improve the accuracy in discriminating HABs. Copyright Taylor & Francis Numéro de notice : A2006-301 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160500396816 En ligne : https://doi.org/10.1080/01431160500396816 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28028 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
080-06061	RAB	Revue	Centre de documentation	En réserve L003	Exclu du prêt

Monitoring the maximum turbidity zone and detecting fine-scale turbidity features in the Gironde estuary using high spatial resolution satellite sensor (SPOT HRV, Landsat ETM+) data / D. Doxaran in International Journal of Remote Sensing IJRS, vol 27 n° 11 (June 2006)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 27 n° 11 (June 2006) . - pp 2303 - 2321 Titre : Monitoring the maximum turbidity zone and detecting fine-scale turbidity features in the Gironde estuary using high spatial resolution satellite sensor (SPOT HRV, Landsat ETM+) data Type de document : Article/Communication Auteurs : D. Doxaran, Auteur ; P. Castaing, Auteur ; S.J. Lavender, Auteur Année de publication : 2006 Article en page(s) : pp 2303 - 2321 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] estuaire [Termes IGN] Gironde (estuaire) [Termes IGN] image Landsat-ETM+ [Termes IGN] image SPOT-HRV [Termes IGN] surveillance hydrologique [Termes IGN] turbidité des eaux Résumé : (Auteur) This study concerns the quantification of suspended particulate matter in the highly turbid estuarine waters of the Gironde, France, from high spatial resolution remotely sensed data, SPOT (Satellite Pour l'Observation de la Terre) High Resolution Visible (HRV), Landsat Enhanced Thematic Mapper Plus (ETM+). The methodology is based on calibration relationships established between the remote sensing reflectance (Rrs) signal and suspended particulate matter concentration (SPM), from in situ optical measurements. These relationships are valid in the long term as Rrs ratios between near-infrared (NIR) and visible wavebands are relatively independent of the particle grain-size and mineralogy. Consequently, they can be applied to satellite images, even if no simultaneous in situ measurements are carried out concurrently with the satellite overpass. Selected satellite sensor data are corrected for atmospheric effects using radiative transfer code, then converted into surface water SPM concentrations according to the established calibration relationships. Resulting SPM maps are presented for different river flow and tidal conditions. These maps are used to locate the maximum turbidity zone and observe its tidal and seasonal movements. The high spatial resolution of SPOT HRV and Landsat ETM+ satellite sensor data also shows detailed turbidity features in the estuary, resulting from re-suspension phenomena over banks and turbulent currents. Copyright Taylor & Francis Numéro de notice : A2006-302 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160500396865 En ligne : https://doi.org/10.1080/01431160500396865 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28029 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
080-06061	RAB	Revue	Centre de documentation	En réserve L003	Exclu du prêt