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Kernel based re-classification of Earth observation data for fine scale habitat mapping / Iphigenia Keramitsoglou in Journal for nature conservation, vol 13 n° 2-3 (July 2005)  

Public [article]  inJournal for nature conservation > vol 13 n° 2-3 (July 2005) . - pp 91 - 99 Titre : Kernel based re-classification of Earth observation data for fine scale habitat mapping Type de document : Article/Communication Auteurs : Iphigenia Keramitsoglou, Auteur ; Charalambos Kontoes, Auteur ; Nicolas Sifakis, Auteur ; Jonathan Mitchley, Auteur ; Panteleimon Xofis, Auteur Année de publication : 2005 Article en page(s) : pp 91 - 99 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] classification pixellaire [Termes IGN] habitat (nature) [Termes IGN] image Ikonos [Termes IGN] image Quickbird [Termes IGN] site Natura 2000 Résumé : (auteur) State-of-the-art Earth observation systems that provide very high spatial resolution imagery have given ecologists a powerful tool to directly identify species, habitats and other ecological units. At the same time, there is an urgent need for harmonised tools and methods to evaluate status and trends in European habitats. Towards that goal, the current work explores the applicability and transferability of an advanced pixel window classifier applied on very high spatial resolution satellite imagery for fine scale habitat mapping. The algorithm is tested on images of varying spatial resolutions acquired over test sites designated for the NATURA 2000 list located in different biogeographical zones. Algorithm application to Quickbird and IKONOS images gives encouraging results, regarding both the overall accuracies and the level of class hierarchy (habitats) identified. Numéro de notice : A2005-008 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/FORET Nature : Article DOI : 10.1016/j.jnc.2005.02.004 En ligne : http://dx.doi.org/10.1016/j.jnc.2005.02.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81066 [article]

Radial basis function neural networks classification using very high spatial resolution satellite imagery: an application to the habitat area of Lake Kerkini (Greece) / Iphigenia Keramitsoglou in International Journal of Remote Sensing IJRS, vol 26 n° 9 (May 2005)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 26 n° 9 (May 2005) . - pp 1861 - 1880 Titre : Radial basis function neural networks classification using very high spatial resolution satellite imagery: an application to the habitat area of Lake Kerkini (Greece) Type de document : Article/Communication Auteurs : Iphigenia Keramitsoglou, Auteur ; H. Sarimveis, Auteur ; et al., Auteur Année de publication : 2005 Article en page(s) : pp 1861 - 1880 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] analyse texturale [Termes IGN] bande spectrale [Termes IGN] classificateur paramétrique [Termes IGN] classification par maximum de vraisemblance [Termes IGN] classification par réseau neuronal [Termes IGN] fonction de base radiale [Termes IGN] Grèce [Termes IGN] image à très haute résolution [Termes IGN] lac Résumé : (Auteur) This study investigates the potential of applying the radial basis function (RBF) neural network architecture for the classification of multispectral very high spatial resolution satellite images into 13 classes of various scales. For the development of the RBF classifiers, the innovative fuzzy means training algorithm is utilized, which is based on a fuzzy partition of the input space. The method requires only a short amount of time to select both the structure and the parameters of the RBF classifier. The new technique was applied to the area of Lake Kerkini, which is a wetland of great ecological value, located in northern Greece. Eleven experiments were carried out in total in order to investigate the performance of the classifier using different input parameters (spectral and textural) as well as different window sizes and neural network complexities. For comparison purposes the same satellite scene was classified using the maximum likelihood (MLH) classification with the same set of training samples. Overall, the neural network classifiers outperformed the MLH classification by 10-17%, reaching a maximum overall accuracy of 78%. Analysis showed that the selection of input parameters is vital for the success of the classifiers. On the other hand, the incorporation of textural analysis and/or modification of the window size do not affect the performance substantially. Numéro de notice : A2005-255 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160512331326594 En ligne : https://doi.org/10.1080/01431160512331326594 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27391 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
080-05091	RAB	Revue	Centre de documentation	En réserve L003	Exclu du prêt

Mapping micro-urban heat islands using NOAA/AVHRR images and CORINE Land Cover : an application to coastal of Greece / M. Stathopoulou in International Journal of Remote Sensing IJRS, vol 25 n° 12 (June 2004)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 25 n° 12 (June 2004) . - pp 2301 - 2316 Titre : Mapping micro-urban heat islands using NOAA/AVHRR images and CORINE Land Cover : an application to coastal of Greece Type de document : Article/Communication Auteurs : M. Stathopoulou, Auteur ; C. Cartalis, Auteur ; Iphigenia Keramitsoglou, Auteur Année de publication : 2004 Article en page(s) : pp 2301 - 2316 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] cartographie thématique [Termes IGN] Corine Land Cover [Termes IGN] emissivité [Termes IGN] Grèce [Termes IGN] image NOAA-AVHRR [Termes IGN] milieu urbain [Termes IGN] rayonnement infrarouge thermique [Termes IGN] température au sol Résumé : (Auteur) Land Surface Temperature (LST) is a significant parameter for identifying micro-climatic changes, their spatial distribution and intensities in relation to the urban environment. In this study, LST is estimated using thermal infrared data as acquired by the Advanced Very High Resolution Radiometer (AVHRR) instrument onboard the National Oceanic and Atmospheric Administration (NOAA) satellite and by using a split window algorithm that is adjusted to account for the region of Greece. For the assignment of the surface emissivity, a new methodology based on the Coordination of Information on the Environment (CORINE) Land Cover database for Greece is used. The algorithm is applied to a night-time series of NOAA/AVHRR images of Greece in order to produce surface temperature maps of an enhanced spatial resolution of 250m for the cities of Thessaloniki, Patra, Volos and Iraklion, which are the most significant harbour cities of Greece. Results indicate the presence of urban heat islands (UHIs) in each case study, with highest temperatures detected along the coastal zone of the harbour cities resulting from denser urban fabric and road network as well as intense human activity. Numéro de notice : A2004-221 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160310001618725 En ligne : https://doi.org/10.1080/01431160310001618725 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26748 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
080-04101	RAB	Revue	Centre de documentation	En réserve L003	Disponible