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Construction of bulk temperature/salinity from surface temperature and atlas profiles for monitoring water volume variations in the Caspian Sea / Ayoub Moradi (2019)  

Public Titre : Construction of bulk temperature/salinity from surface temperature and atlas profiles for monitoring water volume variations in the Caspian Sea Type de document : Article/Communication Auteurs : Ayoub Moradi, Auteur ; Olivier de Viron, Auteur ; Laurent Métivier , Auteur ; Saeid Homayouni, Auteur Editeur : Téhéran : Kharazmi University Année de publication : 2019 Conférence : CICIS 2019, 4th Conference on Contemporary Issues in Computer Information and Sciences 23/01/2019 25/01/2019 Teheran Iran Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse de sensibilité [Termes IGN] Caspienne, mer [Termes IGN] image NOAA [Termes IGN] montée du niveau de la mer [Termes IGN] salinité [Termes IGN] température de surface de la mer Résumé : (auteur) Unlike the other lakes, the Caspian Sea has regular water level fluctuations caused by variation in temperature and salinity, which is known as thermohaline fluctuations. Vertically variable temperature and salinity data are needed in order to monitor thermohaline fluctuations. These data are regularly recorded for the open seas by remote sensing and in-situ approaches. However, there is no such information for inland water bodies, such as the Caspian Sea. In this research, daily Sea Surface Temperature (SST) from the NOAA satellite, plus long-term mean temperature, and salinity datasets from Atlas 2009 were utilized to construct bulk temperature and salinity in the Caspian Sea. The Atlas vertical profiles are not deep enough in the Caspian Sea; we expanded these data down to a thermocline depth, using a linear fitting. Constructed bulk temperature and salinity data utilized in water density calculations. The results show that thermohaline level fluctuation estimated by constructed bulk data is consisted of what a combination of altimetry and gravimetry system observed in the Caspian Sea. In the absence of necessary data, this method is helpful for bulk temperature and salinity estimations in the Caspian Sea with a satisfactory level of accuracy. The estimated thermohaline has an accuracy of about 93%, under the situation that there was 15% error in the estimation of both bulk temperature and salinity. Numéro de notice : C2019-080 Affiliation des auteurs : UMR IPGP-Géod+Ext (2020- ) Thématique : IMAGERIE Nature : Communication DOI : sans En ligne : https://www.researchgate.net/publication/368243402_Construction_of_Bulk_Temperat [...] Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102859

Object-based hyperspectral classification of urban areas using marker-based hierarchical segmentation / Davood Akbari in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 10 (October 2014)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 80 n° 10 (October 2014) . - pp 963 - 970 Titre : Object-based hyperspectral classification of urban areas using marker-based hierarchical segmentation Type de document : Article/Communication Auteurs : Davood Akbari, Auteur ; Abdolreza Safari, Auteur ; Saeid Homayouni, Auteur Année de publication : 2014 Article en page(s) : pp 963 - 970 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification orientée objet [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] classification spectrale [Termes IGN] image hyperspectrale [Termes IGN] segmentation hiérarchique [Termes IGN] zone urbaine Résumé : (auteur)An effective approach to spectral-spatial classification has been achieved using Hierarchical SEGmentation (HSEG) by Tarabalka et al. (2009 and 2010). Our goal is to improve this approach to the classification of hyperspectral images in urban areas. The first step of our proposed method is to segment the spectral images using a novel marker-based HSEG, method. The spatial features from segmented images are then extracted. Spatial information such as the area, entropy, shape, adjacency, and relation features constitute the components of feature space. Last, using both spectral and spatial features, the image objects are classified by a support vector machine (SVM) classifier. Three image data-sets were used to test this method. The results of our experiment indicate that the main advantage of the proposed method, compared to the previous HSEG-based approach, is that it increases classification accuracy by selecting the appropriate contextual features of different objects. Numéro de notice : A2014-673 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.80.10.963 En ligne : https://doi.org/10.14358/PERS.80.10.963 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75153 [article]