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GEOIRIS : GEOspatial Information Retrieval and Indexing System-content mining, semantics modeling, and complex queries / C.R. Shyu in IEEE Transactions on geoscience and remote sensing, vol 45 n° 4 (April 2007)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 45 n° 4 (April 2007) . - pp 839 - 852 Titre : GEOIRIS : GEOspatial Information Retrieval and Indexing System-content mining, semantics modeling, and complex queries Type de document : Article/Communication Auteurs : C.R. Shyu, Auteur ; M. Klaric, Auteur ; G.J. Scott, Auteur ; et al., Auteur Année de publication : 2007 Article en page(s) : pp 839 - 852 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes IGN] base de données d'images [Termes IGN] exploration de données géographiques [Termes IGN] extraction automatique [Termes IGN] fusion de données multisource [Termes IGN] indexation spatiale [Termes IGN] recherche d'information géographique [Termes IGN] requête spatiale Résumé : (Auteur) Searching for relevant knowledge across heterogeneous geospatial databases requires an extensive knowledge of the semantic meaning of images, a keen eye for visual patterns, and efficient strategies for collecting and analyzing data with minimal human intervention. In this paper, we present our recently developed content-based multimodal Geospatial Information Retrieval and Indexing System (GeoIRIS) which includes automatic feature extraction, visual content mining from large-scale image databases, and high-dimensional database indexing for fast retrieval. Using these underpinnings, we have developed techniques for complex queries that merge information from heterogeneous geospatial databases, retrievals of objects based on shape and visual characteristics, analysis of multiobject relationships for the retrieval of objects in specific spatial configurations, and semantic models to link low-level image features with high-level visual descriptors. GeoIRIS brings this diverse set of technologies together into a coherent system with an aim of allowing image analysts to more rapidly identify relevant imagery. GeoIRIS is able to answer analysts' questions in seconds, such as "given a query image, show me database satellite images that have similar objects and spatial relationship that are within a certain radius of a landmark." Copyright IEEE Numéro de notice : A2007-218 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2006.890579 En ligne : https://doi.org/10.1109/TGRS.2006.890579 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28581 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-07041	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Semantic-sensitive satellite image retrieval / Y. Li in IEEE Transactions on geoscience and remote sensing, vol 45 n° 4 (April 2007)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 45 n° 4 (April 2007) . - pp 853 - 860 Titre : Semantic-sensitive satellite image retrieval Type de document : Article/Communication Auteurs : Y. Li, Auteur ; T.R. Bretschneider, Auteur Année de publication : 2007 Article en page(s) : pp 853 - 860 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes IGN] base de données d'images [Termes IGN] image satellite [Termes IGN] inférence sémantique [Termes IGN] recherche d'information [Termes IGN] réseau bayesien Résumé : (Auteur) Content-based image-retrieval techniques based on query scenes are a powerful means for exploration and mining of large remote sensing image databases. However, the gap between low-level unsupervised extracted features in content-based retrieval and the high-level semantic concepts of user queries limits the performance. Therefore, this paper proposes a specialized approach using a context-sensitive Bayesian network for semantic inference of segmented scenes. The regions' remote sensing related semantic concepts are inferred in a multistage process based on their spectral and textural characteristics as well as the semantics of adjacent regions. During the actual retrieval, the semantics are employed for the extraction of candidate scenes which are evaluated and ranked in a consecutive step. The approach was implemented and compared with a different strategy that utilizes the extracted features from the imagery directly to infer the semantics. In summary, the developed system achieved higher precision and recall rates using the same training data Copyright IEEE Numéro de notice : A2007-219 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2007.892008 En ligne : https://doi.org/10.1109/TGRS.2007.892008 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28582 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-07041	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Detecting man-made structures and changes in satellite imagery with a content-based information retrieval system built on Self-Organizing Maps / Matthieu Molinier in IEEE Transactions on geoscience and remote sensing, vol 45 n° 4 (April 2007)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 45 n° 4 (April 2007) . - pp 861 - 874 Titre : Detecting man-made structures and changes in satellite imagery with a content-based information retrieval system built on Self-Organizing Maps Type de document : Article/Communication Auteurs : Matthieu Molinier, Auteur ; Jorma Laaksonen, Auteur ; Tuomas Häme, Auteur Année de publication : 2007 Article en page(s) : pp 861 - 874 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes IGN] base de données d'images [Termes IGN] carte de Kohonen [Termes IGN] détection de changement [Termes IGN] détection du bâti [Termes IGN] image à très haute résolution [Termes IGN] image optique [Termes IGN] image satellite [Termes IGN] recherche d'information Résumé : (Auteur) The increasing amount and resolution of satellite sensors demand new techniques for browsing remote sensing image archives. Content-based querying allows an efficient retrieval of images based on the information they contain, rather than their acquisition date or geographical extent. Self-organizing maps (SOMs) have been successfully applied in the PicSOM system to content-based image retrieval in databases of conventional images. In this paper, we investigate and extend the potential of PicSOM for the analysis of remote sensing data. We propose methods for detecting man-made structures, as well as supervised and unsupervised change detection, based on the same framework. In this paper, a database was artificially created by splitting each satellite image to be analyzed into small images. After training the PicSOM on this imagelet database, both interactive and off-line queries were made to detect man-made structures, as well as changes between two very high resolution images from different years. Experimental results were both evaluated quantitatively and discussed qualitatively, and suggest that this new approach is suitable for analyzing very high resolution optical satellite imagery. Possible applications of this work include interactive detection of man-made structures or supervised monitoring of sensitive sites. Copyright IEEE Numéro de notice : A2007-220 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2006.890580 En ligne : https://doi.org/10.1109/TGRS.2006.890580 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28583 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-07041	RAB	Revue	Centre de documentation	En réserve L003	Disponible