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Premium member Presentation Transcript Information Mining for SSEMihai Datcu (DLR Oberpfaffenhofen) Andrea Colapicchioni (ACS Rome) Klaus Seidel (ETH Zurich): Information Mining for SSE Mihai Datcu (DLR Oberpfaffenhofen) Andrea Colapicchioni (ACS Rome) Klaus Seidel (ETH Zurich)Information Mining for SSEIIM for TerraSA-X payload ground segmentIIM for MERIS archivesIIM for users of EO data: Information Mining for SSE IIM for TerraSA-X payload ground segment IIM for MERIS archives IIM for users of EO dataSlide3: 25m ERS 10m Radarsat 2m Pol E-SAR 0.5m Aerosensing MotivationsSlide4: perspective view Where is the information? SRTM/X-SAR DEM 30m AEROSENSING Buildings 0.5mE-SAR Dresden exploration of image content : E-SAR Dresden exploration of image content Scene elements structure: bridge: Scene elements structure: bridgeBuilding reconstruction: Building reconstructionSummary: Summary Slide9: Image time series: time scales t(day) % pixels 0 Car,Plane apparition Snow, Rain Clouds 100 1 Factory smoke Ice, Wind effects 10 365 Crop evolutions Seasonal effects BuildingIMAGE INFORMATION MINING PROJECTS in ESA TRP: SUMMARY KIM - Knowledge driven Information Mining in remote sensing image archives 2001-2002: DLR, ETH Zurich, ACS Rome, NERSC Bergen KES - EO domain specific Knowledge Enabled Services 2002-2004: ACS Rome, DLR, ETH Zurich KIMV - KIM Validation for EO archived data exploitation support 2003-2004: ACS Rome, DLR, ETH Zurich KEO - Knowledge-centric Earth Observation 2004-2006: ACS Rome, DLR, ETH Zurich,… IMAGE INFORMATION MINING PROJECTS in ESA TRPKIM - Knowledge driven Information Mining in remote sensing image archives: Objectives: create a prototype system of a next generation architecture to help the users to gather relevant information rapidly, manage and add value to the huge amounts of historical and newly acquired satellite data-sets Data: ERS, Landsat, Ikonos, (data volume ~50 scenes) Evaluators and users: ESRIN, EUSC, NERSC, DLR Output: prototype system KIM - Knowledge driven Information Mining in remote sensing image archives KES - EO domain specific Knowledge Enabled Services : KES - EO domain specific Knowledge Enabled Services Objectives: design and demonstrate a scalable prototype applicable to a number of fields supporting image information mining and other related user interactions, knowledge acquisition and sharing within user communities, multi - type data handling (image, text, GIS), up to semantic interactions and knowledge communication Data: ERS, Landsat, HR SAR and optical (data volume ~200 scenes) Evaluators and users: ESRIN, EUSC, DLR Output: prototype system River Domain Ontology Semantics Labels Features ImagesKIMV - KIM Validation for EO archived data exploitation support: KIMV - KIM Validation for EO archived data exploitation support Objectives: to implement, test and evaluate a quasi-operational environment for simple access also as MASS Services to enhanced image selection functions (image selection by combinations of standard spatio – temporal - parameters and image information content queries) Data: MERIS, ERS, Landsat, SPOT… (data volume ~5 000 scenes) Evaluators and users: ESRIN, EUSC, CNES, DLR, universities, industry (total ~15 users) Output: pre-operational systemKEO - Knowledge-centric Earth Observation : KEO - Knowledge-centric Earth Observation Objectives: a prototype system and environment to foster the enlargement of EO data utilisation, and in particular of the large archives of multi-mission and multi-temporal images, provide a better support to research, value-adding industry, service providers and EO user communities, like scientific investigations, risk and disaster management, or in the GMES programme Data: TerraSAR, ENVISAT, ERS, Landsat, SPOT… (data volume ~100GB/day….) Evaluators and users: ESRIN, DLR, EUSC, CNES, universities, industry. Output: operational systemKEO Architecture: KEO Architecture Slide16: Proposed Architecture TerraSAR-X Payload GSSlide17: http://isis.dlr.de/mining/ Data-Information-Knowledge: Data-Information-Knowledge -applicable to a number of fields -knowledge acquisition and sharing within user communities -multi - type data handling -knowledge communication River Domain Ontology Semantics Labels Features ImagesSSE including KIM and DIMS (TerraSAR GS): SSE including KIM and DIMS (TerraSAR GS)Information mining for SSE: Information mining for SSE Data Information Knowledge Understanding Data management Data mining Information mining &KDD Data & Scene understandingSlide21: Data Information Knowledge Understanding Data management Data mining Information mining &KDD Data & Scene understanding SENSORS USERSADVANCED COMMUNICATION CONCEPT: ADVANCED COMMUNICATION CONCEPT The European Image Information Mining Coordination Group IIMCG: Members: ASI, CNES, CNR, DLR, EC-IST, ESA, ETHZ, EUSC Chair: Sergio D‘Elia, ESA/ESRIN IIMCG focus: research and technological activities for automated and user centred extraction of information from EO images and image archives in support to content understanding Events: ESA-EUSC 2002: Joint seminar on Knowledge driven Information Management in Earth Observation data, ESRIN, Frascati, December 5-6, 2002, Madrid, March 17-18, 2004 ESA-EUSC 2005: Theory and Applications of Knowledge driven Image Information Mining with focus on Earth Observation, ESRIN, Frascati,October, 2005 The European Image Information Mining Coordination Group IIMCG You do not have the permission to view this presentation. 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