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Automated near real-time earth observation level 2 product generation for semantic querying

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Baraldi, A. de and Tiede, Dirk and Sudmanss, Martin and Belgiu, Mariana and Lang, Stefan (2016) Automated near real-time earth observation level 2 product generation for semantic querying. In: GEOBIA 2016 : Solutions and Synergies., 14 September 2016 - 16 September 2016, University of Twente Faculty of Geo-Information and Earth Observation (ITC) .

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Event: GEOBIA 2016 : Solutions and Synergies., 14 September 2016 - 16 September 2016, University of Twente Faculty of Geo-Information and Earth Observation (ITC)
Abstract:Existing Earth observation (EO) content-based image retrieval (CBIR) systems support human-machine interaction through queries by metadata text information, image-wide summary statistics or either image, object or multi-object examples. No semantic CBIR (SCBIR) system in operating mode has ever been developed by the remote sensing (RS) community. At the same time, no EO dataderived Level 2 prototype product has ever been generated systematically at the ground segment, in contrast with the visionary goal of the Global Earth Observation System of Systems (GEOSS) implementation plan for years 2005-2015. Typical EO Level 2 products include: (i) a multi-spectral (MS) image corrected for atmospheric, adjacency and topographic effects, and (ii) a scene classification map (SCM), encompassing cloud and cloud-shadow quality layers. This work presents an original hybrid (combined deductive and inductive) feedback EO image understanding for semantic querying (EO-IU4SQ) system as a proof-of-concept where systematic multi-source EO big data transformation into Level 2 products is accomplished as a pre-condition for SCBIR, in agreement with the object-based image analysis (OBIA) paradigm and the Quality Assurance Framework for Earth Observation (QA4EO) guidelines. In the hybrid EO-IU4SQ system, statistical model-based/bottom-up/inductive/machine learning-from-data algorithms and physical model-based/top-down/deductive/human-to-machine knowledge transfer approaches are combined with feedback loops to take advantage of the complementary features of each and overcome their shortcomings.
Item Type:Conference or Workshop Item (Paper)
Link to this item:https://doi.org/10.3990/2.417
Conference URL:https://www.geobia2016.com/
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