Eliciting the evolution of spatiotemporal objects - Seleme Mota, Joice
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One of the main challenges in the information extraction provided by remote sensing images is to model and to represent geographical objects that have their properties changed along the time. This thesis proposes a novel approach based on Case Based Reasoning (CBR) for describing how geospatial objects identified in remote sensing imagery evolve. Given a set of multi-temporal images, the CBR techniques and the expert knowledge in a certain application domain, the approach describes the trajectories of evolution objects. Therefore, it is possible to describe how the objects evolve by retrieving…mehr

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Produktbeschreibung
One of the main challenges in the information extraction provided by remote sensing images is to model and to represent geographical objects that have their properties changed along the time. This thesis proposes a novel approach based on Case Based Reasoning (CBR) for describing how geospatial objects identified in remote sensing imagery evolve. Given a set of multi-temporal images, the CBR techniques and the expert knowledge in a certain application domain, the approach describes the trajectories of evolution objects. Therefore, it is possible to describe how the objects evolve by retrieving their complete evolving history. The proposed method is tested for two case studies, in the Brazilian Amazonia Forest, for describing the evolution of deforestation patterns, which can enable a better understanding of land use changes in these regions.
Autorenporträt
Professor in Information System at the Catarinense Federal Institute. Bachelor in Computer Science, master degree in Computer Science at UFRGS and Ph.D. in Applied Computing at the National Institute For Space Research - INPE. Has experience in Computer Science, Software Engineering, GIS, and Image Processing.