Talk by Mihai Datcu
(CNAM, Amphitheatre George Friedmann, 10:30 – 12:00, March 9, 2018)
The volume and variety of valuable Earth Observation (EO) images as well as non-EO related data is rapidly growing. The open free data access becomes widespread and has an enormous scientific and socio-economic relevance. EO images are acquired by sensors on satellite, suborbital or airborne platforms. They extend the observation beyond the visual information, gathering physical parameters of the observed scenes in a broad electromagnetic spectrum. The sensed information depends largely on the imaging geometry, orbit, illumination and other specific parameters of the space instruments. Typical EO systems can be classified into optical or radar instruments. During the last years, both types of sensors deliver widely different images, and both have made considerable progress in spatial and radiometric resolution, image acquisition strategies, and data rates.
Therefore, the domain of EO Data Science spans from mathematical models for the satellite orbit, the physics of electromagnetic propagation and scattering, signal processing, machine learning, AI and knowledge representation. The new specific EO methods to Data Analytics and Data Mining are designed to leverage advances in physical parameters extraction and leads to AI paradigms where learning is a critical non-traditional element joining the overall EO system parameters to achieve the optimal information extraction. This involves the inclusion of machine or deep learning and reasoning tools in the EO data processing chains together with supplementary external information to analyze target characteristics, to perform classification, to search for similarities or extract semantic entities. Information-theoretic approaches are the fundament for modeling the end-2-end system behavior and analyze its performance and bounds.