Big SAR Data Science: Physics based Machine Learning and Artificial Intelligence

Radar imaging, particularly Synthetic Aperture Radar (SAR) are pioneer technologies in the field of Computational Sensing and Imaging. The challenges of the image formation principles, high data volume and very high acquisition rate stimulated the elaborations of techniques witch today are ubiquitous. SAR technologies have immensely evolved, the state of the art sensors deliver widely different images, and have made considerable progress in spatial and radiometric resolution, target acquisition strategies, imaging modes, or geographical coverage and data rates. Generally imaging sensors generate an isomorphic representation of the observed scene. This is not the case for SAR, the observations are a doppelganger of the scattered field, an indirect signature of the imaged object. This positions the load of SAR image understanding, and the outmost challenge of Big SAR Data Science, as new and particular challenge of Machine Learning (ML) and Artificial Intelligence (AI). The presentation reviews and analyses the new approaches of SAR imaging leveraging the recent advances in physical process based ML and AI methods and signal processing, and leading to Computational Imaging paradigms where intelligence is the analytical component of the end-to-end sensor and Data Science chain design. A particular focus is on the scientific methods of Deep Learning and an information theoretical model of the SAR information extraction process.