Welcome on the web site of the ASTRAL project (ANR-21-ASTR-0011)

The general objective of this project is to develop approaches for analyzing and interpreting scenes from SAR data that differ from existing ones by including the physics of SAR within the learning technique.
It is built according to the 3 following axes. A first axis is dedicated to the representation of knowledge in deep networks, in particular by integrating the physics of SAR acquisition (complex vector data, parameters of interest in Hermitian positive definite covariance matrices), and acquisition geometry (taking into account the influence of geometry, in particular the relative positions of sensors in interferometry or tomography and the spatial relationships between objects in the scene). A second axis is devoted to learning strategies in the case of a small number of labeled data (simulation of training bases, self-supervision techniques) and to the confidence that can be associated to the results. The last axis is devoted to the development of applications for the characterization and monitoring of the urban environment in SAR imagery: the classification of urban scenes, their three-dimensional reconstruction and the detection of changes.

This project is funded by an ANR-ASTRID funding provided by « Agence Innovation de Défense – AID »  (DGA).