Section: New Results

Data-based identification of characteristic scales and automated modeling

Participants: N. Brodu, G. S. Phartiyal, D. Singh, H. Yahia.

Low-rankness transfer for denoising Sentinel-1 SAR images. Published in the 9th International Symposium on Signal, Image, Video and Communications ISIVC, Rabat, 2018, HAL.

A mixed spectral and spatial Convolutional Neural Network for Land Cover Classification using SAR and Optical data. Published in EGU General Assembly, Vienna, 2018, HAL.

Inferrence of causal states from time series for empirical modeling at prescribed scales. The goal of this research is to recover physical systems internal states from data and build a model of their evolution. Clustering together data with the same causal effets leads to consistent internal states: each measured data inferred to match the same state has by definition the same consequence, hence the same functional role. The theory behind this is well established, with major steps in the 80's by Jim Crutchfield. This leads to computational mechanics and epsilon-machines in the discrete case. The theory has however always suffers from computationability issues and it is very hard to apply in practice on large systems and real data. N. Brodu has made (unpublished) progress in 2018 in this theory, showing links between epsilon-machines and stochastic processes in the continuous case. The goal is to form a new class of algorithms drawing on the continuous representation, which would not suffer from the explicit discretization steps needed by current algorithms. N. Brodu has initiated a collaboration with Jim Crutchfield in 2017 and hope to further enhance that collaboration in 2019. This plan was presented to the reviewers during the team evaluation and deemed to be of high priority.