260 citations · 657 across the 30 of their papers we have counts for
6 papers · 1 filter
Learning Endmember Dynamics in Multitemporal Hyperspectral Data Using a State-Space Model Formulation
Lucas Drumetz, Mauro Dalla Mura, Guillaume Tochon +1
Hyperspectral image unmixing is an inverse problem aiming at recovering the spectral signatures of pure materials of interest (called endmembers) and estimating their proportions (…
End-to-end learning of energy-based representations for irregularly-sampled signals and images
Ronan Fablet, Lucas Drumetz, François Rousseau
For numerous domains, including for instance earth observation, medical imaging, astrophysics,..., available image and signal datasets often involve irregular space-time sampling p…
Learning Latent Dynamics for Partially-Observed Chaotic Systems
Said Ouala, Duong Nguyen, Lucas Drumetz +5
This paper addresses the data-driven identification of latent dynamical representations of partially-observed systems, i.e., dynamical systems for which some components are never o…
Spectral Variability Aware Blind Hyperspectral Image Unmixing Based on Convex Geometry
Lucas Drumetz, Jocelyn Chanussot, Christian Jutten +2
Hyperspectral image unmixing has proven to be a useful technique to interpret hyperspectral data, and is a prolific research topic in the community. Most of the approaches used to…
EM-like Learning Chaotic Dynamics from Noisy and Partial Observations
Duong Nguyen, Said Ouala, Lucas Drumetz +1
The identification of the governing equations of chaotic dynamical systems from data has recently emerged as a hot topic. While the seminal work by Brunton et al. reported proof-of…
Spectral Unmixing: A Derivation of the Extended Linear Mixing Model from the Hapke Model
Lucas Drumetz, Jocelyn Chanussot, Christian Jutten
In hyperspectral imaging, spectral unmixing aims at decomposing the image into a set of reference spectral signatures corresponding to the materials present in the observed scene a…