24 citations · 76 across the 22 of their papers we have counts for
8 papers · 1 filter
MultiHU-TD: Multifeature Hyperspectral Unmixing Based on Tensor Decomposition
Mohamad Jouni, Mauro Dalla Mura, Lucas Drumetz +1
Hyperspectral unmixing allows representing mixed pixels as a set of pure materials weighted by their abundances. Spectral features alone are often insufficient, so it is common to…
Learning Sentinel-2 reflectance dynamics for data-driven assimilation and forecasting
Anthony Frion, Lucas Drumetz, Guillaume Tochon +2
Over the last few years, massive amounts of satellite multispectral and hyperspectral images covering the Earth's surface have been made publicly available for scientific purpose,…
Learning Sentinel-2 Spectral Dynamics for Long-Run Predictions Using Residual Neural Networks
Joaquim Estopinan, Guillaume Tochon, Lucas Drumetz
Making the most of multispectral image time-series is a promising but still relatively under-explored research direction because of the complexity of jointly analyzing spatial, spe…
Spectral Variability in Hyperspectral Data Unmixing: A Comprehensive Review
Ricardo Augusto Borsoi, Tales Imbiriba, José Carlos Moreira Bermudez +6
The spectral signatures of the materials contained in hyperspectral images, also called endmembers (EM), can be significantly affected by variations in atmospheric, illumination or…
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 (…
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…