Home / Research /

Hyperspectral unmixing


Our activities deal with spectral unmixing of hyperspectral data.

For these models...

under these scenarios...

and these contexts

using these methods...

The considered applicative context mainly deals with remote sensing. However, the proposed algorithms/methodologies have been also applied for

A review paper on spectral unmixing has been published in IEEE J. Sel. Topics Topics Applied Earth Observations and Remote Sensing in 2012:

A tutorial paper on nonlinear unmixing has been published in IEEE Signal Processing Magazine in 2014:

A preliminary study to validate nonlinear unmixing models in vegetated areas has been published in the IEEE J. Sel. Topics Appl. Earth Observations Remote Sensing. More information is available here:

MCMC algorithm for unsupervised LMM-based unmixing: here.

New! A beta version of a Matlab GUI for the Bayesian Linear Unmixing (BLU) algorithm:

MCMC algorithm for supervised LMM-based unmixing: here.

MCMC algorithm for supervised NCM-based unmixing: here.

MCMC algorithm for unsupervised NCM-based unmixing: here.

Gradient algorithm for supervised LMM-based unmixing: here.

Robust NMF for unsupervised RLMM-based unmixing: here.

ADMM for unsupervised PLMM-based unmixing: here.

Online PLMM-based unmixing of multi-temporal images: here.

Gradient algorithm for supervised GBM-based unmixing: here.

Gradient algorithm for supervised PPNMM-based unmixing: here.

Detection of non-linear mixtures using PPNMM-based unmixing: here.

Detection of non-linear mixtures using subspace estimation: here.




sitemeter stats