Hyperspectral Imagery

Nonlinear spectral unmixing, endmember extraction and variability analysis, and intrinsic dimension estimation. Development of Bayesian models for nonlinear mixing effects in spectral data.


Related Publications

Learning Non-Local Spatial Correlations for Bayesian Unmixing
A. Halimi et al.
IEEE TIP, 2019
Multidimensional Sparse Bayesian Learning for Unmixing
A. Halimi et al.
IEEE TCI, 2019
Correntropy-Based ADMM for Robust Unmixing
A. Halimi et al.
IEEE TGRS, 2017
Fast Unmixing of Hyperspectral Images
A. Halimi et al.
IEEE TCI, 2017
Endmember Variability in Hyperspectral Unmixing
A. Halimi et al.
IEEE TIP, 2016
Intrinsic Dimension Estimation
A. Halimi et al.
IEEE TGRS, 2016
Unsupervised Unmixing of Hyperspectral Images
A. Halimi et al.
IEEE TIP, 2015
PPNM: Nonlinear Mixing Model
A. Halimi et al.
IEEE TIP, 2012
GBM: Generalized Bilinear Model
A. Halimi et al.
IEEE TGRS, 2011
← Back to Research