TY - GEN
T1 - A new Bayesian unmixing algorithm for hyperspectral images mitigating endmember variability
AU - Halimi, Abderrahim
AU - Dobigeon, Nicolas
AU - Tourneret, Jean-Yves
AU - Honeine, Paul
PY - 2015/8/6
Y1 - 2015/8/6
N2 - This paper presents an unsupervised Bayesian algorithm for hyperspectral image unmixing accounting for endmember variability. Each image pixel is modeled by a linear combination of random endmembers to take into account endmember variability in the image. The coefficients of this linear combination (referred to as abundances) allow the proportions of each material (endmembers) to be quantified in the image pixel. An additive noise is also considered in the proposed model generalizing the normal compositional model. The proposed Bayesian algorithm exploits spatial correlations between adjacent pixels of the image and provides spectral information by achieving a spectral unmixing. It estimates both the mean and the covariance matrix of each endmember in the image. A spatial classification is also obtained based on the estimated abundances. Simulations conducted with synthetic and real data show the potential of the proposed model and the unmixing performance for the analysis of hyperspectral images.
AB - This paper presents an unsupervised Bayesian algorithm for hyperspectral image unmixing accounting for endmember variability. Each image pixel is modeled by a linear combination of random endmembers to take into account endmember variability in the image. The coefficients of this linear combination (referred to as abundances) allow the proportions of each material (endmembers) to be quantified in the image pixel. An additive noise is also considered in the proposed model generalizing the normal compositional model. The proposed Bayesian algorithm exploits spatial correlations between adjacent pixels of the image and provides spectral information by achieving a spectral unmixing. It estimates both the mean and the covariance matrix of each endmember in the image. A spatial classification is also obtained based on the estimated abundances. Simulations conducted with synthetic and real data show the potential of the proposed model and the unmixing performance for the analysis of hyperspectral images.
KW - endmember variability
KW - Hamiltonian Monte-Carlo
KW - Hyperspectral imagery
KW - image classification
UR - http://www.scopus.com/inward/record.url?scp=84946079942&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2015.7178415
DO - 10.1109/ICASSP.2015.7178415
M3 - Conference contribution
AN - SCOPUS:84946079942
T3 - International Conference on Acoustics, Speech and Signal Processing (ICASSP)
SP - 2469
EP - 2473
BT - 2015 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
PB - IEEE
T2 - 40th IEEE International Conference on Acoustics, Speech and Signal Processing 2015
Y2 - 19 April 2015 through 24 April 2015
ER -