TY - JOUR
T1 - Adaptive Markov random fields for joint unmixing and segmentation of hyperspectral images
AU - Eches, Olivier
AU - Benediktsson, Jón Atli
AU - Dobigeon, Nicolas
AU - Tourneret, Jean Yves
PY - 2013
Y1 - 2013
N2 - Linear spectral unmixing is a challenging problem in hyperspectral imaging that consists of decomposing an observed pixel into a linear combination of pure spectra (or endmembers) with their corresponding proportions (or abundances). Endmember extraction algorithms can be employed for recovering the spectral signatures while abundances are estimated using an inversion step. Recent works have shown that exploiting spatial dependencies between image pixels can improve spectral unmixing. Markov random fields (MRF) are classically used to model these spatial correlations and partition the image into multiple classes with homogeneous abundances. This paper proposes to define the MRF sites using similarity regions. These regions are built using a self-complementary area filter that stems from the morphological theory. This kind of filter divides the original image into flat zones where the underlying pixels have the same spectral values. Once the MRF has been clearly established, a hierarchical Bayesian algorithm is proposed to estimate the abundances, the class labels, the noise variance, and the corresponding hyperparameters. A hybrid Gibbs sampler is constructed to generate samples according to the corresponding posterior distribution of the unknown parameters and hyperparameters. Simulations conducted on synthetic and real AVIRIS data demonstrate the good performance of the algorithm.
AB - Linear spectral unmixing is a challenging problem in hyperspectral imaging that consists of decomposing an observed pixel into a linear combination of pure spectra (or endmembers) with their corresponding proportions (or abundances). Endmember extraction algorithms can be employed for recovering the spectral signatures while abundances are estimated using an inversion step. Recent works have shown that exploiting spatial dependencies between image pixels can improve spectral unmixing. Markov random fields (MRF) are classically used to model these spatial correlations and partition the image into multiple classes with homogeneous abundances. This paper proposes to define the MRF sites using similarity regions. These regions are built using a self-complementary area filter that stems from the morphological theory. This kind of filter divides the original image into flat zones where the underlying pixels have the same spectral values. Once the MRF has been clearly established, a hierarchical Bayesian algorithm is proposed to estimate the abundances, the class labels, the noise variance, and the corresponding hyperparameters. A hybrid Gibbs sampler is constructed to generate samples according to the corresponding posterior distribution of the unknown parameters and hyperparameters. Simulations conducted on synthetic and real AVIRIS data demonstrate the good performance of the algorithm.
KW - Hyperspectral images
KW - Markov random field (MRF)
KW - morphological filter
KW - segmentation
KW - spectral unmixing
UR - http://www.scopus.com/inward/record.url?scp=84871655932&partnerID=8YFLogxK
U2 - 10.1109/TIP.2012.2204270
DO - 10.1109/TIP.2012.2204270
M3 - Article
AN - SCOPUS:84871655932
SN - 1057-7149
VL - 22
SP - 5
EP - 16
JO - IEEE Transactions on Image Processing
JF - IEEE Transactions on Image Processing
IS - 1
M1 - 6216411
ER -