TY - JOUR
T1 - Hyperspectral Unmixing via Nonnegative Matrix Factorization with Handcrafted and Learned Priors
AU - Zhao, Min
AU - Gao, Tiande
AU - Chen, Jie
AU - Chen, Wei
N1 - Publisher Copyright:
© 2004-2012 IEEE.
PY - 2022
Y1 - 2022
N2 - Nowadays, nonnegative matrix factorization (NMF)-based methods have been widely applied to blind spectral unmixing. Introducing proper regularizers to NMF is crucial for mathematically constraining the solutions and physically exploiting spectral and spatial properties of images. Generally, properly handcrafted regularizers and solving the associated complex optimization problem are nontrivial tasks. In our work, we propose an NMF-based unmixing framework which jointly uses a learned regularizer from data and a handcrafted regularizer. To be specific, we plug learned priors of abundances where the associated subproblem can be addressed using various image denoisers, and we consider an $\ell -{2,1}$-norm as an example to illustrate the way of integrating handcrafted regularizers. The proposed framework is flexible and extendable. Both synthetic data and real airborne data are conducted to confirm the effectiveness of our method.
AB - Nowadays, nonnegative matrix factorization (NMF)-based methods have been widely applied to blind spectral unmixing. Introducing proper regularizers to NMF is crucial for mathematically constraining the solutions and physically exploiting spectral and spatial properties of images. Generally, properly handcrafted regularizers and solving the associated complex optimization problem are nontrivial tasks. In our work, we propose an NMF-based unmixing framework which jointly uses a learned regularizer from data and a handcrafted regularizer. To be specific, we plug learned priors of abundances where the associated subproblem can be addressed using various image denoisers, and we consider an $\ell -{2,1}$-norm as an example to illustrate the way of integrating handcrafted regularizers. The proposed framework is flexible and extendable. Both synthetic data and real airborne data are conducted to confirm the effectiveness of our method.
KW - Hyperspectral unmixing
KW - learned priors
KW - nonnegative matrix factorization (NMF)
UR - http://www.scopus.com/inward/record.url?scp=85099593121&partnerID=8YFLogxK
U2 - 10.1109/LGRS.2020.3047481
DO - 10.1109/LGRS.2020.3047481
M3 - 文章
AN - SCOPUS:85099593121
SN - 1545-598X
VL - 19
JO - IEEE Geoscience and Remote Sensing Letters
JF - IEEE Geoscience and Remote Sensing Letters
ER -