TY - JOUR
T1 - Unmixing K-Gaussians with application to hyperspectral imaging
AU - Woodbridge, Yonatan
AU - Okun, Uri
AU - Elidan, Gal
AU - Wiesel, Ami
N1 - Publisher Copyright:
© 1980-2012 IEEE.
PY - 2019/9
Y1 - 2019/9
N2 - In this paper, we consider the parameter estimation of K-Gaussians, given convex combinations of their realizations. In the remote sensing literature, this setting is known as the normal compositional model (NCM) and has shown promising gains in modeling hyperspectral images. Current NCM parameter estimation techniques are based on Bayesian methodology and are computationally slow and sensitive to their prior assumptions. Here, we introduce a deterministic variant of the NCM, named DNCM, which assumes that the unknown mixing coefficients are nonrandom. This leads to a standard Gaussian model with a simple estimation procedure, which we denote by K-Gaussians. Its iterations are provided in closed form and do not require any sampling schemes or simplifying structural assumptions. We illustrate the performance advantages of K-Gaussians using synthetic and real images, in terms of accuracy and computational costs in comparison to state of the art. We also demonstrate the use of our algorithm in hyperspectral target detection on a real image with known targets.
AB - In this paper, we consider the parameter estimation of K-Gaussians, given convex combinations of their realizations. In the remote sensing literature, this setting is known as the normal compositional model (NCM) and has shown promising gains in modeling hyperspectral images. Current NCM parameter estimation techniques are based on Bayesian methodology and are computationally slow and sensitive to their prior assumptions. Here, we introduce a deterministic variant of the NCM, named DNCM, which assumes that the unknown mixing coefficients are nonrandom. This leads to a standard Gaussian model with a simple estimation procedure, which we denote by K-Gaussians. Its iterations are provided in closed form and do not require any sampling schemes or simplifying structural assumptions. We illustrate the performance advantages of K-Gaussians using synthetic and real images, in terms of accuracy and computational costs in comparison to state of the art. We also demonstrate the use of our algorithm in hyperspectral target detection on a real image with known targets.
KW - Hyperspectral unmixing
KW - normal compositional model (NCM)
UR - http://www.scopus.com/inward/record.url?scp=85071919530&partnerID=8YFLogxK
U2 - 10.1109/TGRS.2019.2912818
DO - 10.1109/TGRS.2019.2912818
M3 - ???researchoutput.researchoutputtypes.contributiontojournal.article???
AN - SCOPUS:85071919530
SN - 0196-2892
VL - 57
SP - 7281
EP - 7293
JO - IEEE Transactions on Geoscience and Remote Sensing
JF - IEEE Transactions on Geoscience and Remote Sensing
IS - 9
M1 - 8715787
ER -