![Learning Endmember Dynamics In Multitemporal Hyperspectral Data Using A State-Space Model Formulation](/assets/initial/large/0.jpg)
Already purchased this program?
Login to View
This video program is a part of the Premium package:
Learning Endmember Dynamics In Multitemporal Hyperspectral Data Using A State-Space Model Formulation
- IEEE MemberUS $11.00
- Society MemberUS $0.00
- IEEE Student MemberUS $11.00
- Non-IEEE MemberUS $15.00
Learning Endmember Dynamics In Multitemporal Hyperspectral Data Using A State-Space Model Formulation
Hyperspectral image unmixing is an inverse problem aiming at recovering the spectral signatures of pure materials of interest (called endmembers) and estimating their proportions (called abundances) in every pixel of the image. However, in spite of a trem
Hyperspectral image unmixing is an inverse problem aiming at recovering the spectral signatures of pure materials of interest (called endmembers) and estimating their proportions (called abundances) in every pixel of the image. However, in spite of a trem