Learning Endmember Dynamics in Multitemporal Hyperspectral Data using a State-Space Model Formulation

From LRDE

Abstract

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 tremendous applicative potential and the avent of new satellite sensors with high temporal resolution, multitemporal hyperspectral unmixing is still a relatively underexplored research avenue in the community, compared to standard image unmixing. In this paper, we propose a new framework for multitemporal unmixing and endmember extraction based on a state-space model, and present a proof of concept on simulated data to show how this representation can be used to inform multitemporal unmixing with external prior knowledge, or on the contrary to learn the dynamics of the quantities involved from data using neural network architectures adapted to the identification of dynamical systems.


Bibtex (lrde.bib)

@InProceedings{	  drumetz.20.icassp,
  author	= {Lucas Drumetz and Mauro Dalla Mura and Guillaume Tochon
		  and Ronan Fablet},
  title		= {Learning Endmember Dynamics in Multitemporal Hyperspectral
		  Data using a State-Space Model Formulation},
  booktitle	= {Proceedings of the 45th IEEE International Conference on
		  Acoustics, Speech, and Signal Processing (ICASSP)},
  year		= 2020,
  month		= mai,
  address	= {Barcelona, Spain},
  abstract	= {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 tremendous applicative
		  potential and the avent of new satellite sensors with high
		  temporal resolution, multitemporal hyperspectral unmixing
		  is still a relatively underexplored research avenue in the
		  community, compared to standard image unmixing. In this
		  paper, we propose a new framework for multitemporal
		  unmixing and endmember extraction based on a state-space
		  model, and present a proof of concept on simulated data to
		  show how this representation can be used to inform
		  multitemporal unmixing with external prior knowledge, or on
		  the contrary to learn the dynamics of the quantities
		  involved from data using neural network architectures
		  adapted to the identification of dynamical systems.},
  lrdenote	= {accepted}
}