Estimating the Number of Endmembers to Use in Spectral Unmixing of Hyperspectral Data with Collaborative Sparsity

From LRDE

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Abstract

Spectral Umixing (SU) in hyperspectral remote sensing aims at recovering the signatures of the pure materials in the scene (endmembers) and their abundances in each pixel of the image. The usual SU chain does not take spectral variability (SV) into account, and relies on the estimation of the Intrinsic Dimensionality (ID) of the data, related to the number of endmembers (NOE) to use. However, the ID can be significantly overestimated in difficult scenariosand sometimes does not correspond to the desired scale and application dependent NOE. Spurious endmembers are then frequently extracted and included in the model. We propose an algorithm for SU incorporating SV, using collaborative sparsity to discard the least explicative endmembers in the whole image. We compute an algorithmic regularization path for this problem to select the optimal set of endmembers using a statistical criterion. Results on simulated and real data show the interest of the approach.

Documents

Bibtex (lrde.bib)

@InProceedings{	  drumetz.17.lva-ica,
  author	= {Lucas Drumetz and Guillaume Tochon and Jocelyn Chanussot
		  and Christian Jutten},
  title		= {Estimating the Number of Endmembers to Use in Spectral
		  Unmixing of Hyperspectral Data with Collaborative
		  Sparsity},
  booktitle	= {Proceedings of the 13th International Conference on Latent
		  Variable Analysis and Signal Separation (LVA-ICA)},
  year		= 2017,
  address	= {Grenoble, France},
  month		= feb,
  abstract	= {Spectral Umixing (SU) in hyperspectral remote sensing aims
		  at recovering the signatures of the pure materials in the
		  scene (endmembers) and their abundances in each pixel of
		  the image. The usual SU chain does not take spectral
		  variability (SV) into account, and relies on the estimation
		  of the Intrinsic Dimensionality (ID) of the data, related
		  to the number of endmembers (NOE) to use. However, the ID
		  can be significantly overestimated in difficult scenarios,
		  and sometimes does not correspond to the desired scale and
		  application dependent NOE. Spurious endmembers are then
		  frequently extracted and included in the model. We propose
		  an algorithm for SU incorporating SV, using collaborative
		  sparsity to discard the least explicative endmembers in the
		  whole image. We compute an algorithmic regularization path
		  for this problem to select the optimal set of endmembers
		  using a statistical criterion. Results on simulated and
		  real data show the interest of the approach.}
}