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

  • Lucas Drumetz
  • Guillaume Tochon
  • Jocelyn Chanussot
  • Christian Jutten
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10169)

Abstract

Spectral Unmixing (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 to use. However, the ID can be significantly overestimated in difficult scenarios, and sometimes does not correspond to the desired scale and application dependent number of endmembers. Spurious endmembers are then frequently 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.

Keywords

Hyperspectral images Remote sensing Collaborative sparsity Alternating Direction Method of Multipliers Regularization path Bayesian Information Criterion 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Lucas Drumetz
    • 1
  • Guillaume Tochon
    • 2
  • Jocelyn Chanussot
    • 1
  • Christian Jutten
    • 1
  1. 1.Univ. Grenoble Alpes, CNRS, GIPSA-labGrenobleFrance
  2. 2.EPITA Research and Development Laboratory (LRDE)Le Kremlin-BicêtreFrance

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