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Communication Dans Un Congrès Année : 2015

Blind hyperspectral unmixing using an Extended Linear Mixing Model to address spectral variability

Lucas Drumetz
Simon Henrot
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Jocelyn Chanussot

Résumé

The Linear Mixing Model is often used to perform Hyperspec-tral Unmixing because of its simplicity, but it assumes that a single spectral signature can be completely representative of an endmember. However, in many scenarios, this assumption does not hold since many factors such as illumination conditions and intrinsic variability of the endmembers have consequences on the spectral signatures of the materials. In this paper, we propose a simple yet flexible algorithm to unmix hyperspectral data using a recently proposed Extended Linear Mixing Model. This model allows a pixelwise variation of the endmembers, which leads to consider scaled versions of reference endmember spectra. The results on synthetic data show that the proposed technique outperforms other methods aimed at tackling spectral variability, and provides an accurate estimation of endmember variability along the observed scene thanks to the scaling factors estimation, provided the abundance of the corresponding material is sufficient.
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Dates et versions

hal-01165114 , version 1 (18-06-2015)

Identifiants

  • HAL Id : hal-01165114 , version 1

Citer

Lucas Drumetz, Simon Henrot, Miguel Angel Veganzones, Jocelyn Chanussot, Christian Jutten. Blind hyperspectral unmixing using an Extended Linear Mixing Model to address spectral variability. WHISPERS 2015 - 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, Jun 2015, Tokyo, Japan. ⟨hal-01165114⟩
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