Variability of the endmembers in spectral unmixing: recent advances

Lucas Drumetz (1) , Jocelyn Chanussot (1) , Christian Jutten (2)

1 GIPSA-SIGMAPHY - GIPSA - Signal Images Physique
2 GIPSA-VIBS - GIPSA - Vision and Brain Signal Processing

Endmember variability has been identified as one of the main limitations of the usual Linear Mixing Model, conventionally used to perform spectral unmixing of hyperspectral data. The topic is currently receiving a lot of attention from the community, and many new algorithms have recently been developed to model this variability and take it into account. In this paper, we review state of the art methods dealing with this problem and classify them into three categories: the algorithms based on endmember bundles, the ones based on computational models, and the ones based on parametric physics-based models. We discuss the advantages and drawbacks of each category of methods and list some open problems and current challenges.

Mots clés: Extended Linear Mixing Model; Endmember variability; Spectral Unmixing; Spectral Bundles

Domaines: Traitement du signal et de l'image [eess.SP]

Lucas Drumetz, Jocelyn Chanussot, Christian Jutten. Variability of the endmembers in spectral unmixing: recent advances. WHISPERS 2016 - 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, Aug 2016, Los Angeles, United States. ⟨hal-01358368⟩