A New Extended Linear Mixing Model to Address Spectral Variability

Miguel Angel Veganzones (1) , Lucas Drumetz (1) , Guillaume Tochon (1) , Mauro Dalla Mura (1) , Antonio Plaza (2) , José M. Bioucas-Dias (3) , Jocelyn Chanussot (4, 1)

1 GIPSA-SIGMAPHY - GIPSA - Signal Images Physique
2 Hyperspectral Computing Laboratory (Caceres, Spain)
3 Instituto de Telecomunicações [Lisboa, Portugal]
4 University of Iceland [Reykjavik]

Spectral variability is a phenomenon due, to a grand extend, to variations in the illumination and atmospheric conditions within a hyperspectral image, causing the spectral signature of a material to vary within a image. Data spectral fluctuation due to spectral variability compromises the linear mixing model (LMM) sum-to-one constraint, and is an important source of error in hyperspectral image analysis. Recently, spectral variability has raised more attention and some techniques have been proposed to address this issue, i.e. spectral bundles. Here, we propose the definition of an extended LMM (ELMM) to model spectral variability and we show that the use of spectral bundles models the ELMM implicitly. We also show that the constrained least squares (CLS) is an explicit modelling of the ELMM when the spectral variability is due to scaling effects. We give experimental validation that spectral bundles (and sparsity) and CLS are complementary techniques addressing spectral variability. We finally discuss on future research avenues to fully exploit the proposed ELMM.

Mots clés: Spectral unmixing; extended linear mixing model; spectral bundles; sparsity; CLS

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

Miguel Angel Veganzones, Lucas Drumetz, Guillaume Tochon, Mauro Dalla Mura, Antonio Plaza, et al.. A New Extended Linear Mixing Model to Address Spectral Variability. WHISPERS 2014 - 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, Jun 2014, Lausanne, Switzerland. pp.n/c. ⟨hal-01010424⟩