Nascimento, JoseBioucas-Dias, José M.2016-05-272016-05-272012NASCIMENTO, José M. P.; BIOUCAS-DIAS, José M. - Hyperspectral unmixing with simultaneous dimensionality estimation. ICPRAM 2012 - International Conference on Pattern Recognition Applications and Methods. 2012http://hdl.handle.net/10400.21/6216This paper is an elaboration of the simplex identification via split augmented Lagrangian (SISAL) algorithm (Bioucas-Dias, 2009) to blindly unmix hyperspectral data. SISAL is a linear hyperspectral unmixing method of the minimum volume class. This method solve a non-convex problem by a sequence of augmented Lagrangian optimizations, where the positivity constraints, forcing the spectral vectors to belong to the convex hull of the endmember signatures, are replaced by soft constraints. With respect to SISAL, we introduce a dimensionality estimation method based on the minimum description length (MDL) principle. The effectiveness of the proposed algorithm is illustrated with simulated and real data.engBlind hyperspectral unmixingMinimum volume simplexMinimum Description LengthMDLVariable splitting augmented LagrangianDimensionality reductionconference object