  Tensor-based methods have recently emerged as a more natural and effective
formulation to address many problems in hyperspectral imaging. In hyperspectral
unmixing (HU), low-rank constraints on the abundance maps have been shown to
act as a regularization which adequately accounts for the multidimensional
structure of the underlying signal. However, imposing a strict low-rank
constraint for the abundance maps does not seem to be adequate, as important
information that may be required to represent fine scale abundance behavior may
be discarded. This paper introduces a new low-rank tensor regularization that
adequately captures the low-rank structure underlying the abundance maps
without hindering the flexibility of the solution. Simulation results with
synthetic and real data show that the the extra flexibility introduced by the
proposed regularization significantly improves the unmixing results.
