A Low-rank Tensor Regularization Strategy for Hyperspectral Unmixing
This work addresses hyperspectral unmixing for remote sensing applications, representing an incremental improvement over existing tensor-based methods.
The paper tackled the problem of hyperspectral unmixing by introducing a new low-rank tensor regularization that captures low-rank structure without discarding fine-scale abundance information, and simulation results with synthetic and real data showed significant improvement in unmixing results.
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.