5 research outputs found
A Low-rank Tensor Regularization Strategy for Hyperspectral Unmixing
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
Super-Resolution for Hyperspectral and Multispectral Image Fusion Accounting for Seasonal Spectral Variability
Image fusion combines data from different heterogeneous sources to obtain
more precise information about an underlying scene. Hyperspectral-multispectral
(HS-MS) image fusion is currently attracting great interest in remote sensing
since it allows the generation of high spatial resolution HS images,
circumventing the main limitation of this imaging modality. Existing HS-MS
fusion algorithms, however, neglect the spectral variability often existing
between images acquired at different time instants. This time difference causes
variations in spectral signatures of the underlying constituent materials due
to different acquisition and seasonal conditions. This paper introduces a novel
HS-MS image fusion strategy that combines an unmixing-based formulation with an
explicit parametric model for typical spectral variability between the two
images. Simulations with synthetic and real data show that the proposed
strategy leads to a significant performance improvement under spectral
variability and state-of-the-art performance otherwise