cicyt UNIZAR
Full-text links:

Download:

Current browse context:

cs.CV

Change to browse by:

cs

References & Citations

DBLP - CS Bibliography

Bookmark

(what is this?)
CiteULike logo BibSonomy logo Mendeley logo del.icio.us logo Digg logo Reddit logo ScienceWISE logo

Computer Science > Computer Vision and Pattern Recognition

Title: A Low-rank Tensor Regularization Strategy for Hyperspectral Unmixing

Abstract: 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.
Subjects: Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:1803.06355 [cs.CV]
  (or arXiv:1803.06355v1 [cs.CV] for this version)

Submission history

From: Tales Cesar De Oliveira Imbiriba [view email]
[v1] Fri, 16 Mar 2018 18:06:46 GMT (610kb,D)