Deblurring and Sparse Unmixing for Hyperspectral Images

The main aim of this paper is to study total variation (TV) regularization in deblurring and sparse unmixing of hyperspectral images. In the model, we also incorporate blurring operators for dealing with blurring effects, particularly blurring operators for hyperspectral imaging whose point spread f...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2013-07, Vol.51 (7), p.4045-4058
Main Authors: Xi-Le Zhao, Fan Wang, Ting-Zhu Huang, Ng, M. K., Plemmons, R. J.
Format: Article
Language:English
Subjects:
Algorithms
Alternating direction methods
Applied geophysics
Blurring
Convergence
deblurring
Earth sciences
Earth, ocean, space
Exact sciences and technology
Hyperspectral imaging
Internal geophysics
linear spectral unmixing
Mathematical models
Matrix decomposition
Numerical models
Operators
Optimization
Point spread functions
Regularization
Studies
Television
total variation (TV)
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Summary:The main aim of this paper is to study total variation (TV) regularization in deblurring and sparse unmixing of hyperspectral images. In the model, we also incorporate blurring operators for dealing with blurring effects, particularly blurring operators for hyperspectral imaging whose point spread functions are generally system dependent and formed from axial optical aberrations in the acquisition system. An alternating direction method is developed to solve the resulting optimization problem efficiently. According to the structure of the TV regularization and sparse unmixing in the model, the convergence of the alternating direction method can be guaranteed. Experimental results are reported to demonstrate the effectiveness of the TV and sparsity model and the efficiency of the proposed numerical scheme, and the method is compared to the recent Sparse Unmixing via variable Splitting Augmented Lagrangian and TV method by Iordache et al.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2012.2227764