Weighted Group Sparsity-Constrained Tensor Factorization for Hyperspectral Unmixing

Recently, unmixing methods based on nonnegative tensor factorization have played an important role in the decomposition of hyperspectral mixed pixels. According to the spatial prior knowledge, there are many regularizations designed to improve the performance of unmixing algorithms, such as the tota...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2022-01, Vol.14 (2), p.383
Main Authors: Feng, Xinxi, Han, Le, Dong, Le
Format: Article
Language:English
Subjects:
Algorithms
Constraints
Decomposition
Factorization
group sparsity
hyperspectral image
Hyperspectral imaging
Mathematical analysis
Methods
nonnegative tensor factorization
Optimization
Regularization
Remote sensing
Sparsity
Spectral bands
Tensors
total variation
unmixing
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Summary:Recently, unmixing methods based on nonnegative tensor factorization have played an important role in the decomposition of hyperspectral mixed pixels. According to the spatial prior knowledge, there are many regularizations designed to improve the performance of unmixing algorithms, such as the total variation (TV) regularization. However, these methods mostly ignore the similar characteristics among different spectral bands. To solve this problem, this paper proposes a group sparse regularization that uses the weighted constraint of the L2,1 norm, which can not only explore the similar characteristics of the hyperspectral image in the spectral dimension, but also keep the data smooth characteristics in the spatial dimension. In summary, a non-negative tensor factorization framework based on weighted group sparsity constraint is proposed for hyperspectral images. In addition, an effective alternating direction method of multipliers (ADMM) algorithm is used to solve the algorithm proposed in this paper. Compared with the existing popular methods, experiments conducted on three real datasets fully demonstrate the effectiveness and advancement of the proposed method.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs14020383