NDLI: Weighted Group Sparsity-Constrained Tensor Factorization for Hyperspectral Unmixing

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Weighted Group Sparsity-Constrained Tensor Factorization for Hyperspectral Unmixing

Content Provider	MDPI
Author	Feng, Xinxi Han, Le Dong, Le
Copyright Year	2022
Description	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.
Starting Page	383
e-ISSN	20724292
DOI	10.3390/rs14020383
Journal	Remote Sensing
Issue Number	2
Volume Number	14
Language	English
Publisher	MDPI
Publisher Date	2022-01-14
Access Restriction	Open
Subject Keyword	Remote Sensing Imaging Science Hyperspectral Image Unmixing Nonnegative Tensor Factorization Total Variation Group Sparsity
Content Type	Text
Resource Type	Article

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