L<sub>21</sub> Nonnegative Matrix Factorization for Hyperspectral Unmixing Based on Subspace Structure Regularization

CHEN Shanxue; LIU Ronghua

doi:10.11999/JEIT210232

Volume 44 Issue 5

May 2022

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CHEN Shanxue, LIU Ronghua. L21 Nonnegative Matrix Factorization for Hyperspectral Unmixing Based on Subspace Structure Regularization[J]. Journal of Electronics & Information Technology, 2022, 44(5): 1704-1713. doi: 10.11999/JEIT210232

Citation:

CHEN Shanxue, LIU Ronghua. L₂₁ Nonnegative Matrix Factorization for Hyperspectral Unmixing Based on Subspace Structure Regularization[J]. Journal of Electronics & Information Technology, 2022, 44(5): 1704-1713. doi: 10.11999/JEIT210232

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L₂₁ Nonnegative Matrix Factorization for Hyperspectral Unmixing Based on Subspace Structure Regularization

doi: 10.11999/JEIT210232

CHEN Shanxue,
LIU Ronghua^,

1.
School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
Chongqing Key Laboratory of Mobile Communications Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Funds: The National Natural Science Foundation of China (61271260), The Science and Technology Research Project of Chongqing Municipal Education Commission (KJ1400416)

Received Date: 2021-03-22
Rev Recd Date: 2021-08-13

Available Online: 2021-08-27

Publish Date: 2022-05-25

Abstract

Abstract

When the standard Nonnegative Matrix Factorization (NMF) is applied to hyperspectral unmixing, it is easy to be interfered by noise and outliers, and the unmixing effect is poor. In order to improve the factorized performance, the L₂₁ norm is introduced into the standard NMF algorithm, and the model is improved to improve the robustness of the algorithm. Secondly, in order to improve the sparsity of the factorized abundance matrix, the double reweighted sparse constraint is introduced into the L₂₁NMF model, so that one of the weights increases sparsity along the abundance vector corresponding to each pixel, and the other weight promotes the sparsity along the abundance vector corresponding to each endmember. Meanwhile, in order to utilize the global spatial distribution information of the pixels and observe the true distribution of materials in different images, the subspace structure regularization is introduced, and the L₂₁ Nonnegative Matrix Factorization based on Subspace Structure Regularization (L₂₁NMF-SSR) is proposed. The better performance and denoising ability of the proposed method are demonstrated by comparing with other classical methods on both synthetic and real datasets.
- Hyperspectral unmixing,
- Nonnegative Matrix Factorization(NMF),
- L₂₁ norm,
- Double reweighted sparse,
- Subspace structure

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References(25)

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