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High spectral and high spatial resolution is paramount for high performance identification and classification of hyperspectal (HS) images.
We propose a novel graph Laplacian-guided coupled tensor decomposition (gLGCTD) model for fusion of hyperspectral image (HSI) and multispectral image (MSI) for ...
Missing: L1/ | Show results with:L1/
High spectral and high spatial resolution is paramount for high performance identification and classification of hyperspectal (HS) images.
2011), hyperspectral and multispectral image fusion (Bendoumi and Mingyi 2013). ... Hyperspectral Unmixing via L1/2 Sparsity-Constrained. Nonnegative Matrix ...
Nov 20, 2019 · The L 2,1-norm as a sparse constraint is added to the model because the L 2,1-norm is robust and can achieve row sparse effect. The ...
Missing: Multispectral | Show results with:Multispectral
Experimental results show that the GLNMF based fusion approach outperforms state-of-the-art CNMF based data fusion. Experimental results are illustrated on ...
Multilayer NMF has been used to improve the results of NMF methods for spectral unmixing of hyperspectral data under the linear mixing framework and results ...
Missing: Fusion. | Show results with:Fusion.
Abstract—Hyperspectral unmixing (HU) is one of the crucial steps for many hyperspectral applications, including material clas- sification and recognition.
Missing: Graph | Show results with:Graph
Sep 29, 2022 · In this paper, a novel NMF unmixing model is proposed, called SMRNMF, which learns multiple subspace structures from the original hyperspectral images.
The L1/2-NMF provides more sparse and accurate results than the other regularizers by considering the end-member additivity constraint explicitly in the ...
Missing: Fusion. | Show results with:Fusion.