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231225s2018 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2018.2836307
|2 doi
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|a pubmed24n1308.xml
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|a DE-627
|b ger
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|e rakwb
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|a eng
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| 100 |
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|a Li, Shutao
|e verfasserin
|4 aut
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| 245 |
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|a Fusing Hyperspectral and Multispectral Images via Coupled Sparse Tensor Factorization
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|c 2018
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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|a ƒa Online-Ressource
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|2 rdacarrier
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|a Date Revised 27.02.2024
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|a published: Print-Electronic
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|a Citation Status Publisher
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|a Fusing a low spatial resolution hyperspectral image (LR-HSI) with a high spatial resolution multispectral image (HR-MSI) to obtain a high spatial resolution hyperspectral image (HR-HSI) has attracted increasing interest in recent years. In this paper, we propose a coupled sparse tensor factorization (CSTF) based approach for fusing such images. In the proposed CSTF method, we consider an HR-HSI as a three-dimensional tensor and redefine the fusion problem as the estimation of a core tensor and dictionaries of the three modes. The high spatial-spectral correlations in the HR-HSI are modeled by incorporating a regularizer which promotes sparse core tensors. The estimation of the dictionaries and the core tensor are formulated as a coupled tensor factorization of the LR-HSI and of the HR-MSI. Experiments on two remotely sensed HSIs demonstrate the superiority of the proposed CSTF algorithm over current state-of-the-art HSI-MSI fusion approaches
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|a Journal Article
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| 700 |
1 |
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|a Dian, Renwei
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Fang, Leyuan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Bioucas-Dias, Jose M
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g (2018) vom: 15. Mai
|w (DE-627)NLM09821456X
|x 1941-0042
|7 nnns
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| 773 |
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|g year:2018
|g day:15
|g month:05
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|u http://dx.doi.org/10.1109/TIP.2018.2836307
|3 Volltext
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|a AR
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|j 2018
|b 15
|c 05
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