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• W2804744787 abstract "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." @default.
• W2804744787 created "2018-06-01" @default.
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• W2804744787 date "2018-08-01" @default.
• W2804744787 modified "2023-10-14" @default.
• W2804744787 title "Fusing Hyperspectral and Multispectral Images via Coupled Sparse Tensor Factorization" @default.
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• W2804744787 doi "https://doi.org/10.1109/tip.2018.2836307" @default.
• W2804744787 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/29994767" @default.
• W2804744787 hasPublicationYear "2018" @default.
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