Journal
IEEE TRANSACTIONS ON IMAGE PROCESSING
Volume 27, Issue 8, Pages 4118-4130Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIP.2018.2836307
Keywords
Super-resolution; fusion; hyperspectral imaging; coupled sparse tensor factorization
Funding
- National Natural Science Fund of China [61325007, 61520106001]
- Fund of Hunan Province for Science and Technology Plan Project [2017RS3024]
- Portuguese Science and Technology Foundation [UID/EEA/50008/2013, ERANETMED/0001/2014]
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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 3D 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 the current state-of-the-art HSI-MSI fusion approaches.
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