Hyperspectral and Multispectral Image Fusion Using Factor Smoothed Tensor Ring Decomposition

作者：Yong Chen, Jinshan Zeng, Wei He, Xile Zhao, Tingzhu Huang · 发表于：IEEE Transactions on Geoscience and Remote Sensing · 年份：2021 · DOI：10.1109/TGRS.2021.3114197 · 被引用次数：82 · 研究领域：Computer Science

Fusing a pair of low-spatial-resolution hyperspectral image (LR-HSI) and high-spatial-resolution multispectral image (HR-MSI) has been regarded as an effective and economical strategy to achieve HR-HSI, which is essential to many applications. Among existing fusion models, the tensor ring (TR) decomposition-based model has attracted rising attention due to its superiority in approximating high-dimensional data compared to other traditional matrix/tensor decomposition models. Unlike directly estimating HR-HSI in traditional models, the TR fusion model translates the fusion procedure into an estimate of the TR factor of HR-HSI, which can efficiently capture the spatial–spectral correlation of HR-HSI. Although the spatial–spectral correlation has been preserved well by TR decomposition, the spatial–spectral continuity of HR-HSI is ignored in existing TR decomposition models, sometimes resulting in poor quality of reconstructed images. In this article, we introduce a factor smoothed regularization for TR decomposition to capture the spatial–spectral continuity of HR-HSI. As a result, our proposed model is called factor smoothed TR decomposition model, dubbed FSTRD. In order to solve the suggested model, we develop an efficient proximal alternating minimization algorithm. A series of experiments on four synthetic datasets and one real-world dataset show that the quality of reconstructed images can be significantly improved by the introduced factor smoothed regularization, and thus...