Journal
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Volume 58, Issue 12, Pages 8384-8394Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2020.2987530
Keywords
Tensors; Dictionaries; Sparse matrices; Noise reduction; Spatial resolution; Correlation; Hyperspectral imaging (HSI); nonlocal sparse tensor factorization (NLSTF); nonnegative tensor dictionary learning; super-resolution
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Funding
- National Key Research and Development Program of China [2017YFA0604903]
- China Scholarship Council [201706040141]
- National Science Foundation [DMS-1521582]
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Hyperspectral image (HSI) super-resolution refers to enhancing the spatial resolution of a 3-D image with many spectral bands (slices). It is a seriously ill-posed problem when the low-resolution (LR) HSI is the only input. It is better solved by fusing the LR HSI with a high-resolution (HR) multispectral image (MSI) for a 3-D image with both high spectral and spatial resolution. In this article, we propose a novel nonnegative and nonlocal 4-D tensor dictionary learning-based HSI super-resolution model using group-block sparsity. By grouping similar 3-D image cubes into clusters and then conduct super-resolution cluster by cluster using 4-D tensor structure, we not only preserve the structure but also achieve sparsity within the cluster due to the collection of similar cubes. We use 4-D tensor Tucker decomposition and impose nonnegative constraints on the dictionaries and group-block sparsity. Numerous experiments demonstrate that the proposed model outperforms many state-of-the-art HSI super-resolution methods.
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