Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume 30, Issue 9, Pages 2672-2683Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2018.2885616
Keywords
Hyperspectral imaging; image fusion; low tensor-train (TT) rank (LTTR) learning; superresolution
Categories
Funding
- National Natural Science Fund of China for International Cooperation and Exchanges [61520106001]
- Fund of Hunan Province for Science and Technology Plan Project [2017RS3024]
- Hunan Provincial Innovation Foundation for Postgraduate
- China Scholarship Council
Ask authors/readers for more resources
Hyperspectral images (HSIs) with high spectral resolution only have the low spatial resolution. On the contrary, multispectral images (MSIs) with much lower spectral resolution can be obtained with higher spatial resolution. Therefore, fusing the high-spatial-resolution MSI (HR-MSI) with low-spatialresolution HSI of the same scene has become the very popular HSI super-resolution scheme. In this paper, a novel low tensortrain (TT) rank (LTTR)-based HSI super-resolution method is proposed, where an LTTR prior is designed to learn the correlations among the spatial, spectral, and nonlocal modes of the nonlocal similar high-spatial-resolution HSI (HR-HSI) cubes. First, we cluster the HR-MSI cubes as many groups based on their similarities, and the HR-HSI cubes are also clustered according to the learned cluster structure in the HR-MSI cubes. The HR-HSI cubes in each group are much similar to each other and can constitute a 4-D tensor, whose four modes are highly correlated. Therefore, we impose the LTTR constraint on these 4-D tensors, which can effectively learn the correlations among the spatial, spectral, and nonlocal modes because of the well balanced matricization scheme of TT rank. We formulate the super-resolution problem as TT rank regularized optimization problem, which is solved via the scheme of alternating direction method of multipliers. Experiments on HSI data sets indicate the effectiveness of the LTTR-based method.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available