2020
DOI: 10.3390/rs12162535
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Toward Super-Resolution Image Construction Based on Joint Tensor Decomposition

Abstract: In recent years, fusing hyperspectral images (HSIs) and multispectral images (MSIs) to acquire super-resolution images (SRIs) has been in the spotlight and gained tremendous attention. However, some current methods, such as those based on low rank matrix decomposition, also have a fair share of challenges. These algorithms carry out the matrixing process for the original image tensor, which will lose the structure information of the original image. In addition, there is no corresponding theory to prove whether… Show more

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Cited by 3 publications
(2 citation statements)
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References 51 publications
(62 reference statements)
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“…HSIs with high spectral resolution are beneficial for various tasks, e.g., classification [2] and detection [3]. However, as the amount of incident energy is limited, observed HSIs usually have low spatial resolution (LR) [4]. Contrary to HSIs, observed multispectral images (MSIs) have high spatial resolution (HR) but low spectral resolution [5,6].…”
Section: Introductionmentioning
confidence: 99%
“…HSIs with high spectral resolution are beneficial for various tasks, e.g., classification [2] and detection [3]. However, as the amount of incident energy is limited, observed HSIs usually have low spatial resolution (LR) [4]. Contrary to HSIs, observed multispectral images (MSIs) have high spatial resolution (HR) but low spectral resolution [5,6].…”
Section: Introductionmentioning
confidence: 99%
“…Recently, an increasing number of HSI-MSI fusion algorithms have been proposed [32][33][34]. These algorithms have been proved to be effective with good fusion performance.…”
Section: Introductionmentioning
confidence: 99%