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241016s2024 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2024.3468905
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|a eng
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| 100 |
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|a Chen, Shi
|e verfasserin
|4 aut
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| 245 |
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|a Cross-Scope Spatial-Spectral Information Aggregation for Hyperspectral Image Super-Resolution
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|c 2024
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 22.10.2024
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Hyperspectral image super-resolution has attained widespread prominence to enhance the spatial resolution of hyperspectral images. However, convolution-based methods have encountered challenges in harnessing the global spatial-spectral information. The prevailing transformer-based methods have not adequately captured the long-range dependencies in both spectral and spatial dimensions. To alleviate this issue, we propose a novel cross-scope spatial-spectral Transformer (CST) to efficiently investigate long-range spatial and spectral similarities for single hyperspectral image super-resolution. Specifically, we devise cross-attention mechanisms in spatial and spectral dimensions to comprehensively model the long-range spatial-spectral characteristics. By integrating global information into the rectangle-window self-attention, we first design a cross-scope spatial self-attention to facilitate long-range spatial interactions. Then, by leveraging appropriately characteristic spatial-spectral features, we construct a cross-scope spectral self-attention to effectively capture the intrinsic correlations among global spectral bands. Finally, we elaborate a concise feed-forward neural network to enhance the feature representation capacity in the Transformer structure. Extensive experiments over three hyperspectral datasets demonstrate that the proposed CST is superior to other state-of-the-art methods both quantitatively and visually. The code is available at https://github.com/Tomchenshi/CST.git
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|a Journal Article
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| 700 |
1 |
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|a Zhang, Lefei
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhang, Liangpei
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 33(2024) vom: 15., Seite 5878-5891
|w (DE-627)NLM09821456X
|x 1941-0042
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|g volume:33
|g year:2024
|g day:15
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|u http://dx.doi.org/10.1109/TIP.2024.3468905
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