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- Publisher Website: 10.1109/TGRS.2020.3015878
- Scopus: eid_2-s2.0-85106733063
- WOS: WOS:000652834200052
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Article: Super-Resolution-Guided Progressive Pansharpening Based on a Deep Convolutional Neural Network
Title | Super-Resolution-Guided Progressive Pansharpening Based on a Deep Convolutional Neural Network |
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Authors | |
Keywords | Deep learning multispectral (MS) image panchromatic image pansharpening super-resolution (SR) |
Issue Date | 2021 |
Citation | IEEE Transactions on Geoscience and Remote Sensing, 2021, v. 59, n. 6, p. 5206-5220 How to Cite? |
Abstract | Pansharpening and super-resolution (SR) methods share the same target to improve the spatial resolution of images. Based on this similarity, we propose and develop a novel pansharpening algorithm that is guided by a deep SR convolutional neural network. The proposed framework comprises three components: an SR process, a progressive pansharpening process, and a high-pass residual module. Specifically, the SR process extracts inner spatial detail that is present in multispectral images. Then, progressive pansharpening is used as a detailed pansharpening process, and the high-pass residual module helps by directly injecting spatial detail from panchromatic images. The performance of the proposed network has been compared with that of traditional and other deep-learning-based pansharpening algorithms based on QuickBird, WorldView-3, and Landsat-8 data, and the results demonstrate the superiority of our algorithm. |
Persistent Identifier | http://hdl.handle.net/10722/329712 |
ISSN | 2023 Impact Factor: 7.5 2023 SCImago Journal Rankings: 2.403 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Cai, Jiajun | - |
dc.contributor.author | Huang, Bo | - |
dc.date.accessioned | 2023-08-09T03:34:47Z | - |
dc.date.available | 2023-08-09T03:34:47Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | IEEE Transactions on Geoscience and Remote Sensing, 2021, v. 59, n. 6, p. 5206-5220 | - |
dc.identifier.issn | 0196-2892 | - |
dc.identifier.uri | http://hdl.handle.net/10722/329712 | - |
dc.description.abstract | Pansharpening and super-resolution (SR) methods share the same target to improve the spatial resolution of images. Based on this similarity, we propose and develop a novel pansharpening algorithm that is guided by a deep SR convolutional neural network. The proposed framework comprises three components: an SR process, a progressive pansharpening process, and a high-pass residual module. Specifically, the SR process extracts inner spatial detail that is present in multispectral images. Then, progressive pansharpening is used as a detailed pansharpening process, and the high-pass residual module helps by directly injecting spatial detail from panchromatic images. The performance of the proposed network has been compared with that of traditional and other deep-learning-based pansharpening algorithms based on QuickBird, WorldView-3, and Landsat-8 data, and the results demonstrate the superiority of our algorithm. | - |
dc.language | eng | - |
dc.relation.ispartof | IEEE Transactions on Geoscience and Remote Sensing | - |
dc.subject | Deep learning | - |
dc.subject | multispectral (MS) image | - |
dc.subject | panchromatic image | - |
dc.subject | pansharpening | - |
dc.subject | super-resolution (SR) | - |
dc.title | Super-Resolution-Guided Progressive Pansharpening Based on a Deep Convolutional Neural Network | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/TGRS.2020.3015878 | - |
dc.identifier.scopus | eid_2-s2.0-85106733063 | - |
dc.identifier.volume | 59 | - |
dc.identifier.issue | 6 | - |
dc.identifier.spage | 5206 | - |
dc.identifier.epage | 5220 | - |
dc.identifier.eissn | 1558-0644 | - |
dc.identifier.isi | WOS:000652834200052 | - |