LDP-Net: An Unsupervised Pansharpening Network Based on Learnable Degradation Processes Article Swipe
Jiahui Ni
,
Zhimin Shao
,
Zhongzhou Zhang
,
Mingzheng Hou
,
Jiliu Zhou
,
Leyuan Fang
,
Yi Zhang
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/jstars.2022.3188181
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/jstars.2022.3188181
Pansharpening in remote sensing image aims at acquiring a high-resolution multispectral (HRMS) image directly by fusing a low-resolution multispectral (LRMS) image with a panchromatic (PAN) image. The main concern is how to effectively combine the rich spectral information of LRMS image with the abundant spatial information of PAN image. Recently, many methods based on deep learning have been proposed for the pansharpening task. However, these methods usually have two main drawbacks: 1) requiring HRMS for supervised learning; and 2) simply ignoring the latent relation between the MS and PAN image and fusing them directly. To solve these problems, we propose a novel unsupervised network based on learnable degradation processes, dubbed as LDP-Net. A reblurring block and a graying block are designed to learn the corresponding degradation processes, respectively. In addition, a novel hybrid loss function is proposed to constrain both spatial and spectral consistency between the pansharpened image and the PAN and LRMS images at different resolutions. Experiments on GaoFen-2, Worldview-2, and Worldview-3 images demonstrate that our proposed LDP-Net can fuse PAN and LRMS images effectively without the help of HRMS samples, achieving promising performance in terms of both qualitative visual effects and quantitative metrics.
Related Topics
Concepts
Panchromatic film
Multispectral image
Computer science
Artificial intelligence
Fuse (electrical)
Pattern recognition (psychology)
Image resolution
Block (permutation group theory)
Image (mathematics)
Relation (database)
Image fusion
Computer vision
Data mining
Mathematics
Electrical engineering
Geometry
Engineering
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/jstars.2022.3188181
- https://ieeexplore.ieee.org/ielx7/4609443/4609444/09814841.pdf
- OA Status
- gold
- Cited By
- 32
- References
- 59
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3217107289
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3217107289Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/jstars.2022.3188181Digital Object Identifier
- Title
-
LDP-Net: An Unsupervised Pansharpening Network Based on Learnable Degradation ProcessesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-01-01Full publication date if available
- Authors
-
Jiahui Ni, Zhimin Shao, Zhongzhou Zhang, Mingzheng Hou, Jiliu Zhou, Leyuan Fang, Yi ZhangList of authors in order
- Landing page
-
https://doi.org/10.1109/jstars.2022.3188181Publisher landing page
- PDF URL
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https://ieeexplore.ieee.org/ielx7/4609443/4609444/09814841.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://ieeexplore.ieee.org/ielx7/4609443/4609444/09814841.pdfDirect OA link when available
- Concepts
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Panchromatic film, Multispectral image, Computer science, Artificial intelligence, Fuse (electrical), Pattern recognition (psychology), Image resolution, Block (permutation group theory), Image (mathematics), Relation (database), Image fusion, Computer vision, Data mining, Mathematics, Electrical engineering, Geometry, EngineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
32Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 5, 2024: 14, 2023: 12, 2022: 1Per-year citation counts (last 5 years)
- References (count)
-
59Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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