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?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2111.12483
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2111.12483
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 has 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 Worldview2 and Worldview3 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 (mathematics)
Block (permutation group theory)
Relation (database)
Image resolution
Consistency (knowledge bases)
Computer vision
Data mining
Mathematics
Electrical engineering
Engineering
Geometry
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2111.12483
- https://arxiv.org/pdf/2111.12483
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4307531780
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4307531780Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2111.12483Digital Object Identifier
- Title
-
LDP-Net: An Unsupervised Pansharpening Network Based on Learnable Degradation ProcessesWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2021Year of publication
- Publication date
-
2021-11-24Full 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://arxiv.org/abs/2111.12483Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2111.12483Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2111.12483Direct OA link when available
- Concepts
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Panchromatic film, Multispectral image, Computer science, Artificial intelligence, Fuse (electrical), Pattern recognition (psychology), Image (mathematics), Block (permutation group theory), Relation (database), Image resolution, Consistency (knowledge bases), Computer vision, Data mining, Mathematics, Electrical engineering, Engineering, GeometryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.(HRMS) | 11 |
| abstract_inverted_index.(LRMS) | 19 |
| abstract_inverted_index.dubbed | 109 |
| abstract_inverted_index.fusing | 15, 91 |
| abstract_inverted_index.hybrid | 132 |
| abstract_inverted_index.image. | 25, 48 |
| abstract_inverted_index.images | 153, 162, 173 |
| abstract_inverted_index.latent | 82 |
| abstract_inverted_index.remote | 2 |
| abstract_inverted_index.simply | 79 |
| abstract_inverted_index.visual | 189 |
| abstract_inverted_index.LDP-Net | 167 |
| abstract_inverted_index.between | 84, 144 |
| abstract_inverted_index.combine | 33 |
| abstract_inverted_index.concern | 28 |
| abstract_inverted_index.effects | 190 |
| abstract_inverted_index.graying | 117 |
| abstract_inverted_index.methods | 51, 65 |
| abstract_inverted_index.network | 103 |
| abstract_inverted_index.propose | 99 |
| abstract_inverted_index.sensing | 3 |
| abstract_inverted_index.spatial | 44, 140 |
| abstract_inverted_index.usually | 66 |
| abstract_inverted_index.without | 175 |
| abstract_inverted_index.However, | 63 |
| abstract_inverted_index.LDP-Net. | 111 |
| abstract_inverted_index.abundant | 43 |
| abstract_inverted_index.designed | 120 |
| abstract_inverted_index.directly | 13 |
| abstract_inverted_index.function | 134 |
| abstract_inverted_index.ignoring | 80 |
| abstract_inverted_index.learning | 55 |
| abstract_inverted_index.metrics. | 193 |
| abstract_inverted_index.proposed | 58, 136, 166 |
| abstract_inverted_index.relation | 83 |
| abstract_inverted_index.samples, | 180 |
| abstract_inverted_index.spectral | 36, 142 |
| abstract_inverted_index.Recently, | 49 |
| abstract_inverted_index.achieving | 181 |
| abstract_inverted_index.acquiring | 7 |
| abstract_inverted_index.addition, | 129 |
| abstract_inverted_index.constrain | 138 |
| abstract_inverted_index.different | 155 |
| abstract_inverted_index.directly. | 93 |
| abstract_inverted_index.learnable | 106 |
| abstract_inverted_index.learning; | 76 |
| abstract_inverted_index.problems, | 97 |
| abstract_inverted_index.promising | 182 |
| abstract_inverted_index.requiring | 72 |
| abstract_inverted_index.Worldview2 | 159 |
| abstract_inverted_index.Worldview3 | 161 |
| abstract_inverted_index.drawbacks: | 70 |
| abstract_inverted_index.processes, | 108, 126 |
| abstract_inverted_index.reblurring | 113 |
| abstract_inverted_index.supervised | 75 |
| abstract_inverted_index.Experiments | 157 |
| abstract_inverted_index.consistency | 143 |
| abstract_inverted_index.degradation | 107, 125 |
| abstract_inverted_index.demonstrate | 163 |
| abstract_inverted_index.effectively | 32, 174 |
| abstract_inverted_index.information | 37, 45 |
| abstract_inverted_index.performance | 183 |
| abstract_inverted_index.qualitative | 188 |
| abstract_inverted_index.panchromatic | 23 |
| abstract_inverted_index.pansharpened | 146 |
| abstract_inverted_index.quantitative | 192 |
| abstract_inverted_index.resolutions. | 156 |
| abstract_inverted_index.unsupervised | 102 |
| abstract_inverted_index.Pansharpening | 0 |
| abstract_inverted_index.corresponding | 124 |
| abstract_inverted_index.multispectral | 10, 18 |
| abstract_inverted_index.pansharpening | 61 |
| abstract_inverted_index.respectively. | 127 |
| abstract_inverted_index.low-resolution | 17 |
| abstract_inverted_index.high-resolution | 9 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile |