The Spatially Seamless Spatiotemporal Fusion Model Based on Generative Adversarial Networks Article Swipe
Chenyang Weng
,
Yulin Zhan
,
Xingfa Gu
,
Jian Yang
,
Yan Liu
,
Hong Guo
,
Zilong Lian
,
Shiyuan Zhang
,
Zhangjie Wang
,
Xuechun Zhao
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/jstars.2024.3381185
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/jstars.2024.3381185
Spatiotemporal fusion is a method of fusing high spatial resolution low temporal resolution remote sensing images and low spatial resolution high temporal resolution in order to obtain high spatiotemporal resolution remote sensing images, which can provide data support for temporal observation of fine objects, and plays an important role in the fields of Earth sciences, environmental monitoring, and so on. This article reveals an issue that is often overlooked in the field of deep learning-based spatiotemporal fusion: the discontinuity between image blocks and image blocks. This discontinuity may have an impact on the visualization of remote sensing images and subsequent applications. In this regard, this article proposes a spatially seamless stitching approach to optimize the spatiotemporal fusion model based on deep learning. By using this method, we successfully obtain high-quality fused remote sensing images with smoother transitions. The spatiotemporal fusion model used in the experiment is a generative adversarial network-based spatiotemporal fusion model (GAN-STFM) and the data are from the Beijing Gaofen-6 dataset (BJGF6). After our splicing method, the ratio of root-mean-square error (RMSE) at the splicing seam to the overall RMSE is reduced from 1.28 to 0.99, which effectively improves the continuity of the image. This new image splicing method has the potential to improve the utility of deep learning-based spatiotemporal fusion algorithms, which has application value for generating large-scale long time series remote sensing datasets with high temporal and high spatial resolution.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/jstars.2024.3381185
- https://ieeexplore.ieee.org/ielx7/4609443/4609444/10478639.pdf
- OA Status
- gold
- Cited By
- 4
- References
- 37
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4393145147
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4393145147Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/jstars.2024.3381185Digital Object Identifier
- Title
-
The Spatially Seamless Spatiotemporal Fusion Model Based on Generative Adversarial NetworksWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-01-01Full publication date if available
- Authors
-
Chenyang Weng, Yulin Zhan, Xingfa Gu, Jian Yang, Yan Liu, Hong Guo, Zilong Lian, Shiyuan Zhang, Zhangjie Wang, Xuechun ZhaoList of authors in order
- Landing page
-
https://doi.org/10.1109/jstars.2024.3381185Publisher landing page
- PDF URL
-
https://ieeexplore.ieee.org/ielx7/4609443/4609444/10478639.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://ieeexplore.ieee.org/ielx7/4609443/4609444/10478639.pdfDirect OA link when available
- Concepts
-
Computer science, Image fusion, Artificial intelligence, Image resolution, Fusion, Image stitching, Deep learning, Remote sensing, Sensor fusion, Pattern recognition (psychology), Computer vision, Image (mathematics), Geography, Philosophy, LinguisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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4Total citation count in OpenAlex
- Citations by year (recent)
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2025: 4Per-year citation counts (last 5 years)
- References (count)
-
37Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.spatiotemporal | 28, 75, 115, 138, 150, 211 |
| abstract_inverted_index.root-mean-square | 171 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 97 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 10 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/14 |
| sustainable_development_goals[0].score | 0.5099999904632568 |
| sustainable_development_goals[0].display_name | Life below water |
| citation_normalized_percentile.value | 0.85285782 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |