A Long-time-series Spatio-Temporal-Spectral Fusion Method via Multi-task Learning
Abstract. Due to the limitations of sensor hardware, clouds and fog, and data transmission limitations, it is difficult for the data obtained by spaceborne remote sensing imager to achieve high temporal, spatial and spectral resolution at the same time, which limits its application in long-time-series high-frequency monitoring. At present, there are several spatio-temporal-spectral algorithms that can realize the fusion of temporal, spatial and spectral resolution, but most of them are based on one to two discrete images, and the integrated fusion at the multi-dimensional level has not yet been realized. There is currently no research on the spatio-temporal-spectral fusion method based on LONG-TIME-SERIES multi-scene remote sensing data. Aiming at solving the bottleneck of spatio-tempora-spectral resolution of remote sensing data, this study proposes a new long-time-series spatio-temporal-spectral fusion method based on multi-task learning to realize the multi-dimensional optimization of multi-source remote sensing data resolutions. Experiments used simulated and real datasets, both of which contain 4 images of 10m ZY1-02D multispectral data, 7 images of 16m GF-6 multispectral data and 4 images of 30m ZY1-02D hyperspectral data, and obtained 7 images of 10m hyperspectral data. The experiments The results show that our method performs the best compared to other methods. This method can provide effective data support for applications based on long-time series remote sensing data.
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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A Long-time-series Spatio-Temporal-Spectral Fusion Method via Multi-task Learning ; volume:XLVIII-1-2024 ; year:2024 ; pages:567-572 ; extent:6
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ; XLVIII-1-2024 (2024), 567-572 (gesamt 6)
- Creator
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Peng, Mingyuan
Li, Canhai
Zhou, Xiaoqing
Li, Guoyuan
- DOI
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10.5194/isprs-archives-XLVIII-1-2024-567-2024
- URN
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urn:nbn:de:101:1-2405160448202.697050059066
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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14.08.2025, 10:53 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Peng, Mingyuan
- Li, Canhai
- Zhou, Xiaoqing
- Li, Guoyuan
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