Authors / CoAuthors
Baker, S. | Huang, Z. | Philippa, L.
Abstract
Cloud coverage remains a key issue for researchers working with satellite data. Accurate reconstruction of measurements obstructed by cloud can enhance the usefulness of satellite databases for identifying trends and changes in various environments. In this work, we develop, train and test a bidirectional long short-term memory (BiLSTM) model with a custom temporal penalty layer for filling gaps in sea surface temperature (SST) images acquired by the Himawari- 8 satellite. The proposed model showed strong performance, achieving a per-image MAE of 0.1193◦C and per-image RMSE of 0.0985◦C. Our model is also shown to outperform previous state-of-the-art literature. Overall, this work shows that our BiLSTM algorithm is an effective tool for gapfilling cloud-affected SST data. <b>Citation: </b>S. Baker, Z. Huang and B. Philippa, "Lightweight Neural Network for Spatiotemporal Filling of Data Gaps in Sea Surface Temperature Images," in <i>IEEE Transactions on Geoscience and Remote Sensing</i>, vol. 61, pp. 1-10, 2023, Art no. 4204310, doi: 10.1109/TGRS.2023.3273575
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document
eCat Id
147158
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Keywords
- theme.ANZRC Fields of Research.rdf
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- EARTH SCIENCES
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- Sea surface temperature - SST
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- bidirectional long short-term memory - BiLSTM
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- neural network
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- spatiotemporal
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- Published_External
Publication Date
2023-11-01T05:47:54
Creation Date
2022-08-23T13:48:00
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completed
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external publication
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Topic Category
geoscientificInformation oceans
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IEEE Transactions on Geoscience and Remote Sensing Volume 61 1-10
Lineage
The is the results of a collaboration with two colleagues from JCU. The paper is published. Baker, S., Huang, Z., Philippa, B., 2023. Lightweight Neural Network for Spatiotemporal Filling of Data Gaps in Sea Surface Temperature Images, IEEE Transactions on Geoscience and Remote Sensing, https://doi.org/10.1109/TGRS.2023.3273575.
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[-44.00, -9.00, 112.00, 154.00]
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