Integrated approach for ocean data remote sensing with extensive ecological and earth system science learning
Author:
Publisher
Springer Science and Business Media LLC
Subject
Management Science and Operations Research,General Decision Sciences
Link
https://link.springer.com/content/pdf/10.1007/s10479-021-04377-6.pdf
Reference24 articles.
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2. Du, Y., Song, W., He, Q., Huang, D., Liotta, A., & Su, C. (2019). Deep learning with multiscale feature fusion in remote sensing for automatic oceanic eddy detection. Information Fusion, 49, 89–99.
3. Gao, J., Wang, H., & Shen, H. (2020). Smartly handling renewable energy instability in supporting a cloud datacenter. In 2020 IEEE international parallel and distributed processing symposium (IPDPS) (pp. 769–778). IEEE.
4. Karpatne, A., Ebert-Uphoff, I., Ravela, S., Babaie, H. A., & Kumar, V. (2018). Machine learning for the geosciences: Challenges and opportunities. IEEE Transactions on Knowledge and Data Engineering, 31(8), 1544–1554.
5. Legeais, J. F., Ablain, M., Zawadzki, L., Zuo, H., Johannessen, J. A., Scharffenberg, M. G., & Benveniste, J. (2018). An improved and homogeneous altimeter sea-level record from the ESA Climate Change Initiative. Earth System Science Data, 10(1), 281–301.
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1. DeepBlue: Advanced convolutional neural network applications for ocean remote sensing;IEEE Geoscience and Remote Sensing Magazine;2023
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