Bridging satellite missions: deep transfer learning for enhanced tropical cyclone intensity estimation

References

• Azizpour, H., A. S. Razavian, J. Sullivan, A. Maki, and S. Carlsson. 2015. “Factors of Transferability for a Generic Convent Representation.” IEEE Transactions on Pattern Analysis and Machine Intelligence 38 (9): 1790–18. https://doi.org/10.1109/TPAMI.2015.2500224.
   PubMed Web of Science ®Google Scholar
• Bai, L., J. Tang, R. Guo, S. Zhang, and K. Liu. 2022. “Quantifying Interagency Differences in Intensity Estimations of Super Typhoon Lekima (2019).” Frontiers of Earth Science 16 (1): 1–12. https://doi.org/10.1007/s11707-020-0866-5.
   Web of Science ®Google Scholar
• Ba, M. B., and S. E. Nicholson. 1998. “Analysis of Convective Activity and Its Relationship to the Rainfall Over the Rift Valley Lakes of East Africa During 1983–90 Using the Meteosat Infrared Channel.” Journal of Applied Meteorology 37 (10): 1250–1264. https://doi.org/10.1175/1520-0450(1998)037<1250:AOCAAI>2.0.CO;2.
   Google Scholar
• Chang, F. L., and Z. Li. 2003. “Retrieving vertical profiles of water‐cloud droplet effective radius: Algorithm modification and preliminary application.” Journal of Geophysical Research: Atmospheres 108 (D24). https://doi.org/10.1029/2003JD003906.
   Web of Science ®Google Scholar
• Chu, B., V. Madhavan, O. Beijbom, J. Hoffman, and T. Darrell. 2016. Best Practices for Fine-Tuning Visual Classifiers to New Domains. In Computer Vision–ECCV 2016 Workshops: Amsterdam, The Netherlands, October 8–10 and 15–16, 2016, Proceedings, Part III 14, Amsterdam, The Netehrlands, 435–442. Springer International Publishing.
   Google Scholar
• Combinido, J. S., J. R. Mendoza, and J. Aborot. 2018. “A Convolutional Neural Network Approach for Estimating Tropical Cyclone Intensity Using Satellite-Based Infrared Images.” In 2018 24th International conference on pattern recognition (ICPR), Beijing, China, 1474–1480. IEEE.
   Google Scholar
• Combinido, J. S., J. R. Mendoza, and J. Aborot. (2018, August). ”A Convolutional Neural Network Approach for Estimating Tropical Cyclone Intensity Using Satellite-Based Infrared Images.” In 2018 24th International conference on pattern recognition (ICPR), 1474–1480. Beijing, China. IEEE.
   Google Scholar
• Dvorak, V. F. 1975. “Tropical Cyclone Intensity Analysis and Forecasting from Satellite Imagery.” Monthly Weather Review 103 (5): 420–430. https://doi.org/10.1175/1520-0493(1975)103<0420:TCIAAF>2.0.CO;2.
   Web of Science ®Google Scholar
• Gadiraju, K. K., and R. R. Vatsavai. 2023. “Application of Transfer Learning in Remote Sensing Crop Image Classification.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 16: 4699–4712.
   Web of Science ®Google Scholar
• Gao, L., H. Liu, M. Yang, L. Chen, Y. Wan, Z. Xiao, and Y. Qian. 2021. “STransFuse: Fusing Swin Transformer and Convolutional Neural Network for Remote Sensing Image Semantic Segmentation.” In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14:10990–11003. https://doi.org/10.1109/JSTARS.2021.3119654.
   Web of Science ®Google Scholar
• Gao, S., L. Zhu, W. Zhang, and X. Shen. 2020. “Western North Pacific Tropical Cyclone Activity in 2018: A Season of Extremes.” Scientific Reports 10 (1): 1–9. https://doi.org/10.1038/s41598-020-62632-5.
   PubMed Web of Science ®Google Scholar
• Gomroki, M., M. Hasanlou, and P. Reinartz. 2023. “STCD-EffV2T Unet: Semi Transfer Learning EfficientNetv2 T-Unet Network for Urban/Land Cover Change Detection Using Sentinel-2 Satellite Images.” Remote Sensing 15 (5): 1232. https://doi.org/10.3390/rs15051232.
   Web of Science ®Google Scholar
• Guo, X., J. Yin, and J. Yang. 2024. ”Fine classification of crops based on an inductive transfer learning method with compact polarimetric SAR images.” GIScience & Remote Sensing 61 (1). https://doi.org/10.1080/15481603.2024.2319939.
   Web of Science ®Google Scholar
• Hewison, T. J., X. Wu, F. Yu, Y. Tahara, X. Hu, D. Kim, and M. Koenig. 2013. “GSICS Inter-Calibration of Infrared Channels of Geostationary Imagers Using Metop/IASI.” IEEE Transactions on Geoscience and Remote Sensing 51 (3): 1160–1170. https://doi.org/10.1109/TGRS.2013.2238544.
   Web of Science ®Google Scholar
• Huh, M., P. Agrawal, and A. A. Efros. 2016. “What Makes ImageNet Good for Transfer Learning?” arXiv preprint arXiv:1608.08614.
   Google Scholar
• Iman, M., H. R. Arabnia, and K. Rasheed. 2023. “A review of deep transfer learning and recent advancements.” Technologies 11 (2): 40. https://doi.org/10.3390/technologies11020040.
   Web of Science ®Google Scholar
• Jiang, W., G. Hu, T. Wu, L. Liu, B. Kim, Y. Xiao, and Z. Duan. 2023. “DMANet_KF: Tropical Cyclone Intensity Estimation Based on Deep Learning and Kalman Filter from Multi-Spectral Infrared Images.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 16: 4469–4483.
   Web of Science ®Google Scholar
• Jiang, S., and L. Tao. 2022. “Classification and Estimation of Typhoon Intensity from Geostationary Meteorological Satellite Images Based on Deep Learning.” Atmosphere 13 (7): 1113. https://doi.org/10.3390/atmos13071113.
   Web of Science ®Google Scholar
• Kaur, S., S. Gupta, S. Singh, V. T. Hoang, S. Almakdi, T. Alelyani, and A. Shaikh. 2022. “Transfer Learning-based Automatic Hurricane Damage Detection Using Satellite Images.” Electronics 11 (9): 1448. https://doi.org/10.3390/electronics11091448.
   Web of Science ®Google Scholar
• Kim, D. H., and M. H. Ahn. 2014. “Introduction of the In-Orbit Test and Its Performance for the First Meteorological Imager of the Communication, Ocean, and Meteorological Satellite.” Atmospheric Measurement Techniques 7 (8): 2471–2485. https://doi.org/10.5194/amt-7-2471-2014.
   Web of Science ®Google Scholar
• Kim, D., M. H. Ahn, and M. Choi. 2015. “Inter-Comparison of the Infrared Channels of the Meteorological Imager Onboard COMS and Hyperspectral IASI Data.” Advances in Atmospheric Sciences 32 (7): 979–990. https://doi.org/10.1007/s00376-014-4124-1.
   Web of Science ®Google Scholar
• Kim, D., M. Gu, T. H. Oh, E. K. Kim, and H. J. Yang. 2021. “Introduction of the Advanced Meteorological Imager of Geo-Kompsat-2a: In-Orbit Tests and Performance Validation.” Remote Sensing 13 (7): 1303. https://doi.org/10.3390/rs13071303.
   Web of Science ®Google Scholar
• Knapp, K. R., M. C. Kruk, D. H. Levinson, H. J. Diamond, and C. J. Neumann. 2010. “The International Best Track Archive for Climate Stewardship (IBTrAcs) Unifying Tropical Cyclone Data.” Bulletin of the American Meteorological Society 91 (3): 363–376. https://doi.org/10.1175/2009BAMS2755.1.
   Web of Science ®Google Scholar
• Kornblith, S., J. Shlens, and Q. V. Le. 2019. “Do Better Imagenet Models Transfer Better?.” In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, California, USA, 2661–2671.
   Google Scholar
• Krichene, H., T. Vogt, F. Piontek, T. Geiger, C. Schötz, and C. Otto. 2023. “The Social Costs of Tropical Cyclones.” Nature Communications 14 (1): 7294. https://doi.org/10.1038/s41467-023-43114-4.
   PubMed Web of Science ®Google Scholar
• Kurniawan, A. A., K. Usman, and R. Y. N. Fuadah. 2019, November. “Classification of Tropical Cyclone Intensity on Satellite Infrared Imagery Using SVM Method.” In 2019 IEEE Asia Pacific Conference on Wireless and Mobile (APWiMob), Bali, Indonesia, 69–73. IEEE.
   Google Scholar
• Lee, S., and J. Choi. 2022. “A Snow Cover Mapping Algorithm Based on a Multitemporal Dataset for GK-2A Imagery.” GIScience & Remote Sensing 59 (1): 1078–1102. https://doi.org/10.1080/15481603.2022.2097395.
   Web of Science ®Google Scholar
• Lee, J., J. Im, D. H. Cha, H. Park, and S. Sim. 2020. “Tropical Cyclone Intensity Estimation Using Multi-dimensional Convolutional Neural Networks From Geostationary Satellite Data.” Remote Sensing 12 (1): 108. https://doi.org/10.3390/rs12010108.
   Web of Science ®Google Scholar
• Lee, J., M. Kim, J. Im, H. Han, and D. Han. 2021. ”Pre-trained feature aggregated deep learning-based monitoring of overshooting tops using multi-spectral channels of GeoKompsat-2A advanced meteorological imagery.” GIScience & Remote Sensing 58 (7): 1052–1071. https://doi.org/10.1080/15481603.2021.1960075.
   Web of Science ®Google Scholar
• Liu, Z., Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, and B. Guo. 2021. “Swin Transformer: Hierarchical Vision Transformer Using Shifted Windows.” In Proceedings of the IEEE/CVF international conference on computer vision, Montreal, Canada, 10012–10022.
   Google Scholar
• Liu, Z., J. Ning, Y. Cao, Y. Wei, Z. Zhang, S. Lin, and H. Hu. 2022. “Video swin transformer.” In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, New Orleans, USA, 3202–3211.
   Google Scholar
• Lv, Z., K. Nunez, E. Brewer, and D. Runfola. 2024. ”Mapping the tidal marshes of coastal Virginia: a hierarchical transfer learning approach.” GIScience & Remote Sensing 61 (1). https://doi.org/10.1080/15481603.2023.2287291.
   Web of Science ®Google Scholar
• Magee, A. D., D. C. Verdon-Kidd, and A. S. Kiem. 2016. “An Intercomparison of Tropical Cyclone Best-Track Products for the Southwest Pacific.” Natural Hazards and Earth System Sciences 16 (6): 1431–1447. https://doi.org/10.5194/nhess-16-1431-2016.
   Web of Science ®Google Scholar
• Ma, D., L. Jiang, J. Li, and Y. Shi. 2023. “Water Index and Swin Transformer Ensemble (WISTE) for Water Body Extraction from Multi-Spectral Remote Sensing Images.” GIScience & Remote Sensing 60 (1): 2251704. https://doi.org/10.1080/15481603.2023.2251704.
   Web of Science ®Google Scholar
• Ma, D., L. Jiang, J. Li, and Y. Shi. 2023. ”Water index and Swin Transformer Ensemble (WISTE) for water body extraction from multispectral remote sensing images.” GIScience & Remote Sensing 60 (1). https://doi.org/10.1080/15481603.2023.2251704.
   Web of Science ®Google Scholar
• Muhammad, M. B., and M. Yeasin. 2020. “Eigen-Cam: Class Activation Map Using Principal Components.” In 2020 international joint conference on neural networks (IJCNN), Glasgow, UK, July, 1–7. IEEE.
   Google Scholar
• Norouzi, H., M. Temimi, A. AghaKouchak, M. Azarderakhsh, R. Khanbilvardi, G. Shields, and K. Tesfagiorgis. 2015. “Inferring Land Surface Parameters from the Diurnal Variability of Microwave and Infrared Temperatures.” Physics and Chemistry of the Earth, Parts A/B/C 83:28–35. https://doi.org/10.1016/j.pce.2015.01.007.
   Web of Science ®Google Scholar
• Olander, T. L., and C. S.Velden. 2007. ”The Advanced Dvorak Technique: Continued Development of an Objective Scheme to Estimate Tropical Cyclone Intensity Using Geostationary Infrared Satellite Imagery.” Weather and Forecasting 22 (2): 287–298. https://doi.org/10.1175/WAF975.1.
   Web of Science ®Google Scholar
• Piñeros, M. F., E. A. Ritchie, and J. S. Tyo. 2011. ”Estimating Tropical Cyclone Intensity from Infrared Image Data.” Weather and Forecasting 26 (5): 690–698. https://doi.org/10.1175/WAF-D-10-05062.1.
   Web of Science ®Google Scholar
• Powell, M. D., S. H. Houston, and T. A. Reinhold. 1996. “Hurricane Andrew’s Landfall in South Florida. Part I: Standardizing Measurements for Documentation of Surface Wind Fields.” Weather and Forecasting 11 (3): 304–328. https://doi.org/10.1175/1520-0434(1996)011<0304:HALISF>2.0.CO;2.
   Web of Science ®Google Scholar
• Pradhan, R., R. S. Aygun, M. Maskey, R. Ramachandran, and D. J. Cecil. 2018. “Tropical Cyclone Intensity Estimation Using a Deep Convolutional Neural Network.” IEEE Transactions on Image Processing 27 (2): 692–702. https://doi.org/10.1109/TIP.2017.2766358.
   PubMed Web of Science ®Google Scholar
• Ritchie, E. A., G. Valliere-Kelley, M. F. Piñeros, and J. S. Tyo. 2012. ”Tropical Cyclone Intensity Estimation in the North Atlantic Basin Using an Improved Deviation Angle Variance Technique.” Weather and Forecasting 27 (5): 1264–1277. https://doi.org/10.1175/WAF-D-11-00156.1.
   Web of Science ®Google Scholar
• Schreck, C. J., III, K. R. Knapp, and J. P. Kossin. 2014. “The Impact of Best Track Discrepancies on Global Tropical Cyclone Climatologies Using IbtrACS.” Monthly Weather Review 142 (10): 3881–3899. https://doi.org/10.1175/MWR-D-14-00021.1.
   Web of Science ®Google Scholar
• Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, and I. Polosukhin. 2017. “Attention is All You Need.” In proceedings of the 2017 Conference on Advances in Neural Information Processing Systems, Long Beach, CA, USA, 4–9 December 2017. 5998–6008.
   Google Scholar
• Wang, C., G. Zheng, X. Li, Q. Xu, B. Liu, and J. Zhang. 2022. “Tropical Cyclone Intensity Estimation from Geostationary Satellite Imagery Using Deep Convolutional Neural Networks.” IEEE Transactions on Geoscience and Remote Sensing 60: 1–16. https://doi.org/10.1109/TGRS.2021.3066299.
   Web of Science ®Google Scholar
• Weiss, K., T. M. Khoshgoftaar, and D. Wang. 2016. “A Survey of Transfer Learning.” Journal of Big Data 3 (1): 1–40. https://doi.org/10.1186/s40537-016-0043-6.
   Google Scholar
• Wu, X., T. Hewison, and Y. Tahara. 2009. ”GSICS GEO-LEO intercalibration: Baseline algorithm and early results.” In proceedings of theAtmospheric and Environmental Remote Sensing Data Processing and Utilization V: Readiness for GEOSS III, San Diego, CA, USA, 2–6 August 2009, Vol. 7456, 25–36. Bellingham, WA, USA: SPIE.
   Google Scholar
• Xiao, H., L. Li, Q. Liu, X. Zhu, and Q. Zhang. 2023. ”Transformers in medical image segmentation: A review.” Biomedical Signal Processing and Control 84: 104791. https://doi.org/10.1016/j.bspc.2023.104791.
   Web of Science ®Google Scholar
• Ye, R., and Q. Dai. 2021. “Implementing Transfer Learning Across Different Datasets for Time Series Forecasting.” Pattern Recognition 109: 107617. https://doi.org/10.1016/j.patcog.2020.107617.
   Web of Science ®Google Scholar
• Yin, G., J. Baik, and J. Park. 2022. “Comprehensive Analysis of GEO-KOMPSAT-2A and FengYun Satellite-Based Precipitation Estimates Across Northeast Asia.” GIScience & Remote Sensing 59 (1): 782–800. https://doi.org/10.1080/15481603.2022.2067970.
   Web of Science ®Google Scholar
• Yu, B., F. Chen, N. Wang, L. Yang, H. Yang, and L. Wang. 2022. ”MSFTrans: a multi-task frequency-spatial learning transformer for building extraction from high spatial resolution remote sensing images.” GIScience & Remote Sensing 59 (1): 1978–1996. https://doi.org/10.1080/15481603.2022.2143678.
   Web of Science ®Google Scholar
• Zhang, D., Z. Liu, and X. Shi. 2020, December. “Transfer Learning on Efficientnet for Remote Sensing Image Classification.” In 2020 5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE), Harbin, China, 2255–2258. IEEE.
   Google Scholar
• Zhang, M., H. Singh, L. Chok, and R. Chunara. 2022. “Segmenting Across Places: The Need for Fair Transfer Learning with Satellite Imagery.” In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, USA, 2916–2925.
   Google Scholar
• Zhang, Y., and B. Wallace. 2015. “A Sensitivity Analysis of (And practitioners’ Guide To) Convolutional Neural Networks for Sentence Classification.” arXiv Preprint arXiv 1510:03820.
   Google Scholar
• Zhang, C. J., X. J. Wang, L. M. Ma, and X. Q. Lu. 2021. “Tropical Cyclone Intensity Classification and Estimation Using Infrared Satellite Images with Deep Learning.” In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14: 2070–2086. https://doi.org/10.1109/JSTARS.2021.3050767.
   Web of Science ®Google Scholar
• Zhang, Z., X. Yang, X. Wang, B. Wang, C. Wang, and Z. Du. 2022. “A Neural Network with Spatiotemporal Encoding Module for Tropical Cyclone Intensity Estimation from Infrared Satellite Image.” Knowledge-Based Systems 258:110005. https://doi.org/10.1016/j.knosys.2022.110005.
   Web of Science ®Google Scholar
• Zhuang, F., Z. Qi, K. Duan, D. Xi, Y. Zhu, H. Zhu, and Q. He, Q. He. 2021. “A Comprehensive Survey on Transfer Learning.” Proceedings of the IEEE 109 (1): 43–76. https://doi.org/10.1109/JPROC.2020.3004555.
   Web of Science ®Google Scholar