Bridging satellite missions: deep transfer learning for enhanced tropical cyclone intensity estimation-Reference-Cited by-同舟云学术

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

Published:2024-03-11 Issue:1 Volume:61 Page:
ISSN:1548-1603
Container-title:GIScience & Remote Sensing
language:en
Short-container-title:GIScience & Remote Sensing

Author:

Choo Minki¹^ORCID,Kim Yejin¹^ORCID,Lee Juhyun¹^ORCID,Im Jungho¹²^ORCID,Moon Il-Ju³^ORCID

Affiliation:

1. Department of Civil, Urban, Earth & Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea

2. Graduate School of Carbon Neutrality, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea

3. Typhoon Research Center, Jeju National University, Jeju, Republic of Korea

Funder

Korea Institute of Marine Science & Technology

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/15481603.2024.2325720

Reference57 articles.

1. Factors of Transferability for a Generic ConvNet Representation

2. Quantifying interagency differences in intensity estimations of Super Typhoon Lekima (2019)

3. 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

4. Retrieving vertical profiles of water-cloud droplet effective radius: Algorithm modification and preliminary application

5. 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.

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