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Analysis of institutional authors

Song, TaoCorresponding Author

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October 24, 2024
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NDFTC: A New Detection Framework of Tropical Cyclones from Meteorological Satellite Images with Deep Transfer Learning

Publicated to: Remote Sensing. 13 (9): 1860- - 2021-05-01 13(9), DOI: 10.3390/rs13091860

Authors:

Pang, Shanchen; Xie, Pengfei; Xu, Danya; Meng, Fan; Tao, Xixi; Li, Bowen; Li, Ying; Song, Tao
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Affiliations

China Univ Petr, Coll Comp Sci & Technol, Qingdao 266580, Peoples R China - Author
China Univ Petr, Sch Geosci, Qingdao 266580, Peoples R China - Author
South China Univ Technol, Sch Comp Sci & Engn, Guangzhou 510006, Peoples R China - Author
Southern Marine Sci & Engn Guangdong Lab Zhuhai, Zhuhai 519080, Peoples R China - Author
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Abstract

Accurate detection of tropical cyclones (TCs) is important to prevent and mitigate natural disasters associated with TCs. Deep transfer learning methods have advantages in detection tasks, because they can further improve the stability and accuracy of the detection model. Therefore, on the basis of deep transfer learning, we propose a new detection framework of tropical cyclones (NDFTC) from meteorological satellite images by combining the deep convolutional generative adversarial networks (DCGAN) and You Only Look Once (YOLO) v3 model. The algorithm process of NDFTC consists of three major steps: data augmentation, a pre-training phase, and transfer learning. First, to improve the utilization of finite data, DCGAN is used as the data augmentation method to generate images simulated to TCs. Second, to extract the salient characteristics of TCs, the generated images obtained from DCGAN are inputted into the detection model YOLOv3 in the pre-training phase. Furthermore, based on the network-based deep transfer learning method, we train the detection model with real images of TCs and its initial weights are transferred from the YOLOv3 trained with generated images. Training with real images helps to extract universal characteristics of TCs and using transferred weights as initial weights can improve the stability and accuracy of the model. The experimental results show that the NDFTC has a better performance, with an accuracy (ACC) of 97.78% and average precision (AP) of 81.39%, in comparison to the YOLOv3, with an ACC of 93.96% and AP of 80.64%.
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Keywords

Adversarial networksCommunicationConvolutional neural networksData augmentationDeep learningDeep transfer learningDetection frameworkDetection modelsDisaster preventionDisastersForestGenerative adversarial networkGenerative adversarial networksHurricanesImage enhancementInitial weightsLearning systemsMeteorological satellite imagesNatural disastersOceaStormsSuperresolutionTransfer learningTransfer learning methodsTropical cycloneTropical cyclone detectionTropics

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Remote Sensing due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2021, it was in position 30/202, thus managing to position itself as a Q1 (Primer Cuartil), in the category Geosciences, Multidisciplinary.

From a relative perspective, and based on the normalized impact indicator calculated from World Citations provided by WoS (ESI, Clarivate), it yields a value for the citation normalization relative to the expected citation rate of: 1.03. This indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: ESI Nov 13, 2025)

This information is reinforced by other indicators of the same type, which, although dynamic over time and dependent on the set of average global citations at the time of their calculation, consistently position the work at some point among the top 50% most cited in its field:

  • Weighted Average of Normalized Impact by the Scopus agency: 2.76 (source consulted: FECYT Mar 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2025-12-22, the following number of citations:

  • WoS: 21
  • Scopus: 34
  • Google Scholar: 34
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Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-12-22:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 23.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 23 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 1.
  • The number of mentions on the social network X (formerly Twitter): 1 (Altmetric).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
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Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: China.

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: Last Author (SONG, TAO).

the author responsible for correspondence tasks has been SONG, TAO.

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Awards linked to the item

This work was supported by the National Key Research and Development Program (no. 2018YFC1406201) and the Natural Science Foundation of China (grant: U1811464). The project was supported by the Innovation Group Project of the Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) (no. 311020008), the Natural Science Foundation of Shandong Province (grant no. ZR2019MF012), and the Taishan Scholars Fund (grant no. ZX20190157).
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