{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T04:13:19Z","timestamp":1776312799775,"version":"3.50.1"},"reference-count":63,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2021,5,10]],"date-time":"2021-05-10T00:00:00Z","timestamp":1620604800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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%.<\/jats:p>","DOI":"10.3390\/rs13091860","type":"journal-article","created":{"date-parts":[[2021,5,10]],"date-time":"2021-05-10T12:49:49Z","timestamp":1620650989000},"page":"1860","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["NDFTC: A New Detection Framework of Tropical Cyclones from Meteorological Satellite Images with Deep Transfer Learning"],"prefix":"10.3390","volume":"13","author":[{"given":"Shanchen","family":"Pang","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, China University of Petroleum, Qingdao 266580, China"}]},{"given":"Pengfei","family":"Xie","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, China University of Petroleum, Qingdao 266580, China"}]},{"given":"Danya","family":"Xu","sequence":"additional","affiliation":[{"name":"Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519080, China"}]},{"given":"Fan","family":"Meng","sequence":"additional","affiliation":[{"name":"School of Geosciences, China University of Petroleum, Qingdao 266580, China"}]},{"given":"Xixi","family":"Tao","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, China University of Petroleum, Qingdao 266580, China"}]},{"given":"Bowen","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China"}]},{"given":"Ying","family":"Li","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, China University of Petroleum, Qingdao 266580, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0130-3340","authenticated-orcid":false,"given":"Tao","family":"Song","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, China University of Petroleum, Qingdao 266580, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1007\/BF00162096","article-title":"Cyclones and storm surges in Bangladesh: Some mitigative measures","volume":"6","author":"Khalil","year":"1992","journal-title":"Nat. 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