Chong Wang, Qing Xu, Xiaofeng Li, G. Zheng, B. Liu, Yongcun Cheng
{"title":"基于卫星红外图像的台风客观监测技术","authors":"Chong Wang, Qing Xu, Xiaofeng Li, G. Zheng, B. Liu, Yongcun Cheng","doi":"10.1109/PIERS-Fall48861.2019.9021497","DOIUrl":null,"url":null,"abstract":"In this paper, an objective technique was developed for monitoring typhoons over the Northwestern Pacific Ocean with Himawari-8 geostationary satellite infrared imagery. Two convolutional neural networks (CNNs) were designed to locate a typhoon and estimate its intensity, respectively. The mean error of the typhoon center location (CNN-Location) model is 5.4 pixels (54 km), and the top-1 accuracy and root mean square error (RMSE) of the intensity estimation (CNN-Intensity) model are 79.6% and 11.66 kt, respectively. By changing the loss function from categorical_crossentropy to focal_loss in the CNN-Intensity model, higher top-1 accuracy of 82.9% and lower RMSE of 10.84 kt are obtained. The results demonstrate that CNN has great potential in the application of automatic typhoon monitoring.","PeriodicalId":197451,"journal":{"name":"2019 Photonics & Electromagnetics Research Symposium - Fall (PIERS - Fall)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"An Objective Technique for Typhoon Monitoring with Satellite Infrared Imagery\",\"authors\":\"Chong Wang, Qing Xu, Xiaofeng Li, G. Zheng, B. Liu, Yongcun Cheng\",\"doi\":\"10.1109/PIERS-Fall48861.2019.9021497\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, an objective technique was developed for monitoring typhoons over the Northwestern Pacific Ocean with Himawari-8 geostationary satellite infrared imagery. Two convolutional neural networks (CNNs) were designed to locate a typhoon and estimate its intensity, respectively. The mean error of the typhoon center location (CNN-Location) model is 5.4 pixels (54 km), and the top-1 accuracy and root mean square error (RMSE) of the intensity estimation (CNN-Intensity) model are 79.6% and 11.66 kt, respectively. By changing the loss function from categorical_crossentropy to focal_loss in the CNN-Intensity model, higher top-1 accuracy of 82.9% and lower RMSE of 10.84 kt are obtained. The results demonstrate that CNN has great potential in the application of automatic typhoon monitoring.\",\"PeriodicalId\":197451,\"journal\":{\"name\":\"2019 Photonics & Electromagnetics Research Symposium - Fall (PIERS - Fall)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 Photonics & Electromagnetics Research Symposium - Fall (PIERS - Fall)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/PIERS-Fall48861.2019.9021497\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 Photonics & Electromagnetics Research Symposium - Fall (PIERS - Fall)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PIERS-Fall48861.2019.9021497","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An Objective Technique for Typhoon Monitoring with Satellite Infrared Imagery
In this paper, an objective technique was developed for monitoring typhoons over the Northwestern Pacific Ocean with Himawari-8 geostationary satellite infrared imagery. Two convolutional neural networks (CNNs) were designed to locate a typhoon and estimate its intensity, respectively. The mean error of the typhoon center location (CNN-Location) model is 5.4 pixels (54 km), and the top-1 accuracy and root mean square error (RMSE) of the intensity estimation (CNN-Intensity) model are 79.6% and 11.66 kt, respectively. By changing the loss function from categorical_crossentropy to focal_loss in the CNN-Intensity model, higher top-1 accuracy of 82.9% and lower RMSE of 10.84 kt are obtained. The results demonstrate that CNN has great potential in the application of automatic typhoon monitoring.