摘要
基于2005—2020年的中国气象局台风最佳路径数据集以及葵花(Himawari)8和风云(FY)卫星云图数据,首先将卫星原始数据转换为FULLDISK灰度图像作为台风涡旋识别技术的图像来源,并制定新的VOC(Visual Object Classes)标注规范,构建了样本标注数据集。利用运行速度快、识别准确率高的人工智能领域经典目标检测SSD(Single Shot MultiBox Detector)模型作为台风涡旋识别的基础模型,并针对台风涡旋识别的独特性,特别是弱涡旋识别困难,提出一种迭代的SSD目标检测模型,明显提高了台风涡旋的识别精度。通过目标检测技术对卫星云图进行智能特征分析、抽取、识别和定位,实现了自动涡旋正确识别和定位,最终建立了智能台风涡旋识别技术。测试结果显示:该技术对强热带风暴级以下强度台风涡旋正确识别率为40%~80%,对强热带风暴级及以上强度台风涡旋正确识别率达90%以上,能够精准识别强台风级及以上强度涡旋,该技术为今后业务利用高时空分辨率卫星图像对台风进行实时精密监测提供了技术支撑。
The present study is based on the typhoon best track data released by the China Meteorological Administration and the satellite images of Himawari-8 and FY from 2005 to 2020.Firstly,the FULLDISK gray images from original satellite data are used as the source of images for typhoon vortex detection,and the images are labeled to develop the sample labeling data set.Furthermore,the classic target detection SSD model with fast running speed and high recognition accuracy is employed as the basic model of typhoon vortex detection.Based on the characteristics of typhoon vortex,the SSD basic model is improved,and an iterative SSD target detection model is developed,which can improve vortex detection and positioning.Finally,the intelligent typhoon vortex detection technique is developed.The test results show that the correct recognition rate of typhoon weaker than severe tropical storms is between 40%~80%,the correct recognition rate of severe tropical storm or stronger typhoon is more than 90%,and the correct recognition rate of vortex at typhoon level or above is close to 100%.This technique will support the realtime and precise monitoring of typhoon by using high spatio-temporal resolution satellite data in the future.
作者
吕心艳
钱奇峰
王登科
周冠博
徐雅静
LYU Xinyan;QIAN Qifeng;WANG Dengke;ZHOU Guanbo;XU Yajing(National Meteorological Center,Beijing 100081,China;Beijing University of Post and Telecommunications,Beijing 100876,China)
出处
《热带气象学报》
CSCD
北大核心
2022年第4期492-501,共10页
Journal of Tropical Meteorology
基金
广东省重点领域研发计划项目(2019B111101002)
国家自然科学基金(42175016)
国家气象中心预报员专项(Y202114)共同资助
关键词
目标检测
卫星云图
台风涡旋
智能识别
object detection
satellite image
typhoon vortex
intelligent detection
