南京航空航天大学学报（英文版）2022，Vol.39Issue(4)：425-433,9.
基于对比学习的终端区相似气象场景识别
Recognition of Similar Weather Scenarios in Terminal Area Based on Contrastive Learning
摘要
Abstract
In order to improve the recognition accuracy of similar weather scenarios(SWSs) in terminal area,a recognition model for SWS based on contrastive learning (SWS-CL) is proposed. Firstly,a data augmentation method is designed to improve the number and quality of weather scenarios samples according to the characteristics of convective weather images. Secondly,in the pre-trained recognition model of SWS?CL,a loss function is formulated to minimize the distance between the anchor and positive samples,and maximize the distance between the anchor and the negative samples in the latent space. Finally,the pre-trained SWS-CL model is fine-tuned with labeled samples to improve the recognition accuracy of SWS. The comparative experiments on the weather images of Guangzhou terminal area show that the proposed data augmentation method can effectively improve the quality of weather image dataset,and the proposed SWS-CL model can achieve satisfactory recognition accuracy. It is also verified that the fine-tuned SWS-CL model has obvious advantages in datasets with sparse labels.关键词
空中交通管制/终端区/相似气象场景/图像识别/对比学习Key words
air traffic control/terminal area/similar weather scenarios(SWSs)/image recognition/contrastive learning分类
信息技术与安全科学引用本文复制引用
陈海燕,刘振亚,周逸,袁立罡..基于对比学习的终端区相似气象场景识别[J].南京航空航天大学学报（英文版）,2022,39(4):425-433,9.基金项目
This work was supported by the Fun?damental Research Funds for the Central Universities(NOS.NS2019054,NS2020045). （NOS.NS2019054,NS2020045）
