空间科学学报2023，Vol.43Issue(6)：1091-1099,9.DOI:10.11728/cjss2023.06.2023-0097
基于U-Net的海洋锋智能检测模型
Oceanic Front Detection Model Based on U-Net Network
摘要
Abstract
As a boundary of two water masses with different properties,oceanic fronts have impor-tant influences on many fields such as fishery,marine military and environmental protection.How to quickly and accurately implement automatic detection and identification of ocean front is of great scien-tific significance for ocean monitoring and forecasting.In this paper,the deep learning image segmenta-tion network is combined with the method of extracting frontal features,and the detection models of frontal area and frontal line are established by using U-Net architecture.Meanwhile,the residual unit is used to improve the feature extraction network in the processes of encoding and decoding.The results show that the deep learning frontal detection model can accurately extract the features of frontal area and frontal line.The Dice coefficients reach 0.92 and 0.97 respectively,achieving a good detection perfor-mance.In this paper,the model is trained by the sample data of different frontal thresholds.The com-parison results show that the accuracy of model is significantly improved after the threshold of sample set is reduced.关键词
海洋锋/海表温度/深度学习/U-NetKey words
Oceanic fronts/Sea surface temperature/Deep learning/U-Net分类
海洋科学引用本文复制引用
任诗鹤,韩焱红,李竞时,赵亚明,匡晓迪,吴湘玉,杨晓峰..基于U-Net的海洋锋智能检测模型[J].空间科学学报,2023,43(6):1091-1099,9.基金项目
国家自然科学基金项目(41806003),遥感科学国家重点实验室开放基金项目(OFSLRSS202219)和国家重大科技基础设施项目"地球系统数值模拟装置"共同资助 （41806003）
