Monitoring Sea Fog over the Yellow Sea and Bohai Bay Based on Deep Convolutional Neural Network  

作　　者：HUANG Bin GAO Shi-bo YU Run-ling ZHAO Wei ZHOU Guan-bo 黄彬;高士博;于润玲;赵伟;周冠博(National Meteorological Center,Beijing 100081 China;State Key Laboratory of Severe Weather,Chinese Academy of Meteorological Sciences,Beijing 100081 China;Key Laboratory of South China Sea Meteorological Disaster Prevention and Mitigation of Hainan Province,Haikou 570203 China;Agronomy College,Shenyang Agricultural University,Shenyang 110866 China;Shanghai Typhoon Institute,China Meteorological Administration,Shanghai 200030 China;Key Laboratory of Numerical Modeling for Tropical Cyclones,China Meteorological Administration,Shanghai 200030 China)

机构地区：[1]National Meteorological Center,Beijing 100081 China [2]State Key Laboratory of Severe Weather,Chinese Academy of Meteorological Sciences,Beijing 100081 China [3]Key Laboratory of South China Sea Meteorological Disaster Prevention and Mitigation of Hainan Province,Haikou 570203 China [4]Agronomy College,Shenyang Agricultural University,Shenyang 110866 China [5]Shanghai Typhoon Institute,China Meteorological Administration,Shanghai 200030 China [6]Key Laboratory of Numerical Modeling for Tropical Cyclones,China Meteorological Administration,Shanghai 200030 China

出　　处：《Journal of Tropical Meteorology》2024年第3期223-229,共7页热带气象学报（英文版）

基　　金：National Key R&D Program of China(2021YFC3000905);Open Research Program of the State Key Laboratory of Severe Weather(2022LASW-B09);National Natural Science Foundation of China(42375010)。

摘　　要：In this paper,we utilized the deep convolutional neural network D-LinkNet,a model for semantic segmentation,to analyze the Himawari-8 satellite data captured from 16 channels at a spatial resolution of 0.5 km,with a focus on the area over the Yellow Sea and the Bohai Sea(32°-42°N,117°-127°E).The objective was to develop an algorithm for fusing and segmenting multi-channel images from geostationary meteorological satellites,specifically for monitoring sea fog in this region.Firstly,the extreme gradient boosting algorithm was adopted to evaluate the data from the 16 channels of the Himawari-8 satellite for sea fog detection,and we found that the top three channels in order of importance were channels 3,4,and 14,which were fused into false color daytime images,while channels 7,13,and 15 were fused into false color nighttime images.Secondly,the simple linear iterative super-pixel clustering algorithm was used for the pixel-level segmentation of false color images,and based on super-pixel blocks,manual sea-fog annotation was performed to obtain fine-grained annotation labels.The deep convolutional neural network D-LinkNet was built on the ResNet backbone and the dilated convolutional layers with direct connections were added in the central part to form a string-and-combine structure with five branches having different depths and receptive fields.Results show that the accuracy rate of fog area(proportion of detected real fog to detected fog)was 66.5%,the recognition rate of fog zone(proportion of detected real fog to real fog or cloud cover)was 51.9%,and the detection accuracy rate(proportion of samples detected correctly to total samples)was 93.2%.

关 键 词：deep convolutional neural network satellite images sea fog detection multi-channel image fusion 

分 类 号：P456.1[天文地球—大气科学及气象学]