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Daytime sea fog detection based on multi-scale feature fusion of generated adversarial network under attention mechanism

  • role： First author第一作者
  • Affiliation：

    Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China

  • Email：fangxuyuan1130@163.com
  • Introduction：方旭源，研究方向为海雾监测、深度学习。E-mail： fangxuyuan1130@163.com
FANG Xuyuan1,  
  • role： Corresponding author通信作者
  • Affiliation：

    Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China

  • Email：xyjw1969@126.com
  • Introduction：金炜，研究方向为遥感图像处理、医学影像处理。E-mail： xyjw1969@126.com
JIN Wei1*,  
  • Affiliation：

    Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China

FU Randi1,  
  • Affiliation：

    Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China

LI Gang1,  
  • Affiliation：

    Zhenhai District Meteorological Bureau, Ningbo 315202, China

HE Caifen2,  
  • Affiliation：

    Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China

YI Caoqian1

resumen

Sea fog is a common weather phenomenon at sea. It will reduce visibility at sea and greatly threaten maritime traffic and other operations. Traditional sea fog detection algorithms using satellite remote sensing have low accuracy, poor portability, and low automation. Although some existing deep learning-based sea fog monitoring algorithms have been improved, they do not consider the spectral characteristics of sea fog in different channels. The accuracy of sea fog monitoring is also low, especially in edge recognition.A daytime sea fog detection method, which is based on multi-scale feature fusion of generated adversarial network under attention mechanism, is proposed to improve the accuracy of sea fog detection. First, according to the spectral response of sea fog in different imaging channels of meteorological satellite, the satellite cloud images of different imaging channels that can reflect the characteristics of sea fog are selected as the input of the network. Meanwhile, a channel attention mechanism is introduced to calculate the weights of different input channels for prioritizing significant imaging channels within multichannel input. Then, a multi-scale feature fusion mechanism is adopted to fuse the feature maps of different levels of the network for obtaining the multi-scale features of the sea fog. In this way, the problem of losing detailed features in cloud images caused by the pooling operation of the traditional deep network can be solved. Finally, given the difficulty of traditional methods to accurately describe the edge of sea fog, a generation network for sea fog detection supervised by an adversarial network is used to accurately define the edge of sea fog and reduce the false alarm rate.This study takes the Yellow Sea and the Bohai Sea (116.5°—128.25°E，30°—42.5°N) as the research area. Given that March to June each year is the period of high incidence of sea fog in the Yellow Sea and the Bohai Sea, we produce a dataset based on the weather satellite monitoring report of the National Meteorological Center from March to June 2017—2020. After training the model, concerning the quantitative indicators of sea fog detection, our method achieves a probability of detection of 90.5%, a critical success index of 81.28%, and a false positive rate of 10.86%, which are better than those of other methods.The experimental results show that the proposed method can effectively improve the accuracy of sea fog identification, which is important for marine vessel navigation, fishery production, national defense, and military affairs.

palabra clave

Sea fog monitoring;satellite remote sensing;attention mechanism;Generate adversarial network;Multi-scale feature fusion

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