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基于深度学习特征融合的天气雷达生物回波提取研究
作者:
作者单位:

1.南京信息工程大学江苏省大气环境与装备技术协同创新中心;2.中国气象局气象探测中心

基金项目:

国家重点研发计划(2018YFC1405703)；国家自然科学基金(51875293)


Research on biological echo extraction of weather radar based on deep learning feature fusion
Author:
Affiliation:

1.Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment TechnologyCICAEET,Nanjing University of Information Science Technology,Nanjing;2.Meteorological Detection Center,China Meteorological Administration,Beijing

Fund Project:

National Key Research and Development Program of China；National Natural Science Foundation of China

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    摘要:

    针对当前天气雷达提取生物回波的网络存在参数量大、效率低以及对大量样本的需求等问题，本文提出了一种改进的生物回波提取网络，命名为 MFF-PSPnet（Multi-Feature Fusion Pyramid Scene Parsing Network）。首先，该网络在 MobilenetV2 主干的基础上引入了注意力机制模块和边缘提取模块，然后对所有特征进行融合识别，本模型从生物回波主体提取和生物回波边缘细节刻画两个方面提升了网络的分割能力。最后，MFF-PSPnet 是一款轻量级网络，参数量相较于一般生物回波提取网络降低了 10.2%，相较于其他模型，该网络对样本容量的需求更低，更适应于小样本环境。通过本模型和其他模型在历史数据上的消融和对比实验，MFF-PSPnet 提取生物回波的准确率达到了 98.1%，IoU 达到了 94.8%，实验结果表明：在新一代中国天气雷达的历史数据中，本文改进的模型能够有效地提取出生物回波，并更好地适应小样本环境，可应用于移动端。这一成果为未来利用天气雷达进行生物习性研究和保护提供了有效的数据和技术支持。

    Abstract:

    The present network for extracting biological echoes from weather radar includes limitations such as a large number of parameters, low efficiency, and the requirement for a large number of samples. This paper offers an improved biological echo extraction network called MFF-PSPnet (Multi-Feature Fusion Pyramid Scene Parsing Network). This network introduces the attention mechanism module and edge extraction module based on the MobilenetV2 backbone, with all features fused for recognition. This modification increases the network's segmentation capabilities in two ways: biological echo main body extraction and biological echo edge detail characterisation. MFF-PSPnet is a lightweight network that uses 10.2% fewer parameters than the conventional biological echo extraction network. Compared to previous models, this network requires less sample capacity and is better suited to small sample situations. On the historical data set, MFF-PSPnet extracted biological echoes with an accuracy of 98.1% and an IoU of 94.8%. The experimental results show that in the historical data of the new generation of Chinese weather radar, the improved model in this work can efficiently extract biological echoes, adapt better to the limited sample environment, and be used to mobile devices. This finding gives useful data and technological support for the future application of weather radar for biological behavior study and protection.

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陈奕伕,邓志良,吴东丽,刘云平,张静.基于深度学习特征融合的天气雷达生物回波提取研究[J].南京信息工程大学学报,,():

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历史
  • 收稿日期:2024-04-18
  • 最后修改日期:2024-08-12
  • 录用日期:2024-08-22

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