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基于深度卷积神经网络的气象雷达噪声图像语义分割方法

杨宏宇 王峰岩

杨宏宇, 王峰岩. 基于深度卷积神经网络的气象雷达噪声图像语义分割方法[J]. 电子与信息学报, 2019, 41(10): 2373-2381. doi: 10.11999/JEIT190098
引用本文: 杨宏宇, 王峰岩. 基于深度卷积神经网络的气象雷达噪声图像语义分割方法[J]. 电子与信息学报, 2019, 41(10): 2373-2381. doi: 10.11999/JEIT190098
Hongyun YANG, Fengyan WANG. Meteorological Radar Noise Image Semantic Segmentation Method Based on Deep Convolutional Neural Network[J]. Journal of Electronics & Information Technology, 2019, 41(10): 2373-2381. doi: 10.11999/JEIT190098
Citation: Hongyun YANG, Fengyan WANG. Meteorological Radar Noise Image Semantic Segmentation Method Based on Deep Convolutional Neural Network[J]. Journal of Electronics & Information Technology, 2019, 41(10): 2373-2381. doi: 10.11999/JEIT190098

基于深度卷积神经网络的气象雷达噪声图像语义分割方法

doi: 10.11999/JEIT190098
基金项目: 国家自然科学基金(U1833107),国家科技重大专项(2012ZX03002002)
详细信息
    作者简介:

    杨宏宇:男,1969年生,博士,教授,研究方向为网络信息安全、图像处理

    王峰岩:男,1993年生,硕士生,研究方向为网络信息安全、图像处理

    通讯作者:

    杨宏宇 yhyxlx@hotmail.com

  • 中图分类号: TN957.52

Meteorological Radar Noise Image Semantic Segmentation Method Based on Deep Convolutional Neural Network

Funds: The National Natural Science Foundation of China (U1833107), The National Science and Technology Major Project (2012ZX03002002)
  • 摘要: 针对新一代多普勒气象雷达的散射回波图像受非降雨等噪声回波干扰导致精细化短时气象预报准确度降低的问题,该文提出一种基于深度卷积神经网络(DCNN)的气象雷达噪声图像语义分割方法。首先,设计一种深度卷积神经网络模型(DCNNM),利用MJDATA数据集的训练集数据进行训练,通过前向传播过程提取特征,将图像高维全局语义信息与局部特征细节融合;然后,利用训练误差值反向传播迭代更新网络参数,实现模型的收敛效果最优化;最后,通过该模型对气象雷达图像数据进行分割处理。实验结果表明,该文方法对气象雷达图像的去噪效果较好,与光流法、全卷积网络(FCN)等方法相比,该文方法对气象雷达图像中真实回波和噪声回波的识别准确率高,图像的像素精度较高。
  • 图  1  气象雷达噪声回波图

    图  2  DCNNM的网络结构

    图  3  手工标注的气象雷达回波图

    图  4  灰度映射后的气象雷达回波图

    图  5  添加空间信息后图像的可视化结果

    图  6  各类模型训练和测试时间的对比结果

    图  7  经过灰度处理的气象雷达图

    图  8  气象雷达图像语义分割效果对比

    表  1  4类噪声回波的特征描述

    噪声回波形状高度(km)强度(dBz)
    逆温层回波分布比较均匀的块状回波,范围较大,边缘清晰5~610~30
    涓流回波分布比较均匀的半圆形回波,范围较大,边缘清晰6~75~15
    低空昆虫回波分布不均匀的点状回波,范围小,比较分散2~30~10
    形态学噪声回波分布不均匀的点状或片状回波,范围较小,比较分散3~45~20
    下载: 导出CSV

    表  2  模型训练参数设置

    训练参数参数取值
    网络学习率10–8
    权重衰减系数0.001
    momentum系数0.91
    感知屏蔽数量0.5
    批处理大小4
    网络最大迭代次数10000
    下载: 导出CSV

    表  3  气象雷达图像去噪效果交叉验证取值表

    像素点255128
    255A手工标注为降雨的像素点,并且机器去噪也标注为降雨的像素点B手工标注为噪声的像素点,但是机器去噪标注为降雨的像素点
    128C手工标注为降雨的像素点,但是机器去噪标注为噪声的像素点D手工标注为噪声的像素点,并且机器去噪也标注为噪声的像素点
    下载: 导出CSV

    表  4  4类模型测试效果对比(%)

    数据集方法TERACCNERACCPA
    MJDATA (5000)光流法88.2159.0373.39
    FCN91.6879.6185.43
    光流法+FCN92.6073.9178.17
    Model193.6581.6596.75
    下载: 导出CSV

    表  5  4类模型测试效果对比(%)

    数据集方法TERACCNERACCPA
    MJDATA (7473)DeepLab v388.5781.6591.75
    ShelfNet86.9284.3490.51
    Mask R-CNN89.6685.2093.63
    Model290.4084.3692.79
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-02-17
  • 修回日期:  2019-06-04
  • 网络出版日期:  2019-06-10
  • 刊出日期:  2019-10-01

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