Meteorological Radar Noise Image Semantic Segmentation Method Based on Deep Convolutional Neural Network
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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。Abstract: Considering the problem that the scattering echo image of the new generation Doppler meteorological radar is reduced by the noise echoes such as non-rainfall, the accuracy of the refined short-term weather forecast is reduced. A method for semantic segmentation of meteorological radar noise image based on Deep Convolutional Neural Network(DCNN) is proposed. Firstly, a Deep Convolutional Neural Network Model (DCNNM) is designed. The training set data of the MJDATA data set are used for training, and the feature is extracted by the forward propagation process, and the high-dimensional global semantic information of the image is merged with the local feature details. Then, the network parameters are updated by using the training error value back propagation iteration to optimize the convergence effect of the model. Finally, the meteorological radar image data are segmented by the model. The experimental results show that the proposed method has better denoising effect on meteorological radar images, and compared with the optical flow method and the Fully Convolutional Networks (FCN), the method has high recognition accuracy for meteorological radar image real echo and noise echo, and the image pixel precision is high.
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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 5～6 10～30 涓流回波 分布比较均匀的半圆形回波，范围较大，边缘清晰 6～7 5～15 低空昆虫回波 分布不均匀的点状回波，范围小，比较分散 2～3 0～10 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 3～4 5～20 
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表 3 气象雷达图像去噪效果交叉验证取值表
像素点 255 128 255 A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 128 C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
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表 4 4类模型测试效果对比(%)
数据集 方法 TERACC NERACC PA MJDATA (5000) 光流法 88.21 59.03 73.39 FCN 91.68 79.61 85.43 光流法+FCN 92.60 73.91 78.17 Model1 93.65 81.65 96.75
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表 5 4类模型测试效果对比(%)
数据集 方法 TERACC NERACC PA MJDATA (7473) DeepLab v3 88.57 81.65 91.75 ShelfNet 86.92 84.34 90.51 Mask R-CNN 89.66 85.20 93.63 Model2 90.40 84.36 92.79
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