基于改进DeepLabv3+网络的气象卫星影像雷暴识别
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贵州省科技基金项目(黔科合基础-ZK[2022]一般245)资助


Thunderstorm Identification in Meteorological Satellite Images Based on an Improved DeepLabv3+ Network
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    摘要:

    为实现像素级雷暴活动范围识别,开展气象静止卫星影像分割研究。以贵州省及周边区域风云静止卫星水汽、长波红外通道(6.25~13.5.5 μm)辐射数据为特征,融合地面甚低频/低频(VLF/LF)闪电监测和星载闪电成像仪(LMI)数据构建标签数据。通过改进 DeepLabv3+语义分割网络并增加训练策略,对静止卫星影像进行雷暴范围识别。结果表明,数据增强、主动学习的自适应采样、Combo Loss组合损失、Ranger21优化器等训练策略可降低小样本训练对网络性能的影响,解决数据不平衡问题;骨干网络提取特征采用MobilenetV2运行速度最快,ResNet_101分割性能最好;引入卷积注意力机制模块可提升模型分割精度和特征提取能力。改进后的 DeepLabv3 + 模型在测试数据集上像素平均准确率为 96.82%,平均交并比 MIoU 为 76.93%,性能优于SegNet、UNet、FCN等其他模型。该研究通过挖掘卫星影像中的雷暴特征信息,提高了对雷暴活动的识别精度,可为下一步引入循环神经网络开展雷暴活动预测奠定基础。

    Abstract:

    To achieve pixel-level classification and identification of thunderstorm activity ranges, research on image segmentation technology of meteorological geostationary satellite images is conducted. Taking Guizhou Province and surrounding areas (24°-30°N, 103°-110°E) as an example, the radiation data of Fengyun geostationary satellite water vapour and long-wave infrared channels (6.25-13.5 μm) are selected as features. By integrating ground-based very low frequency/low frequency (VLF/LF) lightning monitoring and spaceborne Lightning Mapping Imager (LMI) data, labelled data is constructed to establish a deep learning dataset. The improved DeepLabv3+ semantic segmentation network along with added training strategies is used to identify the thunderstorm activity range in geostationary satellite images. The research results show that by adopting deep learning training strategies such as data augmentation, adaptive sampling in active learning, Combo Loss combination loss, and Ranger21 optimiser, the impact of thunderstorm small sample data training on network model performance can be effectively reduced, and the problem of data imbalance can be solved. When further comparing different backbone networks, including MobilenetV2, Xception, ResNet_101, ResNet_50, and HRNetV2-48 for feature extraction, it is found that MobilenetV2 has the fastest running speed while ResNet_101 has the best segmentation performance. In addition, by introducing the convolutional block attention module (CBAM) in the encoder and decoder, the model’s ability to learn target features and fuse information at all levels is greatly enhanced. As a result, both pixel accuracy and average intersection are significantly improved, further enhancing the model’s segmentation accuracy. Through extensive ablation experiments on the test dataset comparing SegNet, UNet, FCN, Lraspp, and the original DeepLabv3+ semantic segmentation network model, it is evident that the improved DeepLabv3+ model is superior to all other models. It achieves a pixel average accuracy of 96.82% and an average intersection over union (MIoU) of 76.93%. This not only showcases its superiority but also to a certain extent addresses the problem of high accuracy but low MIoU of the training model on the test set due to imbalanced sample data. This research extends the RGB three-channel data in image recognition to meteorological multi-dimensional data with more than three channels, aiming to mine thunderstorm characteristic information in satellite images more accurately and efficiently and lay a solid foundation for the next step of applying spatio-temporal recurrent neural networks to thunderstorm activity prediction. This study holds great promise for improving our understanding and prediction of thunderstorm activities.

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吴安坤,郭军成,王强,冷宇.基于改进DeepLabv3+网络的气象卫星影像雷暴识别[J].气象科技,2024,52(6):775~786

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  • 收稿日期:2024-01-22
  • 定稿日期:2024-10-09
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  • 在线发布日期: 2024-12-25
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