Radar quantitative precipitation estimation(QPE)is a key and challenging task for many designs and applications with meteorological purposes.Since the Z-R relation between radar and rain has a number of parameters on ...Radar quantitative precipitation estimation(QPE)is a key and challenging task for many designs and applications with meteorological purposes.Since the Z-R relation between radar and rain has a number of parameters on different areas,and the rainfall varies with seasons,the traditional methods are incapable of achieving high spatial and temporal resolution and thus difficult to obtain a refined rainfall estimation.This paper proposes a radar quantitative precipitation estimation algorithm based on the spatiotemporal network model(ST-QPE),which designs a convolutional time-series network QPE-Net8 and a multi-scale feature fusion time-series network QPE-Net22 to address these limitations.We report on our investigation into contrast reversal experiments with radar echo and rainfall data collected by the Hunan Meteorological Observatory.Experimental results are verified and analyzed by using statistical and meteorological methods,and show that the ST-QPE model can inverse the rainfall information corresponding to the radar echo at a given moment,which provides practical guidance for accurate short-range precipitation nowcasting to prevent and mitigate disasters efficiently.展开更多
We propose an end-to-end dehazing model based on deep learning(CNN network)and uses the dehazing model re-proposed by AOD-Net based on the atmospheric scattering model for dehazing.Compare to the previously proposed d...We propose an end-to-end dehazing model based on deep learning(CNN network)and uses the dehazing model re-proposed by AOD-Net based on the atmospheric scattering model for dehazing.Compare to the previously proposed dehazing network,the dehazing model proposed in this paper make use of the FPN network structure in the field of target detection,and uses five feature maps of different sizes to better obtain features of different proportions and different sub-regions.A large amount of experimental data proves that the dehazing model proposed in this paper is superior to previous dehazing technologies in terms of PSNR,SSIM,and subjective visual quality.In addition,it achieved a good performance in speed by using EfficientNet B0 as a feature extractor.We find that only using high-level semantic features can not effectively obtain all the information in the image.The FPN structure used in this paper can effectively integrate the high-level semantics and the low-level semantics,and can better take into account the global and local features.The five feature maps with different sizes are not simply weighted and fused.In order to keep all their information,we put them all together and get the final features through decode layers.At the same time,we have done a comparative experiment between ResNet with FPN and EfficientNet with BiFPN.It is proved that EfficientNet with BiFPN can obtain image features more efficiently.Therefore,EfficientNet with BiFPN is chosen as our network feature extraction.展开更多
基金This work is supported by the Key Research and Development Program of Hunan Province(No.2019SK2161)the Key Research and Development Program of Hunan Province(No.2016SK2017).
文摘Radar quantitative precipitation estimation(QPE)is a key and challenging task for many designs and applications with meteorological purposes.Since the Z-R relation between radar and rain has a number of parameters on different areas,and the rainfall varies with seasons,the traditional methods are incapable of achieving high spatial and temporal resolution and thus difficult to obtain a refined rainfall estimation.This paper proposes a radar quantitative precipitation estimation algorithm based on the spatiotemporal network model(ST-QPE),which designs a convolutional time-series network QPE-Net8 and a multi-scale feature fusion time-series network QPE-Net22 to address these limitations.We report on our investigation into contrast reversal experiments with radar echo and rainfall data collected by the Hunan Meteorological Observatory.Experimental results are verified and analyzed by using statistical and meteorological methods,and show that the ST-QPE model can inverse the rainfall information corresponding to the radar echo at a given moment,which provides practical guidance for accurate short-range precipitation nowcasting to prevent and mitigate disasters efficiently.
基金the Key Research and Development Program of Hunan Province(No.2019SK2161)the Key Research and Development Program of Hunan Province(No.2016SK2017).
文摘We propose an end-to-end dehazing model based on deep learning(CNN network)and uses the dehazing model re-proposed by AOD-Net based on the atmospheric scattering model for dehazing.Compare to the previously proposed dehazing network,the dehazing model proposed in this paper make use of the FPN network structure in the field of target detection,and uses five feature maps of different sizes to better obtain features of different proportions and different sub-regions.A large amount of experimental data proves that the dehazing model proposed in this paper is superior to previous dehazing technologies in terms of PSNR,SSIM,and subjective visual quality.In addition,it achieved a good performance in speed by using EfficientNet B0 as a feature extractor.We find that only using high-level semantic features can not effectively obtain all the information in the image.The FPN structure used in this paper can effectively integrate the high-level semantics and the low-level semantics,and can better take into account the global and local features.The five feature maps with different sizes are not simply weighted and fused.In order to keep all their information,we put them all together and get the final features through decode layers.At the same time,we have done a comparative experiment between ResNet with FPN and EfficientNet with BiFPN.It is proved that EfficientNet with BiFPN can obtain image features more efficiently.Therefore,EfficientNet with BiFPN is chosen as our network feature extraction.
