Sea Surface Wind Field Image Reconstruction Based On U-net Convolution Neural Network

Sea surface wind field refers to the distribution of local wind speed?wind direction and other relevant factors on the ocean surface in a specific area per unit time.The change of sea surface wind field directly affects the normal conduct of marine activities such as marine shipping?marine engineering construction and marine fishing and aquaculture.Therefore,it is of great significance to accurately monitor and analyze sea surface wind field for oceanography and meteorology research.Traditional monitoring of sea surface wind field is mainly carried out by buoys,ships and automatic meteorological monitoring instruments,etc.However,these traditional monitoring methods have disadvantages such as high instrument consumption and sparse distribution,which leads to uneven spatial and temporal distribution of observation data.With the rapid development of remote sensing and computer technology,the inversion of sea surface wind field using satellite remote sensing technology has become the main monitoring method of sea surface wind field.This method has the advantages of wide coverage,accurate and synchronous monitoring and all-weather observation,which can effectively solve the problem of the lack of conventional sea surface wind field data.Current satellite inversion results of the sea surface wind field are usually presented in the form of images.However,during the reconstruction of the sea surface wind field images,we found that the frequent precipitation process on the sea surface would have a great impact on the image reconstruction.Therefore,this paper explored how to reconstruct the sea surface wind field images with high quality,the image area affected by precipitation can be accurately restored.Traditional image reconstruction methods are mainly based on different interpolation methods and global variational methods,but these methods are easy to ignore the local differences in the image,resulting in low precision,large error and low degree of automation of the output results after image reconstruction.With the development of deep learning methods,the use of convolution neural network(convolution neural network)for image reconstruction has become the mainstream method in this field.In particular,we have noticed that the unique U-shaped structure of U-NET convolution neural network can combine the information of each layer under sampling and the input information of the upper sampling to restore the details of the image and improve the accuracy of image reconstruction.Therefore,this paper proposes an image reconstruction method of sea surface wind field based on U-Net convolution neural network,which integrates the idea of partial convolution into the process of image encoding and decoding,effectively extracting semantic feature information of the reconstruction of missing areas of irregular shapes in the sea surface wind field image,and improves the accuracy of the reconstruction of sea surface wind field image.In order to verify the effectiveness of the image reconstruction method of sea surface wind field based on U-NET convolution neural network proposed in this paper,firstly,this paper collected a sea surface wind field image datasets(a total of 10,464 images)under the longitude and latitude grid of east longitude 96°-75.127° and north latitude 16°-47.75°,and conducted data preprocessing on this image data set,including: Data cleaning and screening,longitude and latitude grid division and data transformation.Then,on this data set,the proposed method of sea surface wind field image reconstruction based on U-Net convolution neural network is compared with the traditional methods such as fixed value replacement,linear interpolation and global variable grade.The experimental results show that,The proposed method is superior to other traditional methods in the evaluation indexes of image reconstruction methods such as root mean square error(RMSE),peak signal-to-noise ratio(PSNR)and structural similarity.Furthermore,we also present the visualization results of image reconstruction of sea surface wind field.It can also be found that compared with other traditional image reconstruction methods,the method proposed in this paper has better image reconstruction effect and better results.