基于深度学习的近岸海浪图像反演有效波高算法研究OA北大核心CSTPCD

The significant wave height is an important factor of offshore ocean observation,and the im-ages taken by the offshore camera can directly reflect the wave height,but the current researches on the significant wave height inversion algorithm based on image are mainly conducted for laboratory experi-ment and universal method is lacked.Based on the deep learning technology,taking the inshore wave of the Xiaomai Island in Qingdao,Shandong Province as an example,based on the wave image and buoy data,the method of inshore wave image inversion effective wave height is studied,and a method of image inversion effective wave height is given.In this method,multiple convolutional networks are used to extract the features of wave images,and full connection networks are used to extract meteorological features such as wind speed.The features are fused as the input of the full connection layer,and finally the effective wave height of the inversion is output.By comparing the inversion results of various models with the buoy observation data,it is found that the inversion ability of the multi-parameter Dense Net121 model is superior to other neural network models,and the average absolute error is 8.97 cm.The research in this paper provides a new technical idea for offshore wave observation.