海洋科学2025，Vol.49Issue(8)：1-10,10.DOI:10.11759/hykx20241211001
基于CCD图像的海浪波高反演方法探索
Research on sea wave height estimation methods utilizing CCD imaging
摘要
Abstract
This study investigates a deep learning-based method for retrieving significant wave height(SWH)using wave im-ages captured by an industrial charge-coupled device(CCD)camera and corresponding SWH measurements from a wave radar.To accurately extract SWH information,the images were first tilt-corrected and then input into an improved EfficientNetB7 model for inversion.Experimental results demonstrate that the proposed method is highly feasible and achieves superior inver-sion accuracy compared with traditional convolutional neural network models,including ResNet152,InceptionV3,and Dense-Net264.This study explores and validates the potential of deep learning techniques for SWH inversion under complex sea con-ditions,providing a new technical pathway for related research.关键词
有效波高反演/卷积神经网络/海浪图像/透视变换/图像回归Key words
significant wave height inversion/convolutional neural networks/ocean wave images/perspective transformation/image regression分类
信息技术与安全科学引用本文复制引用
丁辰,王瑞富,孟俊敏..基于CCD图像的海浪波高反演方法探索[J].海洋科学,2025,49(8):1-10,10.基金项目
国家基金重点项目(U2006207) National Natural Science Foundation of China,No.U2006207 （U2006207）
