基于CCD图像的海浪波高反演方法探索OACSCD
Research on sea wave height estimation methods utilizing CCD imaging
本文基于深度学习方法,利用工业电荷耦合器件(Charge Coupled Device,CCD)摄像机拍摄的海浪图像及对应测波雷达获取的有效波高数据,开展了海浪有效波高的反演研究.为准确提取图像中的有效波高信息,对图像进行了倾斜校正,并将校正图像输入改进后的 EfficientNetB7 模型进行反演.反演实验结果表明,本文提出的方法较可行,对比 ResNet152、InceptionV3、DenseNet264 等传统卷积神经网络模型,反演精度更高.通过这一方法,本文探索并验证了深度学习技术在复杂海况下进行波高反演的潜力,为相关领域的研究提供了新的技术路径.
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.
丁辰;王瑞富;孟俊敏
山东科技大学,山东 青岛 266590||自然资源部第一海洋研究所,山东 青岛 266061山东科技大学,山东 青岛 266590自然资源部第一海洋研究所,山东 青岛 266061
信息技术与安全科学
有效波高反演卷积神经网络海浪图像透视变换图像回归
significant wave height inversionconvolutional neural networksocean wave imagesperspective transformationimage regression
《海洋科学》 2025 (8)
1-10,10
国家基金重点项目(U2006207) National Natural Science Foundation of China,No.U2006207
评论