Research on High-Resolution Reconstruction of Marine Environmental Parameters Using Deep Learning Model

Author:

Hu Yaning1,Ma Liwen1ORCID,Zhang Yushi2,Wu Zhensen3ORCID,Wu Jiaji4ORCID,Zhang Jinpeng2,Zhang Xiaoxiao5

Affiliation:

1. College of Computer Science & Technology, Qingdao University, Qingdao 266071, China

2. China Research Institute of Radiowave Propagation, Qingdao 266107, China

3. School of Physical and Optoelectronic Engineering, Xidian University, Xi’an 710071, China

4. School of Electronic Engneering, Xidian University, Xi’an 710071, China

5. School of Electronic Engneering, Xi’an University of Posts & Telecommunications, Xi’an 710121, China

Abstract

The analysis of marine environmental parameters plays a significant role in various aspects, including sea surface target detection, the monitoring of the marine ecological environment, marine meteorology and disaster forecasting, and the monitoring of internal waves in the ocean. In particular, for sea surface target detection, the accurate and high-resolution input of marine environmental parameters is crucial for multi-scale sea surface modeling and the prediction of sea clutter characteristics. In this paper, based on the low-resolution wind speed, significant wave height, and wave period data provided by ECMWF for the surrounding seas of China (specified latitude and longitude range), a deep learning model based on a residual structure is proposed. By introducing an attention module, the model effectively addresses the poor modeling performance of traditional methods like nearest neighbor interpolation and linear interpolation at the edge positions in the image. Experimental results demonstrate that with the proposed approach, when the spatial resolution of wind speed increases from 0.5° to 0.25°, the results achieve a mean square error (MSE) of 0.713, a peak signal-to-noise ratio (PSNR) of 49.598, and a structural similarity index measure (SSIM) of 0.981. When the spatial resolution of the significant wave height increases from 1° to 0.5°, the results achieve a MSE of 1.319, a PSNR of 46.928, and an SSIM of 0.957. When the spatial resolution of the wave period increases from 1° to 0.5°, the results achieve a MSE of 2.299, a PSNR of 44.515, and an SSIM of 0.940. The proposed method can generate high-resolution marine environmental parameter data for the surrounding seas of China at any given moment, providing data support for subsequent sea surface modeling and for the prediction of sea clutter characteristics.

Funder

National Natural Science Foundation of China

Foundation of the National Key Laboratory of the Electromagnetic Environment of China Electronics Tech-nology Group Corporation

Natural Science Foundation of Shaanxi Province, China

Publisher

MDPI AG

Subject

General Earth and Planetary Sciences

Reference49 articles.

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2. Reconstructing High-Resolution Ocean Subsurface and Interior Temperature and Salinity Anomalies From Satellite Observations;Meng;IEEE Trans. Geosci. Remote Sens.,2022

3. Performances of Deep Learning Models for Indian Ocean Wind Speed Prediction;Biswas;Model. Earth Syst. Environ.,2021

4. Luo, Z., Li, Z., Zhang, C., Deng, J., and Qin, T. (2022). Low Observable Radar Target Detection Method within Sea Clutter Based on Correlation Estimation. Remote Sens., 14.

5. A Deep Neural Networks Based Model for Uninterrupted Marine Environment Monitoring;Reddy;Comput. Commun.,2020

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