Research on High-Resolution Modeling of Satellite-Derived Marine Environmental Parameters Based on Adaptive Global Attention
The analysis of marine environmental parameters plays an important role in areas such as sea surface simulation modeling, analysis of sea clutter characteristics, and environmental monitoring. However, ocean observation remote sensing satellites typically deliver large volumes of data with limited s...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-02-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/17/4/709 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849720112567812096 |
|---|---|
| author | Ruochu Cui Liwen Ma Yaning Hu Jiaji Wu Haiying Li |
| author_facet | Ruochu Cui Liwen Ma Yaning Hu Jiaji Wu Haiying Li |
| author_sort | Ruochu Cui |
| collection | DOAJ |
| description | The analysis of marine environmental parameters plays an important role in areas such as sea surface simulation modeling, analysis of sea clutter characteristics, and environmental monitoring. However, ocean observation remote sensing satellites typically deliver large volumes of data with limited spatial resolution, which often does not meet the precision requirements of practical applications. To overcome challenges in constructing high-resolution marine environmental parameters, this study conducts a systematic comparison of various interpolation techniques and deep learning models, aiming to develop a highly effective and efficient model optimized for enhancing the resolution of marine applications. Specifically, we incorporated adaptive global attention (AGA) mechanisms and a spatial gating unit (SGU) into the model. The AGA mechanism dynamically adjusts the weights of different regions in feature maps, enabling the model to focus more on critical spatial features and channel features. The SGU optimizes the utilization of spatial information by controlling the information transmission pathways. The experimental results indicate that for four types of marine environmental parameters from ERA5, our model achieves an overall PSNR of 44.0705, an SSIM of 0.9947, and an MAE of 0.2606 when the resolution is increased by a upscale factor of 2, as well as an overall PSNR of 35.5215, an SSIM of 0.9732, and an MAE of 0.8330 when the resolution is increased by an upscale factor of 4. These experiments demonstrate the model’s effectiveness in enhancing the spatial resolution of satellite-derived marine environmental parameters and its ability to be applied to any marine region, providing data support for many subsequent oceanic studies. |
| format | Article |
| id | doaj-art-da99dab1f7eb4de292ff9ffaa45b4d3e |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-da99dab1f7eb4de292ff9ffaa45b4d3e2025-08-20T03:12:01ZengMDPI AGRemote Sensing2072-42922025-02-0117470910.3390/rs17040709Research on High-Resolution Modeling of Satellite-Derived Marine Environmental Parameters Based on Adaptive Global AttentionRuochu Cui0Liwen Ma1Yaning Hu2Jiaji Wu3Haiying Li4College of Computer Science & Technology, Qingdao University, Qingdao 266071, ChinaCollege of Computer Science & Technology, Qingdao University, Qingdao 266071, ChinaCollege of Computer Science & Technology, Qingdao University, Qingdao 266071, ChinaSchool of Electronic Engineering, Xidian University, Xi’an 710071, ChinaChina Research Institute of Radiowave Propagation, Qingdao 266107, ChinaThe analysis of marine environmental parameters plays an important role in areas such as sea surface simulation modeling, analysis of sea clutter characteristics, and environmental monitoring. However, ocean observation remote sensing satellites typically deliver large volumes of data with limited spatial resolution, which often does not meet the precision requirements of practical applications. To overcome challenges in constructing high-resolution marine environmental parameters, this study conducts a systematic comparison of various interpolation techniques and deep learning models, aiming to develop a highly effective and efficient model optimized for enhancing the resolution of marine applications. Specifically, we incorporated adaptive global attention (AGA) mechanisms and a spatial gating unit (SGU) into the model. The AGA mechanism dynamically adjusts the weights of different regions in feature maps, enabling the model to focus more on critical spatial features and channel features. The SGU optimizes the utilization of spatial information by controlling the information transmission pathways. The experimental results indicate that for four types of marine environmental parameters from ERA5, our model achieves an overall PSNR of 44.0705, an SSIM of 0.9947, and an MAE of 0.2606 when the resolution is increased by a upscale factor of 2, as well as an overall PSNR of 35.5215, an SSIM of 0.9732, and an MAE of 0.8330 when the resolution is increased by an upscale factor of 4. These experiments demonstrate the model’s effectiveness in enhancing the spatial resolution of satellite-derived marine environmental parameters and its ability to be applied to any marine region, providing data support for many subsequent oceanic studies.https://www.mdpi.com/2072-4292/17/4/709satellite remote sensingmarine environmental parametershigh resolutionadaptive global attentionspatial gating unitdeep learning |
| spellingShingle | Ruochu Cui Liwen Ma Yaning Hu Jiaji Wu Haiying Li Research on High-Resolution Modeling of Satellite-Derived Marine Environmental Parameters Based on Adaptive Global Attention Remote Sensing satellite remote sensing marine environmental parameters high resolution adaptive global attention spatial gating unit deep learning |
| title | Research on High-Resolution Modeling of Satellite-Derived Marine Environmental Parameters Based on Adaptive Global Attention |
| title_full | Research on High-Resolution Modeling of Satellite-Derived Marine Environmental Parameters Based on Adaptive Global Attention |
| title_fullStr | Research on High-Resolution Modeling of Satellite-Derived Marine Environmental Parameters Based on Adaptive Global Attention |
| title_full_unstemmed | Research on High-Resolution Modeling of Satellite-Derived Marine Environmental Parameters Based on Adaptive Global Attention |
| title_short | Research on High-Resolution Modeling of Satellite-Derived Marine Environmental Parameters Based on Adaptive Global Attention |
| title_sort | research on high resolution modeling of satellite derived marine environmental parameters based on adaptive global attention |
| topic | satellite remote sensing marine environmental parameters high resolution adaptive global attention spatial gating unit deep learning |
| url | https://www.mdpi.com/2072-4292/17/4/709 |
| work_keys_str_mv | AT ruochucui researchonhighresolutionmodelingofsatellitederivedmarineenvironmentalparametersbasedonadaptiveglobalattention AT liwenma researchonhighresolutionmodelingofsatellitederivedmarineenvironmentalparametersbasedonadaptiveglobalattention AT yaninghu researchonhighresolutionmodelingofsatellitederivedmarineenvironmentalparametersbasedonadaptiveglobalattention AT jiajiwu researchonhighresolutionmodelingofsatellitederivedmarineenvironmentalparametersbasedonadaptiveglobalattention AT haiyingli researchonhighresolutionmodelingofsatellitederivedmarineenvironmentalparametersbasedonadaptiveglobalattention |