A New Deep Learning Method for Sea Fog Detection Using Fengyun-4A Satellite Data.
Saved in:
| Title: | A New Deep Learning Method for Sea Fog Detection Using Fengyun-4A Satellite Data. |
|---|---|
| Authors: | Cheng, Yinhe1 (AUTHOR) chengyh@jou.edu.cn, Hong, Danyu1,2 (AUTHOR), Ma, Tinghuai1,3 (AUTHOR), Wang, Shuwen1,2 (AUTHOR), Shi, Dawei2,3 (AUTHOR) |
| Source: | Remote Sensing. Jul2026, Vol. 18 Issue 14, p2334. 23p. |
| Subjects: | Deep learning, Multispectral imaging, Meteorological satellites, Marine meteorology, Neural architecture search, Remote sensing |
| Geographic Terms: | China |
| Abstract: | Highlights: What are the main findings? HA-ENASnet improves daytime sea fog detection from FY-4A multispectral data by jointly modeling local textural details and global spatial continuity. The proposed NLA and ENAS-FPN modules enhance daytime cloud–fog discrimination by adaptively integrating spatial and spectral multiscale features. What are the implications of the main findings? The proposed method provides a reliable solution for operational daytime sea fog monitoring under complex conditions, such as cloud–fog mixtures and blurred fog boundaries. The cross-satellite validation indicates that HA-ENASnet has potential applicability to daytime sea fog detection using other geostationary meteorological satellites with similar multispectral observations. Sea fog is a common maritime meteorological hazard that poses a serious threat to maritime traffic safety and coastal economic activities. Existing sea fog detection models still struggle to effectively capture both global structural information and local textural details, while spectral confusion between clouds and fog further limits their performance. To address these challenges, a daytime sea fog detection model, HA-ENASnet, is developed using data from China's Fengyun-4A (FY-4A) satellite and compared with six representative semantic segmentation models. Among the seven models, HA-ENASnet achieves the best performance, followed by SegMamba, scSE_LinkNet, SegMAN, ECA_TransUnet, DeepLabv3+, and UNet++. In terms of Intersection over Union (IoU), HA-ENASnet outperforms SegMamba and UNet++ by 3.08 and 17.26 percentage points, respectively. Ablation experiments for this model demonstrate that the introduction of a collaborative mechanism combining local and global linear attention effectively enhances the model's perception capabilities for sea fog regions. Furthermore, the combination of Adaptive Architecture Search (NAS) and Efficient Channel Attention (ECA) facilitates the adaptive fusion of spectral and spatial multiscale features, thereby improving the model's ability to distinguish between cloud and fog in spectrally confounded regions. Additionally, the proposed model is validated on Himawari-8 satellite data, exhibiting satisfactory detection performance and generalization capabilities. This provides methodological support for sea fog remote sensing monitoring using the FY satellite series. [ABSTRACT FROM AUTHOR] |
| Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| Abstract: | Highlights: What are the main findings? HA-ENASnet improves daytime sea fog detection from FY-4A multispectral data by jointly modeling local textural details and global spatial continuity. The proposed NLA and ENAS-FPN modules enhance daytime cloud–fog discrimination by adaptively integrating spatial and spectral multiscale features. What are the implications of the main findings? The proposed method provides a reliable solution for operational daytime sea fog monitoring under complex conditions, such as cloud–fog mixtures and blurred fog boundaries. The cross-satellite validation indicates that HA-ENASnet has potential applicability to daytime sea fog detection using other geostationary meteorological satellites with similar multispectral observations. Sea fog is a common maritime meteorological hazard that poses a serious threat to maritime traffic safety and coastal economic activities. Existing sea fog detection models still struggle to effectively capture both global structural information and local textural details, while spectral confusion between clouds and fog further limits their performance. To address these challenges, a daytime sea fog detection model, HA-ENASnet, is developed using data from China's Fengyun-4A (FY-4A) satellite and compared with six representative semantic segmentation models. Among the seven models, HA-ENASnet achieves the best performance, followed by SegMamba, scSE_LinkNet, SegMAN, ECA_TransUnet, DeepLabv3+, and UNet++. In terms of Intersection over Union (IoU), HA-ENASnet outperforms SegMamba and UNet++ by 3.08 and 17.26 percentage points, respectively. Ablation experiments for this model demonstrate that the introduction of a collaborative mechanism combining local and global linear attention effectively enhances the model's perception capabilities for sea fog regions. Furthermore, the combination of Adaptive Architecture Search (NAS) and Efficient Channel Attention (ECA) facilitates the adaptive fusion of spectral and spatial multiscale features, thereby improving the model's ability to distinguish between cloud and fog in spectrally confounded regions. Additionally, the proposed model is validated on Himawari-8 satellite data, exhibiting satisfactory detection performance and generalization capabilities. This provides methodological support for sea fog remote sensing monitoring using the FY satellite series. [ABSTRACT FROM AUTHOR] |
|---|---|
| ISSN: | 20724292 |
| DOI: | 10.3390/rs18142334 |
