A Multi-Feature Fusion Approach for Sea Fog Detection Under Complex Background
Sea fog is a natural phenomenon that significantly reduces visibility, posing navigational hazards for ships and impacting coastal activities. Geostationary meteorological satellite data have proven to be indispensable for sea fog monitoring due to their large spatial coverage and spatiotemporal con...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/14/2409 |
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| author | Shuyuan Yang Yuzhu Tang Zeming Zhou Xiaofeng Zhao Pinglv Yang Yangfan Hu Ran Bo |
| author_facet | Shuyuan Yang Yuzhu Tang Zeming Zhou Xiaofeng Zhao Pinglv Yang Yangfan Hu Ran Bo |
| author_sort | Shuyuan Yang |
| collection | DOAJ |
| description | Sea fog is a natural phenomenon that significantly reduces visibility, posing navigational hazards for ships and impacting coastal activities. Geostationary meteorological satellite data have proven to be indispensable for sea fog monitoring due to their large spatial coverage and spatiotemporal consistency. However, the spectral similarities between sea fog and low clouds result in omissions and misclassifications. Furthermore, high clouds obscure certain sea fog regions, leading to under-detection and high false alarm rates. In this paper, we present a novel sea fog detection method to alleviate the challenges. Specifically, the approach leverages a fusion of spectral, motion, and spatiotemporal texture consistency features to effectively differentiate sea fog and low clouds. Additionally, a multi-scale self-attention module is incorporated to recover the sea fog region obscured by clouds. Based on the spatial distribution characteristics of sea fog and clouds, we redesigned the loss function to integrate total variation loss, focal loss, and dice loss. Experimental results validate the effectiveness of the proposed method, and the detection accuracy is compared with the vertical feature mask produced by the CALIOP and exhibits a high level of consistency. |
| format | Article |
| id | doaj-art-6f9a225f4d8340f5ba35268e4eea6d4d |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-6f9a225f4d8340f5ba35268e4eea6d4d2025-08-20T03:32:15ZengMDPI AGRemote Sensing2072-42922025-07-011714240910.3390/rs17142409A Multi-Feature Fusion Approach for Sea Fog Detection Under Complex BackgroundShuyuan Yang0Yuzhu Tang1Zeming Zhou2Xiaofeng Zhao3Pinglv Yang4Yangfan Hu5Ran Bo6College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, ChinaCollege of Intelligent Science and Control Engineering, Jinling Institute of Technology, Nanjing 211169, ChinaCollege of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, ChinaCollege of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, ChinaCollege of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, ChinaCollege of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, ChinaCollege of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, ChinaSea fog is a natural phenomenon that significantly reduces visibility, posing navigational hazards for ships and impacting coastal activities. Geostationary meteorological satellite data have proven to be indispensable for sea fog monitoring due to their large spatial coverage and spatiotemporal consistency. However, the spectral similarities between sea fog and low clouds result in omissions and misclassifications. Furthermore, high clouds obscure certain sea fog regions, leading to under-detection and high false alarm rates. In this paper, we present a novel sea fog detection method to alleviate the challenges. Specifically, the approach leverages a fusion of spectral, motion, and spatiotemporal texture consistency features to effectively differentiate sea fog and low clouds. Additionally, a multi-scale self-attention module is incorporated to recover the sea fog region obscured by clouds. Based on the spatial distribution characteristics of sea fog and clouds, we redesigned the loss function to integrate total variation loss, focal loss, and dice loss. Experimental results validate the effectiveness of the proposed method, and the detection accuracy is compared with the vertical feature mask produced by the CALIOP and exhibits a high level of consistency.https://www.mdpi.com/2072-4292/17/14/2409sea fog detectioncomplex backgroundsmulti-feature fusionmulti-scale self-attention |
| spellingShingle | Shuyuan Yang Yuzhu Tang Zeming Zhou Xiaofeng Zhao Pinglv Yang Yangfan Hu Ran Bo A Multi-Feature Fusion Approach for Sea Fog Detection Under Complex Background Remote Sensing sea fog detection complex backgrounds multi-feature fusion multi-scale self-attention |
| title | A Multi-Feature Fusion Approach for Sea Fog Detection Under Complex Background |
| title_full | A Multi-Feature Fusion Approach for Sea Fog Detection Under Complex Background |
| title_fullStr | A Multi-Feature Fusion Approach for Sea Fog Detection Under Complex Background |
| title_full_unstemmed | A Multi-Feature Fusion Approach for Sea Fog Detection Under Complex Background |
| title_short | A Multi-Feature Fusion Approach for Sea Fog Detection Under Complex Background |
| title_sort | multi feature fusion approach for sea fog detection under complex background |
| topic | sea fog detection complex backgrounds multi-feature fusion multi-scale self-attention |
| url | https://www.mdpi.com/2072-4292/17/14/2409 |
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