Red Tide Detection Method Based on a Time Series Fusion Network Model: A Case Study of GOCI Data in the East China Sea
In China’s coastal regions, severe seawater eutrophication has led to frequent occurrences of red tides, causing significant damage to marine fisheries and aquatic resources. Therefore, red tide detection and prediction are of great research importance. Although current deep learning-based red tide...
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2025-05-01
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| author | Tianhong Ding Zhiqiang Xu Yunjie Wang Qinglian Hou Xiangyong Liu Fengshuang Ma |
| author_facet | Tianhong Ding Zhiqiang Xu Yunjie Wang Qinglian Hou Xiangyong Liu Fengshuang Ma |
| author_sort | Tianhong Ding |
| collection | DOAJ |
| description | In China’s coastal regions, severe seawater eutrophication has led to frequent occurrences of red tides, causing significant damage to marine fisheries and aquatic resources. Therefore, red tide detection and prediction are of great research importance. Although current deep learning-based red tide detection methods perform well in detecting single-day red tides, they struggle with continuous multi-day detection due to insufficient mining of temporal features and difficulties in accurately capturing dynamic variations, limiting further improvements in detection accuracy. To address these issues, this study proposes a time-series fusion network model (CSF-RTDNet) for red tide detection using time-continuous GOCI data from the East China Sea. By integrating multi-temporal GOCI data, the model comprehensively captures spatiotemporal characteristics of red tides, enhancing dynamic process modeling. The CSF-RTDNet method improves feature discrimination by introducing NDVI to enhance red tide characteristics and increase separability between red tides and seawater. Additionally, an ECA channel attention mechanism is employed to fully exploit spectral features across different bands for deeper feature extraction. A novel feature extraction module, ASPC-DSC, combines atrous spatial pyramid convolution with depthwise separable convolution to effectively fuse multi-scale contextual features while improving computational efficiency. Furthermore, ConvLSTM is introduced to integrate temporal and spatial features, effectively addressing the insufficient mining of sequential characteristics in multi-day red tide detection. Experimental results demonstrate that CSF-RTDNet achieves robust detection of red tides with complex boundaries and continuous temporal patterns, attaining an accuracy of 95.89%, precision of 93.03%, recall of 96.34%, and a Kappa coefficient of 0.95. This method significantly enhances red tide detection accuracy and provides valuable technical support for marine environmental monitoring. |
| format | Article |
| id | doaj-art-59442fe9799a4bd68ed8e6c8688cfeb5 |
| institution | Kabale University |
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| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-59442fe9799a4bd68ed8e6c8688cfeb52025-08-20T03:46:45ZengMDPI AGSensors1424-82202025-05-012511345510.3390/s25113455Red Tide Detection Method Based on a Time Series Fusion Network Model: A Case Study of GOCI Data in the East China SeaTianhong Ding0Zhiqiang Xu1Yunjie Wang2Qinglian Hou3Xiangyong Liu4Fengshuang Ma5The Fishery Machinery and Instrument Research Institute, Chinese Academy of Fishery Science, Shanghai 200092, ChinaThe Fishery Machinery and Instrument Research Institute, Chinese Academy of Fishery Science, Shanghai 200092, ChinaThe Fishery Machinery and Instrument Research Institute, Chinese Academy of Fishery Science, Shanghai 200092, ChinaThe Fishery Machinery and Instrument Research Institute, Chinese Academy of Fishery Science, Shanghai 200092, ChinaThe Fishery Machinery and Instrument Research Institute, Chinese Academy of Fishery Science, Shanghai 200092, ChinaThe Fishery Machinery and Instrument Research Institute, Chinese Academy of Fishery Science, Shanghai 200092, ChinaIn China’s coastal regions, severe seawater eutrophication has led to frequent occurrences of red tides, causing significant damage to marine fisheries and aquatic resources. Therefore, red tide detection and prediction are of great research importance. Although current deep learning-based red tide detection methods perform well in detecting single-day red tides, they struggle with continuous multi-day detection due to insufficient mining of temporal features and difficulties in accurately capturing dynamic variations, limiting further improvements in detection accuracy. To address these issues, this study proposes a time-series fusion network model (CSF-RTDNet) for red tide detection using time-continuous GOCI data from the East China Sea. By integrating multi-temporal GOCI data, the model comprehensively captures spatiotemporal characteristics of red tides, enhancing dynamic process modeling. The CSF-RTDNet method improves feature discrimination by introducing NDVI to enhance red tide characteristics and increase separability between red tides and seawater. Additionally, an ECA channel attention mechanism is employed to fully exploit spectral features across different bands for deeper feature extraction. A novel feature extraction module, ASPC-DSC, combines atrous spatial pyramid convolution with depthwise separable convolution to effectively fuse multi-scale contextual features while improving computational efficiency. Furthermore, ConvLSTM is introduced to integrate temporal and spatial features, effectively addressing the insufficient mining of sequential characteristics in multi-day red tide detection. Experimental results demonstrate that CSF-RTDNet achieves robust detection of red tides with complex boundaries and continuous temporal patterns, attaining an accuracy of 95.89%, precision of 93.03%, recall of 96.34%, and a Kappa coefficient of 0.95. This method significantly enhances red tide detection accuracy and provides valuable technical support for marine environmental monitoring.https://www.mdpi.com/1424-8220/25/11/3455red tideNDVItime-series samplesASPC-DSCCSF-RTDNetConvLSTM |
| spellingShingle | Tianhong Ding Zhiqiang Xu Yunjie Wang Qinglian Hou Xiangyong Liu Fengshuang Ma Red Tide Detection Method Based on a Time Series Fusion Network Model: A Case Study of GOCI Data in the East China Sea Sensors red tide NDVI time-series samples ASPC-DSC CSF-RTDNet ConvLSTM |
| title | Red Tide Detection Method Based on a Time Series Fusion Network Model: A Case Study of GOCI Data in the East China Sea |
| title_full | Red Tide Detection Method Based on a Time Series Fusion Network Model: A Case Study of GOCI Data in the East China Sea |
| title_fullStr | Red Tide Detection Method Based on a Time Series Fusion Network Model: A Case Study of GOCI Data in the East China Sea |
| title_full_unstemmed | Red Tide Detection Method Based on a Time Series Fusion Network Model: A Case Study of GOCI Data in the East China Sea |
| title_short | Red Tide Detection Method Based on a Time Series Fusion Network Model: A Case Study of GOCI Data in the East China Sea |
| title_sort | red tide detection method based on a time series fusion network model a case study of goci data in the east china sea |
| topic | red tide NDVI time-series samples ASPC-DSC CSF-RTDNet ConvLSTM |
| url | https://www.mdpi.com/1424-8220/25/11/3455 |
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