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Automatic extraction of green tide in areas with clouds or solar flares in HY-1C/D CZI multispectral images
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Ke Wu1, Changying Wang1, *, Rui Huang1, Huawei Li1
Haiyang Xuebao | 2023, 45(10) : 168 - 182
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Haiyang Xuebao | 2023, 45(10): 168-182
Article
Automatic extraction of green tide in areas with clouds or solar flares in HY-1C/D CZI multispectral images
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Ke Wu1, Changying Wang1, *, Rui Huang1, Huawei Li1
Affiliations
  • 1School of Computer Science and Technology, Qingdao University, Qingdao 266071, China
Published: 2023-10-01 doi: 10.12284/hyxb2023151
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Multispectral images are greatly affected by factors such as clouds, fog, and solar flares, which makes it difficult to automatically extract high-precision green tides under complex weather conditions. Based on the multi-spectral images of my country’s HY-1C/D satellite CZI payload, using data mining technology to explore the difference in data distribution between green tide areas and non-green tide areas, we propose a high-precision and fully automatic green tide extraction method , which can be applied to HY-1C/D CZI sensor data. First of all, the thick cloud area is removed by preliminary extraction rules to achieve preliminary classification. Then, the correctly classified green tide samples and non-green tide samples were used as positive and negative samples respectively, and these samples were used as experimental data to train the decision tree model, and the automatic extraction rules of green tide were obtained according to the model. Finally, 5 strategies for correcting misclassifications were designed to achieve fully automatic extraction of green tides. In order to verify the effectiveness of the method, we collected 25 images of the green tide outbreak period in the Yellow Sea in 2021 for automatic detection experiments, and compared the experimental results with traditional index methods (NDVI, VB-FAH) and deep learning methods (ResNet50, U-Net). The results showed that the method outperformed other methods in terms of accuracy, Kappa coefficient, F1-Score, and MIoU. The accuracy of green tide extraction was higher in areas with thick clouds, thin clouds, cloudless clouds, cloud spots, and flares.

HY-1C/D satellite  /  green tide extraction  /  decision tree  /  solar flare  /  cloud cover
Ke Wu, Changying Wang, Rui Huang, Huawei Li. Automatic extraction of green tide in areas with clouds or solar flares in HY-1C/D CZI multispectral images[J]. Haiyang Xuebao, 2023 , 45 (10) : 168 -182 . DOI: 10.12284/hyxb2023151
Year 2023 volume 45 Issue 10
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doi: 10.12284/hyxb2023151
  • Receive Date:2023-02-15
  • Online Date:2025-12-28
  • Published:2023-10-01
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  • Received:2023-02-15
  • Revised:2023-06-13
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    1School of Computer Science and Technology, Qingdao University, Qingdao 266071, China
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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