Automatic extraction of green tide in areas with clouds or solar flares in HY-1C/D CZI multispectral images

Wu Ke; Wang Changying; Huang Rui; Li Huawei

doi:10.12284/hyxb2023151

Volume 45 Issue 10

Oct. 2023

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Wu Ke，Wang Changying，Huang Rui, et al. 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

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Automatic extraction of green tide in areas with clouds or solar flares in HY-1C/D CZI multispectral images

doi: 10.12284/hyxb2023151

Wu Ke^1
,,
Wang Changying^{1
,
,},
Huang Rui¹,
Li Huawei¹

School of Computer Science and Technology, Qingdao University, Qingdao 266071, China

Received Date: 2023-02-15
Rev Recd Date: 2023-06-13

Available Online: 2023-12-27

Publish Date: 2023-10-30

Abstract

Abstract

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

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References(32)

References

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