High-precision sea fog image recognition and prediction method based on ensemble learning

Through integrated learning methods, combined with multi-source data and physical mechanism modeling, high-precision sea fog recognition and prediction are achieved, solving the problems of strong data dependence and insufficient real-time performance in the existing technology, and supporting real-time decision-making of maritime activities.

CN120408381AActive Publication Date: 2025-08-01YUNHAI ZHICHUANG (JIANGSU) TECHNOLOGY CO LTD
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Patent Information

Application Number
CN202510863738.9
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-06-26
Publication Date
2025-08-01
Estimated Expiration
2045-06-26

AI Technical Summary

Technical Problem

The existing sea fog identification and forecasting technologies have problems such as strong data dependence, poor interpretability and insufficient real-time performance, which is difficult to meet the high-precision and real-time needs of maritime activities.

Method used

Using an integrated learning-based method, combining visible satellite remote sensing images, infrared band data and microwave radiometer data, through physical mechanism modeling and multi-source hard fusion, dynamic feedback calibration, high-precision sea fog recognition and prediction results are generated.

Benefits of technology

It realizes high-precision and low-dependence sea fog recognition and prediction, breaks through the limitations of data dependence and insufficient real-time performance of traditional methods, and supports minute-level forecasting and hierarchical control of port waterways.

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Abstract

The invention relates to the field of sea fog image recognition, discloses a high-precision sea fog image recognition and prediction method based on ensemble learning, and aims to improve multi-source data fusion efficiency and reasonable utilization and improve the stability of a model so as to realize accurate recognition of sea fog and more accurate time-space evolution prediction. According to the method, a three-dimensional feature space is constructed through visible light wavelet texture analysis, infrared dynamic temperature correction and microwave liquid water path inversion, an image branch adopts improved DeepLabV3 + to carry out fog region semantic segmentation, and fog region identification and trajectory prediction are realized in combination with a thermodynamic phase change criterion and a fluid mechanics diffusion model. And finally, a high-precision sea fog image identification and prediction system is formed. A black box model is replaced with a physical equation, and the defects that a traditional method is poor in interpretability and insufficient in real-time performance are overcome.
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