A method and system for fast decision of aeroglow gravity wave image of ground base

By employing a hierarchical transfer learning model and data augmentation strategies, the problem of real-time gravity wave identification in short-term deployment of ground-based airglow imagers was solved, achieving high-precision, low-latency gravity wave identification, which is suitable for ground-based airglow imaging equipment.

CN121053596BActive Publication Date: 2026-04-17NAT SPACE SCI CENT CAS
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Patent Information

Application Number
CN202511152731.2
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-08-18
Publication Date
2026-04-17
Estimated Expiration
2045-08-18

AI Technical Summary

Technical Problem

Ground-based airglow imagers have difficulty automatically identifying gravity waves in short-term deployments, and traditional methods are prone to overfitting under small sample conditions and cannot meet the needs of real-time monitoring.

Method used

A hierarchical transfer learning model is adopted, which combines a pre-trained EfficientNetB3 model, deformable convolutional layers and a classification head. Through data augmentation and two-stage optimization training, real-time recognition of gravity waves is achieved.

Benefits of technology

Achieve high-precision, low-latency real-time gravity wave identification under limited sample conditions, simplify model complexity, and be suitable for short-range ground-based airglow imaging equipment.

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Abstract

The present application relates to a kind of ground airglow gravity wave image fast determination method and system, method includes: collecting real-time ground airglow image;After the real-time ground airglow image is preprocessed, input layered transfer learning model, output gravity wave existence probability, and save wave image, wherein, the layered transfer learning model is obtained by segment training to ground airglow image dataset, the layered transfer learning model includes pre-training EfficientNetB3 model, deformable convolution layer and classification head.The present application solves the problem that ground airglow imager is difficult to automatically distinguish gravity wave in short-term deployment in real time, reduces artificial expert discrimination workload, while avoiding the problem of overfitting low accuracy of traditional machine learning method in short time small amount of data training.Reach the purpose of rapid deployment, efficient identification.
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