# Quality Assessment of Solar EUV Remote Sensing Images Using Multi-Feature Fusion

## TL;DR

A new method combining deep learning and handcrafted features improves solar EUV image quality assessment, achieving high accuracy for reliable space weather forecasting.

## Contribution

A hybrid framework fusing deep learning and handcrafted features for solar EUV image quality assessment is introduced.

## Key findings

- The fusion of deep and handcrafted features achieved 97.91% accuracy in image quality classification.
- The method offers a scalable solution for automated quality control of solar EUV data streams.
- XGBoost classifier trained on fused features showed an AUC of 0.9992, indicating excellent performance.

## Abstract

What are the main findings?
A novel hybrid framework for assessing solar EUV image quality was developed, combining deep learning features from a HyperNet-based model with 22 handcrafted physical and statistical indicators.The fusion of these feature types significantly improved the performance of image quality classification, achieving a high accuracy of 97.91% and an AUC of 0.9992.
What are the implications of the main findings?
This method provides a robust and scalable solution for the automated quality con-trol of large-scale solar EUV observation data streams, which is crucial for space weather forecasting.The research demonstrates the effectiveness of a multi-feature fusion approach for complex image quality assessment tasks, offering a new direction for similar applica-tions in remote sensing.

A novel hybrid framework for assessing solar EUV image quality was developed, combining deep learning features from a HyperNet-based model with 22 handcrafted physical and statistical indicators.

The fusion of these feature types significantly improved the performance of image quality classification, achieving a high accuracy of 97.91% and an AUC of 0.9992.

This method provides a robust and scalable solution for the automated quality con-trol of large-scale solar EUV observation data streams, which is crucial for space weather forecasting.

The research demonstrates the effectiveness of a multi-feature fusion approach for complex image quality assessment tasks, offering a new direction for similar applica-tions in remote sensing.

Accurate quality assessment of solar Extreme Ultraviolet (EUV) remote sensing imagery is critical for data reliability in space science and weather forecasting. This study introduces a hybrid framework that fuses deep semantic features from a HyperNet-based model with 22 handcrafted physical and statistical quality indicators to create a robust 24-dimensional feature vector. We used a dataset of top-quality images, i.e., quality class “Excellent”, and generated a dataset of 47,950 degraded, lower-quality images by simulating seven types of degradation including defocus, blur and noise. Experimental results show that an XGBoost classifier, when trained on these fused features, achieved superior performance with 97.91% accuracy and an AUC of 0.9992. This approach demonstrates that combining deep and handcrafted features significantly enhances the classification’s robustness and offers a scalable solution for automated quality control in solar EUV observation pipelines.