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Research on SAR image quality evaluation method based on improved harris hawk optimization algorithm and XGBoost.

Min Huang^1,2, Hang Zhao², Yazhou Chen³

¹Army Engineering University, Shijiazhuang Campus, Shijiazhuang, 050003, China.

Scientific Reports

|November 17, 2024

Summary

This summary is machine-generated.

This study introduces a new method for evaluating Synthetic Aperture Radar (SAR) image quality using objective and subjective data. The developed IHHO-XGBoost model significantly improves SAR image quality assessment for better target detection.

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Area of Science:

Remote Sensing
Artificial Intelligence
Image Processing

Background:

Synthetic Aperture Radar (SAR) is vital for military reconnaissance and remote sensing.
SAR image quality directly impacts target detection performance, necessitating pre-evaluation.
Existing methods may not fully address the complexities of SAR image quality assessment.

Purpose of the Study:

To propose a comprehensive SAR image quality evaluation method.
To develop an optimized machine learning model for accurate SAR image quality assessment.
To enhance target detection accuracy and efficiency through improved image quality evaluation.

Main Methods:

Generated disturbed SAR images and calculated objective quality indicators on the SAR Ship Detection Dataset (SSDD).
Assigned subjective quality labels to images through human evaluation to create a comprehensive dataset.
Developed the IHHO-XGBoost model, optimizing extreme gradient boosting (XGBoost) hyperparameters with an improved Harris Hawk Optimization (IHHO) algorithm.

Main Results:

The IHHO algorithm demonstrated superior performance over traditional HHO by avoiding local optima and maintaining population diversity.
Comparative experiments confirmed the superiority of the IHHO-XGBoost model in SAR image quality evaluation.
The proposed method showed scientific validity and practical applicability.

Conclusions:

The developed comprehensive SAR image quality evaluation method is effective.
The IHHO-XGBoost model provides a powerful tool for SAR image quality assessment.
This research offers advancements for SAR image quality research and applications.