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Research on SAR Image Target Recognition Method Based on Multi-Dimensional Feature Fusion.

Jiaqi Fang¹, Hemin Sun¹, Hongquan Li¹

¹Air Force Early Warning Academy, Wuhan 430019, China.

Sensors (Basel, Switzerland)

|June 26, 2026

Summary

This study introduces a novel Synthetic Aperture Radar (SAR) target recognition method using multidimensional feature fusion. The approach enhances accuracy by integrating physical and deep features, outperforming existing techniques.

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Area of Science:

Remote Sensing
Computer Vision
Artificial Intelligence

Background:

Synthetic Aperture Radar (SAR) target recognition faces challenges from speckle noise, geometric distortions, and feature coupling.
These limitations hinder comprehensive representation and efficient fusion, impacting recognition accuracy.

Purpose of the Study:

To develop an advanced SAR image target recognition method.
To overcome limitations in feature representation and fusion for improved accuracy.

Main Methods:

A dual-branch hierarchical feature extraction network combining physical prior features and deep convolutional features.
An optimized preprocessing layer for noise suppression and contrast enhancement.
A variance-adaptive weighted fusion layer for dynamic feature contribution balancing.

Keywords:

SAR images adaptive weighting feature extraction hierarchical extraction multi-dimensional fusion target recognition

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Main Results:

The proposed method significantly improves precision, recall, and F1-score by 5%-15% over baseline methods on MSTAR and CETC38-SAR datasets.
Demonstrated robustness and computational efficiency across various pre-trained backbones.
Ablation studies confirmed the effectiveness of the decoupled architecture.

Conclusions:

The multidimensional feature fusion approach effectively addresses SAR target recognition challenges.
The method offers a robust, efficient, and interpretable solution for SAR image analysis.
Validated effectiveness under extended operating conditions.