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FCCD-SAR: A Lightweight SAR ATR Algorithm Based on FasterNet.

Xiang Dong1, Dong Li1, Jiandong Fang1

  • 1The College of Information Engineering, Information and Communication Engineering, Inner Mongol University of Technology, Hohhot 010080, China.

Sensors (Basel, Switzerland)
|August 12, 2023
PubMed
Summary

This study introduces FCCD-SAR, a novel algorithm for synthetic aperture radar (SAR) target recognition. It achieves high accuracy and speed by using a lightweight network, improving remote sensing capabilities.

Keywords:
automatic target recognition (ATR)deep learningfasterNetlightweightsynthetic aperture radar (SAR)

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Area of Science:

  • Remote Sensing
  • Artificial Intelligence
  • Computer Vision

Background:

  • Deep learning models for synthetic aperture radar (SAR) target recognition often prioritize network complexity over efficiency.
  • Existing models neglect the crucial balance between accuracy and computational speed in SAR image analysis.

Purpose of the Study:

  • To develop a lightweight and efficient SAR target recognition algorithm, FCCD-SAR, that balances accuracy and speed.
  • To enhance feature extraction and scattering information retrieval for improved SAR target identification.

Main Methods:

  • Developed a lightweight, SAR-specific feature extraction backbone integrated with the FasterNet architecture.
  • Incorporated the CARAFE upsampling operator to enhance scattering information extraction.
  • Utilized a C3-Faster module for improved recognition accuracy and computational efficiency.
  • Implemented a DyHead attention-based detection head to handle multi-scale SAR target variations.

Main Results:

  • The FCCD-SAR algorithm achieved 99.5% mean Average Precision (mAP) on the MSTAR dataset.
  • The model demonstrates high efficiency with only 2.72 million parameters and 6.11 G FLOPs.
  • The proposed methods significantly improved SAR target recognition performance.

Conclusions:

  • FCCD-SAR offers a proficient and computationally efficient solution for SAR target recognition.
  • The algorithm's design effectively addresses the challenges of scale variation and data complexity in SAR imagery.
  • This work advances the application of deep learning in remote sensing for target identification.