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SSDDPM: A single SAR image generation method based on denoising diffusion probabilistic model.

Jinyu Wang1, Haitao Yang1, Zhengjun Liu2

  • 1Space Engineering University, Beijing, 101416, China.

Scientific Reports
|March 29, 2025
PubMed
Summary
This summary is machine-generated.

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This study introduces a novel diffusion model for generating realistic Synthetic Aperture Radar (SAR) images from a single sample. The method enhances target detection and classification accuracy by improving image quality and generation diversity.

Area of Science:

  • Remote Sensing
  • Artificial Intelligence
  • Image Processing

Background:

  • High-quality Synthetic Aperture Radar (SAR) image availability is crucial for accurate target detection, classification, and segmentation.
  • Limited datasets hinder the development and robustness of SAR image analysis algorithms.

Purpose of the Study:

  • To develop a novel image generation method for realistic SAR images using a diffusion model with only one training sample.
  • To improve feature extraction and suppress redundant information in generated SAR images.

Main Methods:

  • A single-scale architecture was employed to prevent noise accumulation during image generation.
  • An attention module was integrated into the generator's sampling layer for enhanced feature extraction.
  • An information-guided attention module was designed to reduce redundant information.
Keywords:
Attention moduleCodec networkDiffusion modelSingle sample generationSynthetic aperture radar

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Main Results:

  • The proposed diffusion model significantly outperforms SinGAN in SAR image generation quality, evidenced by substantial improvements in SIFID, SSIM, and LPIPS metrics.
  • Generation diversity was increased by 27.35% compared to ExSinGAN.
  • The method was validated on both an open-source dataset and a custom-built Sentinel-1 dataset.

Conclusions:

  • The novel diffusion model effectively generates high-quality SAR images from minimal data, addressing the scarcity of training samples.
  • This approach offers a robust solution for enhancing SAR-based target detection, classification, and segmentation tasks.
  • The proposed method demonstrates superior performance and generation diversity compared to existing generative adversarial network-based methods.