Han et al. (2025) SAR-Conditioned Consistency Model for Effective Cloud Removal in Remote Sensing Images

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Remote Sensing
• Year: 2025
• Date: 2025-11-14
• Authors: Qizhuo Han, Bo Huang, Ying Li
• DOI: 10.3390/rs17223721

Research Groups

[Not explicitly mentioned in the provided text.]

Short Summary

This paper proposes CM-CR, a fast-sampling SAR-conditioned consistency model, to simultaneously enhance reconstruction quality and sampling efficiency for cloud removal in optical remote sensing imagery. The method achieves state-of-the-art performance in image quality and delivers up to a 40-fold acceleration in inference speed.

Objective

• To develop a method that simultaneously enhances reconstruction quality and sampling efficiency for SAR-assisted deep learning cloud removal in optical remote sensing imagery.

Study Configuration

• Spatial Scale: Imagery-based, processing individual scenes at pixel level.
• Temporal Scale: Not explicitly mentioned; the focus is on single-instance image generation rather than time-series analysis.

Methodology and Data

• Models used:
  • SAR-Conditioned Consistency Model based on Consistency Distillation (CM-CR)
  • Teacher network: SAR-Conditioned Score-Based Diffusion Model (SCSBD)
  • Student network: SAR-Conditioned Consistency Model (SCCM)
  • Progressive Denoising via Multistep Resampling (PDMSR) strategy
• Data sources:
  • SEN12MS-CR cloud removal benchmark dataset (comprising SAR and optical imagery).

Main Results

• The proposed CM-CR method achieves state-of-the-art (SOTA) performance across all image quality metrics on the SEN12MS-CR dataset.
• CM-CR delivers up to a 40-fold acceleration at inference compared to a standard Denoising Diffusion Probabilistic Model (DDPM).
• CM-CR's design uses approximately 80 times more parameters compared with a standard Denoising Diffusion Probabilistic Model (DDPM).

Contributions

• Introduction of CM-CR, a novel fast-sampling SAR-conditioned consistency model that significantly improves both reconstruction quality and sampling efficiency for cloud removal.
• Development of a teacher–student architecture (SCSBD as teacher, SCCM as student) that divides the reconstruction process into rapid coarse prediction and detailed refinement stages.
• Proposal of the Progressive Denoising via Multistep Resampling (PDMSR) strategy for iterative refinement of single-step outputs, leading to fine-grained reconstructions.
• Demonstrates a substantial acceleration in inference speed (up to 40-fold) while achieving SOTA image quality in SAR-assisted cloud removal.

Funding

[Not explicitly mentioned in the provided text.]

Citation

@article{Han2025SARConditioned,
  author = {Han, Qizhuo and Huang, Bo and Li, Ying},
  title = {SAR-Conditioned Consistency Model for Effective Cloud Removal in Remote Sensing Images},
  journal = {Remote Sensing},
  year = {2025},
  doi = {10.3390/rs17223721},
  url = {https://doi.org/10.3390/rs17223721}
}