Tuo et al. (2026) A Deep Learning Enhanced Atmospheric Correction Algorithm for Chinese GF-1 and GF-6 WFV Images

Identification

• Journal: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
• Year: 2026
• Date: 2026-01-01
• Authors: Tianyu Tuo, Qifan Wang, Yang Cui, Youjun Wang
• DOI: 10.1109/jstars.2026.3655375

Research Groups

Not specified in the provided text.

Short Summary

This paper focuses on the development of a deep learning-enhanced atmospheric correction algorithm tailored for Chinese Gaofen-1 (GF-1) and Gaofen-6 (GF-6) Wide Field of View (WFV) satellite images.

Objective

• To develop and enhance an atmospheric correction algorithm utilizing deep learning methodologies specifically for Chinese Gaofen-1 (GF-1) and Gaofen-6 (GF-6) Wide Field of View (WFV) satellite imagery.

Study Configuration

• Spatial Scale: Implied by satellite imagery (GF-1 and GF-6 WFV), typically covering regional to global scales, but specific coverage is not detailed in the provided text.
• Temporal Scale: Not specified in the provided text.

Methodology and Data

• Models used: Deep learning models for atmospheric correction. Specific model architectures are not detailed.
• Data sources: Chinese Gaofen-1 (GF-1) and Gaofen-6 (GF-6) Wide Field of View (WFV) satellite images.

Main Results

Not specified in the provided text.

Contributions

Not specified in the provided text.

Funding

Not specified in the provided text.

Citation

@article{Tuo2026Deep,
  author = {Tuo, Tianyu and Wang, Qifan and Cui, Yang and Wang, Youjun},
  title = {A Deep Learning Enhanced Atmospheric Correction Algorithm for Chinese GF-1 and GF-6 WFV Images},
  journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  year = {2026},
  doi = {10.1109/jstars.2026.3655375},
  url = {https://doi.org/10.1109/jstars.2026.3655375}
}