Aslam et al. (2026) CGA: Curvature-Guided Attention for Remote Sensing Image Super-Resolution

Identification

• Journal: IEEE Transactions on Geoscience and Remote Sensing
• Year: 2026
• Date: 2026-01-01
• Authors: Muhammad Waleed Aslam, SAMI UL REHMAN, Abu Muaz Muhammad Tayyab, Muhammad Ameer Hamza, Xinwei Li, Yong Li
• DOI: 10.1109/tgrs.2026.3661098

Research Groups

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Short Summary

This paper introduces Curvature-Guided Attention (CGA) to enhance the super-resolution of remote sensing images.

Objective

• To develop and evaluate a novel Curvature-Guided Attention mechanism to improve the quality and detail of super-resolved remote sensing images.

Study Configuration

• Spatial Scale: Image-level processing of remote sensing data.
• Temporal Scale: Static image enhancement.

Methodology and Data

• Models used: A deep learning model incorporating Curvature-Guided Attention (CGA), likely a type of convolutional neural network (CNN) designed for super-resolution.
• Data sources: Remote sensing images (e.g., satellite imagery, aerial photographs).

Main Results

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Contributions

• Introduction of a novel Curvature-Guided Attention mechanism specifically tailored for remote sensing image super-resolution.

Funding

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Citation

@article{Aslam2026CGA,
  author = {Aslam, Muhammad Waleed and REHMAN, SAMI UL and Tayyab, Abu Muaz Muhammad and Hamza, Muhammad Ameer and Li, Xinwei and Li, Yong},
  title = {CGA: Curvature-Guided Attention for Remote Sensing Image Super-Resolution},
  journal = {IEEE Transactions on Geoscience and Remote Sensing},
  year = {2026},
  doi = {10.1109/tgrs.2026.3661098},
  url = {https://doi.org/10.1109/tgrs.2026.3661098}
}