Masrur et al. (2026) Learning to See More: A Spectral Extension Super-Resolution Framework for Harmonized Satellite-UAS Imagery

Identification

• Journal: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
• Year: 2026
• Date: 2026-01-01
• Authors: Arif Masrur, Carlan Jackson, Paul R. Adler
• DOI: 10.1109/jstars.2026.3667863

Research Groups

[Information not available in the provided text]

Short Summary

This paper proposes a spectral extension super-resolution framework designed to harmonize satellite and Unmanned Aerial System (UAS) imagery, aiming to enhance the utility and detail of combined remote sensing data.

Objective

• To develop and evaluate a novel spectral extension super-resolution framework for the effective harmonization of satellite and UAS imagery, thereby enabling more comprehensive and detailed multi-scale environmental analysis.

Study Configuration

• Spatial Scale: [Information not available in the provided text]
• Temporal Scale: [Information not available in the provided text]

Methodology and Data

• Models used: A "Spectral Extension Super-Resolution Framework" is utilized, likely involving advanced image processing or machine learning techniques. Specific model names are [Information not available in the provided text].
• Data sources: Satellite imagery, UAS (Unmanned Aerial System) imagery.

Main Results

[Information not available in the provided text]

Contributions

[Information not available in the provided text]

Funding

[Information not available in the provided text]

Citation

@article{Masrur2026Learning,
  author = {Masrur, Arif and Olsen, Peder A. and Jackson, Carlan and Adler, Paul R.},
  title = {Learning to See More: A Spectral Extension Super-Resolution Framework for Harmonized Satellite-UAS Imagery},
  journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  year = {2026},
  doi = {10.1109/jstars.2026.3667863},
  url = {https://doi.org/10.1109/jstars.2026.3667863}
}