Çağlayan et al. (2026) SAR-W-MixMAE: Polarization-Aware Self-Supervised Pretraining for Masked Autoencoders on SAR Data

Identification

• Journal: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
• Year: 2026
• Date: 2026-01-01
• Authors: Ali Çağlayan, Nevrez İmamoğlu, T. Kouyama
• DOI: 10.1109/jstars.2026.3652404

Research Groups

Not available in the provided text.

Short Summary

Not available in the provided text.

Objective

• To develop and evaluate SAR-W-MixMAE, a polarization-aware self-supervised pretraining method for masked autoencoders, specifically designed for Synthetic Aperture Radar (SAR) data.

Study Configuration

• Spatial Scale: Not available in the provided text.
• Temporal Scale: Not available in the provided text.

Methodology and Data

• Models used: SAR-W-MixMAE, Masked Autoencoders (MAE)
• Data sources: Synthetic Aperture Radar (SAR) data

Main Results

Not available in the provided text.

Contributions

Not available in the provided text.

Funding

Not available in the provided text.

Citation

@article{Çağlayan2026SARWMixMAE,
  author = {Çağlayan, Ali and İmamoğlu, Nevrez and Kouyama, T.},
  title = {SAR-W-MixMAE: Polarization-Aware Self-Supervised Pretraining for Masked Autoencoders on SAR Data},
  journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  year = {2026},
  doi = {10.1109/jstars.2026.3652404},
  url = {https://doi.org/10.1109/jstars.2026.3652404}
}