Zhou et al. (2026) Physics-Guided Polarimetric Feature Selection Based on Dual-Branch Networks for PolSAR Agriculture Field Classification

Identification

• Journal: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
• Year: 2026
• Date: 2026-01-01
• Authors: Yueyang Zhou, Junjun Yin, Jian Yang
• DOI: 10.1109/jstars.2026.3652841

Research Groups

[Not specified in the provided text.]

Short Summary

This paper proposes a method for classifying agricultural fields using Polarimetric Synthetic Aperture Radar (PolSAR) data, employing physics-guided polarimetric feature selection within a dual-branch network architecture.

Objective

• To develop and evaluate a physics-guided polarimetric feature selection approach based on dual-branch networks for the classification of agricultural fields using PolSAR data.

Study Configuration

• Spatial Scale: [Not specified in the provided text.]
• Temporal Scale: [Not specified in the provided text.]

Methodology and Data

• Models used: Dual-branch networks, Physics-guided polarimetric feature selection.
• Data sources: Polarimetric Synthetic Aperture Radar (PolSAR) data for agriculture. [Specific sources not specified in the provided text.]

Main Results

[Not specified in the provided text.]

Contributions

[Not specified in the provided text.]

Funding

[Not specified in the provided text.]

Citation

@article{Zhou2026PhysicsGuided,
  author = {Zhou, Yueyang and Yin, Junjun and Yang, Jian},
  title = {Physics-Guided Polarimetric Feature Selection Based on Dual-Branch Networks for PolSAR Agriculture Field Classification},
  journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  year = {2026},
  doi = {10.1109/jstars.2026.3652841},
  url = {https://doi.org/10.1109/jstars.2026.3652841}
}