Zhang et al. (2026) Land-Use Classification of High-Resolution Remote Sensing Imagery Incorporating Global–Local Interactive Features Across Optical Domain and Land Cover Primitives

Identification

• Journal: IEEE Transactions on Geoscience and Remote Sensing
• Year: 2026
• Date: 2026-01-01
• Authors: Binqiang Zhang, Dawei Wen, You Lü, Yuan Tian, Xin Huang
• DOI: 10.1109/tgrs.2026.3669385

Research Groups

N/A

Short Summary

This paper proposes a novel approach for land-use classification of high-resolution remote sensing imagery, integrating global and local interactive features derived from the optical domain and land cover primitives to improve classification accuracy.

Objective

• To develop an improved method for land-use classification of high-resolution remote sensing imagery by incorporating global–local interactive features across the optical domain and land cover primitives.

Study Configuration

• Spatial Scale: High-resolution (specific resolution not provided, but implied to be fine-grained).
• Temporal Scale: N/A

Methodology and Data

• Models used: Likely involves advanced image processing or machine learning models for feature extraction and classification, specifically designed to integrate "Global–Local Interactive Features."
• Data sources: High-resolution remote sensing imagery (optical domain), and land cover primitives.

Main Results

N/A

Contributions

N/A

Funding

N/A

Citation

@article{Zhang2026LandUse,
  author = {Zhang, Binqiang and Wen, Dawei and Lü, You and Tian, Yuan and Huang, Xin},
  title = {Land-Use Classification of High-Resolution Remote Sensing Imagery Incorporating Global–Local Interactive Features Across Optical Domain and Land Cover Primitives},
  journal = {IEEE Transactions on Geoscience and Remote Sensing},
  year = {2026},
  doi = {10.1109/tgrs.2026.3669385},
  url = {https://doi.org/10.1109/tgrs.2026.3669385}
}