Wang et al. (2025) Joint Learning for Feature Reconstruction and Prediction in Agricultural Semantic Segmentation From Incomplete Satellite Image Time Series

Identification

• Journal: IEEE Transactions on Geoscience and Remote Sensing
• Year: 2025
• Date: 2025-12-29
• Authors: Yuze Wang, Mariana Belgiu, Haiyang Wu, D. Zhong, Yangyang Cao, Haoyu Li, Chao Tao
• DOI: 10.1109/tgrs.2025.3649017

Research Groups

[Information not available in the provided text.]

Short Summary

[Information not available in the provided text.]

Objective

• To develop a joint learning framework for feature reconstruction and prediction to improve agricultural semantic segmentation, specifically addressing challenges posed by incomplete satellite image time series.

Study Configuration

• Spatial Scale: Agricultural fields or regions.
• Temporal Scale: Multiple observations over time, forming time series.

Methodology and Data

• Models used: Joint learning framework, likely involving deep learning models for feature reconstruction, prediction, and semantic segmentation.
• Data sources: Incomplete satellite image time series, specifically for agricultural applications.

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{Wang2025Joint,
  author = {Wang, Yuze and Belgiu, Mariana and Wu, Haiyang and Zhong, D. and Cao, Yangyang and Li, Haoyu and Tao, Chao},
  title = {Joint Learning for Feature Reconstruction and Prediction in Agricultural Semantic Segmentation From Incomplete Satellite Image Time Series},
  journal = {IEEE Transactions on Geoscience and Remote Sensing},
  year = {2025},
  doi = {10.1109/tgrs.2025.3649017},
  url = {https://doi.org/10.1109/tgrs.2025.3649017}
}