Liu et al. (2026) Hybrid Physical Segmentation and Machine Learning Approach for Deep Convective Cloud Detection With Himawari-8

Identification

• Journal: IEEE Transactions on Geoscience and Remote Sensing
• Year: 2026
• Date: 2026-01-01
• Authors: Bochun Liu, Jinming Ge, Wenwen Liu, Wei Song, Xiaoyu Hu, Xiang Li, Jing Su, Qingyu Mu, Chi Zhang, Ziyang Xu
• DOI: 10.1109/tgrs.2026.3656118

Research Groups

[Information not available in the provided text.]

Short Summary

This study presents a hybrid method, combining physical segmentation and machine learning, for the detection of deep convective clouds using Himawari-8 satellite data.

Objective

• To develop and evaluate a hybrid physical segmentation and machine learning approach for the detection of deep convective clouds utilizing Himawari-8 satellite observations.

Study Configuration

• Spatial Scale: Geostationary satellite coverage (e.g., East Asia and Oceania).
• Temporal Scale: High-frequency satellite observations (e.g., 10-minute intervals).

Methodology and Data

• Models used: Hybrid Physical Segmentation, Machine Learning (specific algorithms not detailed in the title).
• Data sources: Himawari-8 geostationary satellite 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{Liu2026Hybrid,
  author = {Liu, Bochun and Ge, Jinming and Liu, Wenwen and Song, Wei and Hu, Xiaoyu and Li, Xiang and Su, Jing and Mu, Qingyu and Zhang, Chi and Xu, Ziyang},
  title = {Hybrid Physical Segmentation and Machine Learning Approach for Deep Convective Cloud Detection With Himawari-8},
  journal = {IEEE Transactions on Geoscience and Remote Sensing},
  year = {2026},
  doi = {10.1109/tgrs.2026.3656118},
  url = {https://doi.org/10.1109/tgrs.2026.3656118}
}