A Self Training Mechanism With Scanty and Incompletely Annotated Samples for Learning‐Based Cloud Detection in Whole Sky Images-Reference-Cited by-同舟云学术

A Self Training Mechanism With Scanty and Incompletely Annotated Samples for Learning‐Based Cloud Detection in Whole Sky Images

Published:2022-06 Issue:6 Volume:9 Page:
ISSN:2333-5084
Container-title:Earth and Space Science
language:en
Short-container-title:Earth and Space Science

Author:

Ye Liang¹^ORCID,Wang Yufeng¹,Cao Zhiguo²,Yang Zhibiao³,Min Huasong¹

Affiliation:

1. Institute of Robotics and Intelligent Systems School of Information Science and Engineering/School of Artificial Intelligence Wuhan University of Science and Technology Wuhan China

2. National Key Laboratory of Science and Technology on Multi‐Spectral Information Processing School of Automation and Artificial Intelligence Huazhong University of Science and Technology Wuhan China

3. Hubei Meteorological Bureau China Meteorological Administration Wuhan China

Funder

China Postdoctoral Science Foundation

Publisher

American Geophysical Union (AGU)

Subject

General Earth and Planetary Sciences,Environmental Science (miscellaneous)

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1029/2022EA002220

Reference24 articles.

1. SLIC Superpixels Compared to State-of-the-Art Superpixel Methods

2. Spatiotemporal distributions of cloud parameters and their response to meteorological factors over the Tibetan Plateau during 2003-2015 based on MODIS data

3. Feature Extraction from Whole-Sky Ground-Based Images for Cloud-Type Recognition

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