Ground-based cloud detection using mixture losses

Zhifei Hu; Zeyu Zang; Shuoyang Fan; Shuang Liu; Zhong Zhang; Chaojun Shi

doi:10.1117/12.3055456

12 December 2024 Ground-based cloud detection using mixture losses

Zhifei Hu, Zeyu Zang, Shuoyang Fan, Shuang Liu, Zhong Zhang, Chaojun Shi

Author Affiliations +

Proceedings Volume 13439, Fourth International Conference on Testing Technology and Automation Engineering (TTAE 2024); 134391N (2024) https://doi.org/10.1117/12.3055456
Event: Fourth International Conference on Testing Technology and Automation Engineering (TTAE 2024), 2024, Xiamen, China

Abstract

Recently, neural networks dominate various aspects of the field of ground-based cloud detection, serving as an advanced and robust method for cloud observation. Since loss functions are crucial for optimizing neural networks, the existing networks with advanced loss functions are also a vital area. This paper explores the impact of three distinct loss functions and their mixture effects on the optimization of network performance and gives strategies for selecting loss functions. To ascertain the efficacy of various loss functions, we carried out a comprehensive set of experiments on a dataset dedicated to ground-based cloud detection. Our findings indicate that employing a mixture of loss functions significantly enhances the training process for models focused on ground-based cloud identification.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhifei Hu, Zeyu Zang, Shuoyang Fan, Shuang Liu, Zhong Zhang, and Chaojun Shi "Ground-based cloud detection using mixture losses", Proc. SPIE 13439, Fourth International Conference on Testing Technology and Automation Engineering (TTAE 2024), 134391N (12 December 2024); https://doi.org/10.1117/12.3055456

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