A brief analysis of remote sensing image cloud extraction method based on deep learning

Gao Lin; Chenxi Gai

doi:10.1117/12.2675174

25 May 2023 A brief analysis of remote sensing image cloud extraction method based on deep learning

Gao Lin, Chenxi Gai

Proceedings Volume 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022); 126360Y (2023) https://doi.org/10.1117/12.2675174
Event: Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 2022, Shenyang, China

Abstract

In optical remote sensing images, different degrees of cloud cover will have different degrees of obscuration to ground information. Due to the special spectral and shape characteristics of clouds, cloud extraction makes the detection and extraction of cloud cover in the process of remote sensing data processing is one of the primary problems to be solved for further analysis and utilization of image information, and cloud extraction has gradually become a hot spot and frontier of current research in recent years. In view of this, this paper categorizes and summarizes the existing deep learning theories and methods based on a large amount of relevant literature in recent years, and classifies cloud extraction into three types of methods based on convolutional neural networks, recurrent neural networks, and generative adversarial neural networks by analyzing the network architectures adopted by different methods, briefly introduces the current development trends and representative works of cloud extraction methods based on remote sensing images at home and abroad, and finally gives an overview of the current Finally, we suggest and foresee the current problems and future development trend of cloud extraction.

Citation Download Citation

Gao Lin and Chenxi Gai "A brief analysis of remote sensing image cloud extraction method based on deep learning", Proc. SPIE 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 126360Y (25 May 2023); https://doi.org/10.1117/12.2675174

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KEYWORDS

Clouds

Data modeling

Image segmentation

Remote sensing

Deep learning

Feature extraction

Education and training

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