Leveraging deep residual learning with Atrous-based UNet for enhanced cloud segmentation in satellite imagery

Sicheng Li

doi:10.1117/12.3052030

18 November 2024 Leveraging deep residual learning with Atrous-based UNet for enhanced cloud segmentation in satellite imagery

Sicheng Li

Author Affiliations +

Proceedings Volume 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) ; 134031Q (2024) https://doi.org/10.1117/12.3052030
Event: International Conference on Algorithms, High Performance Computing, and Artificial Intelligence, 2024, Zhengzhou, China

Abstract

Satellite images play a pivotal role in meteorological studies and environmental monitoring, providing crucial data that contributes to our understanding of atmospheric conditions, weather forecasts, and climate patterns. One of the significant challenges in the analysis of satellite images is the accurate segmentation of clouds, which is imperative for refining the data used in various meteorological applications. This paper introduces an innovative approach to cloud segmentation by harnessing the capabilities of Deep Residual Learning combined with the UNet architecture. Our method focuses on optimizing the segmentation process by employing residual connections that facilitate the training of deep networks and enhance the flow of information through the network, allowing the model to learn hierarchical features effectively. The integration of the UNet architecture further assists in capturing intricate details and enables precise localization, resulting in improved segmentation accuracy. We present comprehensive experiments that demonstrate the efficacy of our approach in providing superior cloud segmentation performance, underscoring its potential as a robust tool for enhancing the quality and reliability of satellite image analysis for meteorological and environmental research. Through this contribution, we aim to propel the advancement of cloud segmentation techniques, facilitating improved data accuracy for a broader spectrum of geospatial and atmospheric studies.

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

Citation Download Citation

Sicheng Li "Leveraging deep residual learning with Atrous-based UNet for enhanced cloud segmentation in satellite imagery", Proc. SPIE 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) , 134031Q (18 November 2024); https://doi.org/10.1117/12.3052030

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KEYWORDS

Image segmentation

Clouds

Satellites

Satellite imaging

Earth observing sensors

Deep learning

Education and training

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