Onboard Cloud Detection and Atmospheric Correction With Efficient Deep Learning Models

Aybar, Cesar; Mateo-García, Gonzalo; Acciarini, Giacomo; Růžička, Vít; Meoni, Gabriele; Longépé, Nicolas; Gómez-Chova, Luis

doi:10.1109/jstars.2024.3480520

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

2024

DOI: 10.1109/jstars.2024.3480520

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Onboard Cloud Detection and Atmospheric Correction With Efficient Deep Learning Models

Cesar Aybar,

Gonzalo Mateo-García,

Giacomo Acciarini

et al.

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Deep Learning-Based Cloud Detection for Optical Remote Sensing Images: A Survey

Wang,

Zhao,

Meng

et al. 2024

Remote Sensing

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In optical remote sensing images, the presence of clouds affects the completeness of the ground observation and further affects the accuracy and efficiency of remote sensing applications. Especially in quantitative analysis, the impact of cloud cover on the reliability of analysis results cannot be ignored. Therefore, high-precision cloud detection is an important step in the preprocessing of optical remote sensing images. In the past decade, with the continuous progress of artificial intelligence, algorithms based on deep learning have become one of the main methods for cloud detection. The rapid development of deep learning technology, especially the introduction of self-attention Transformer models, has greatly improved the accuracy of cloud detection tasks while achieving efficient processing of large-scale remote sensing images. This review provides a comprehensive overview of cloud detection algorithms based on deep learning from the perspective of semantic segmentation, and elaborates on the research progress, advantages, and limitations of different categories in this field. In addition, this paper introduces the publicly available datasets and accuracy evaluation indicators for cloud detection, compares the accuracy of mainstream deep learning models in cloud detection, and briefly summarizes the subsequent processing steps of cloud shadow detection and removal. Finally, this paper analyzes the current challenges faced by existing deep learning-based cloud detection algorithms and the future development direction of the field.

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Deep Learning-Based Cloud Detection for Optical Remote Sensing Images: A Survey

Wang,

Zhao,

Meng

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

show abstract

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Onboard Cloud Detection and Atmospheric Correction With Efficient Deep Learning Models

Cited by 1 publication

References 56 publications

Deep Learning-Based Cloud Detection for Optical Remote Sensing Images: A Survey

Deep Learning-Based Cloud Detection for Optical Remote Sensing Images: A Survey

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