Cloud detection of multi-type satellite images based on spectral assimilation and deep learning

ABSTRACT

With strong self-learning and data analysis capabilities, deep learning is essential in cloud detection. However, many high-quality samples are the key to deep learning cloud detection methods. Different satellite image cloud detection techniques need to select adequate representative and high-quality samples and make corresponding cloud masks, which not only requires professional knowledge but also consumes a lot of workforce and time. To improve the generalization ability of the deep learning cloud detection model and quickly apply it to the cloud detection of different satellite images, this paper proposes a deep learning cloud detection method based on spectral assimilation for multiple types of satellite images (SAUNetCD). Under the condition of using fewer deep learning data samples, the deep learning model is used to achieve automatic cloud detection of multiple satellite data. Taking Landsat 8 OLI, Landsat 9 OLI, GF-1 WFV, and Sentinel 2A as examples, this paper selects Landsat 8 OLI data as the source data of cloud detection. The experimental results show that the spectral assimilation method improves the generalization ability of the deep learning cloud detection model and improves the cloud detection accuracy by nearly 20%. It realizes the fast cloud detection application of different satellite images by deep learning. It provides an effective way for cloud detection of multiple types of satellite images.

KEYWORDS:

• Deep learning
• spectral assimilation
• multi-type satellite images
• Cloud detection

Acknowledgements

The authors thank the National Natural Science Foundation of China [42271412] and the Natural Science Foundation of Shandong Province under Grant [ZR2020MD051].

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The work was supported by the National Natural Science Foundation of China [42271412].