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Research on Cloud Detection in Non-agricultural Image Based on Long Time Series Data

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DOI: 10.23977/jaip.2024.070118 | Downloads: 14 | Views: 169


Ping Wu 1, Xiaoping Lin 1


1 Fujian Provincial Natural Resources Geographic Information Center, Fuzhou, Fujian, 350003, China

Corresponding Author

Ping Wu


With the rapid development of China's economy, the increasingly severe phenomenon of farmland non-agriculturalization may potentially impact China’s food security. Against this backdrop, the task of monitoring farmland non-agriculturalization in Fujian Province has become increasingly arduous, leading to an exponential increase in the amount of remote sensing image data that needs to be received and analyzed throughout the year. Solely relying on manual methods for analyzing the quality of vast amounts of data becomes highly challenging. Therefore, it is imperative to introduce automated detection technology to improve the speed of cloud layer detection in images. This paper proposes an algorithm that utilizes long-term historical sequences of remote sensing imagery to obtain statistical data on the dark channel prior of ground objects, which are then compared with the dark channel prior from the images to be inspected, thereby obtaining information about cloud layers. Experimental validation confirms that the method presented in this paper can achieve automatic cloud layer detection, which to a certain extent improves production efficiency while also delivering relatively satisfactory detection results.


Long-Time Series Data, Non-Agriculturalization, Remote Sensing Imagery, Cloud Detection


Ping Wu, Xiaoping Lin, Research on Cloud Detection in Non-agricultural Image Based on Long Time Series Data. Journal of Artificial Intelligence Practice (2024) Vol. 7: 116-123. DOI:


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