Dynamic cloud detection based on multi-scale retinal image enhancement

doi:10.13474/j.cnki.11-2246.2022.0326

Abstract

Abstract: Cloud detection is the basis of various quantitative remote sensing products of meteorological satellites. Whether it is weather analysis based on cloud images, inversion of various atmospheric and surface parameters, sand, dust, fire and other disaster detection based on cloud removal, accurate cloud detection in images, especially details such as thin clouds and cloud edges is required. Aiming at the refined cloud detection of geostationary satellites, this paper proposes a dynamic cloud detection method based on multi-scale retinal image enhancement. The clear sky radiation background field is applied to enhance and extract the cloud detail information of the radiation difference through the multi-scale image enhancement and the maximum inter-class difference method. This paper uses 75 MODIS cloud detection products from 2021 to 2022 as the verification data to verify the algorithm accuracy. The overall algorithm accuracy reaches 91.13%, the recall rate is 94.02%, and the precision rate is 86.71%. Overall, the algorithm has strong applicability and robustness. It is excellent and has well supported the commercial application of quantitative remote sensing products in the past two years.

Key words: cloud detection, retinal, image enhancement, background field, geostationary satellites

CLC Number:

P237

CHEN Farong, FANG Songsong. Dynamic cloud detection based on multi-scale retinal image enhancement[J]. Bulletin of Surveying and Mapping, 2022, 0(11): 62-66,143.

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