Cloud cover seriously restricts the observation and application of remote sensing imagery. Standard cloud detection products, including five types of ground objects (clouds, cloud shadows, snow, water bodies, and land), are an important part of optical image preprocessing and have been widely used in mainstream satellites both domestically and internationally. However, SDGSAT-1, as the first satellite of the Sustainable Development Agenda (SDA), has not yet provided standard pixel-level labeled products, which restricts the utilization and sharing of its data. Aiming at the limitation that SDGSAT-1’s multispectral bands are few, making it prone to confusion between cloud-snow and cloud shadow-water, this paper proposes a standardized cloud detection method by fusing multispectral and thermal infrared (TIR) data. The method utilizes the low-temperature characteristics of thick clouds in the TIR band to enhance cloud-snow differentiation, incorporates GSWO and DEM data to assist in identifying complex backgrounds, and optimizes cloud mask boundaries using morphology and guided filtering. Experimental results show that the cloud detection IoU reaches 74.33% and the overall accuracy reaches 85.09%. The study effectively fills the gap in providing standard cloud detection products for SDGSAT-1 and provides important support for the development of automatic labeling and high-precision detection algorithms for subsequent data.
LI Xueyan
,
HU Changmiao
. Standardized cloud detection algorithm for SDGSAT-1 based on multispectral and thermal infrared data fusion[J]. Journal of University of Chinese Academy of Sciences, 0
: 42
-42
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DOI: 10.7523/j.ucas. 2025.053
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