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http://dx.doi.org/10.7780/kjrs.2004.20.4.235

SPOT/VEGETATION-based Algorithm for the Discrimination of Cloud and Snow  

Han Kyung-Soo (프랑스 기상청 기상연구소)
Kim Young-Seup (부경대학교 위성정보과학과)
Publication Information
Korean Journal of Remote Sensing / v.20, no.4, 2004 , pp. 235-244 More about this Journal
Abstract
This study focuses on the assessment for proposed algorithm to discriminate cloudy pixels from snowy pixels through use of visible, near infrared, and short wave infrared channel data in VEGETATION-1 sensor embarked on SPOT-4 satellite. Traditional threshold algorithms for cloud and snow masks did not show very good accuracy. Instead of these independent masking procedures, K-Means clustering scheme is employed for cloud/snow discrimination in this study. The pixels used in clustering were selected through an integration of two threshold algorithms, which group ensemble the snow and cloud pixels. This may give a opportunity to simplify the clustering procedure and to improve the accuracy as compared with full image clustering. This paper also compared the results with threshold methods of snow cover and clouds, and assesses discrimination capability in VEGETATION channels. The quality of the cloud and snow mask even more improved when present algorithm is implemented. The discrimination errors were considerably reduced by 19.4% and 9.7% for cloud mask and snow mask as compared with traditional methods, respectively.
Keywords
SPOT/VEGETATION; K-Mean; Cloud Mask; Snow Mask.;
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