Spacecraft Recovery & Remote Sensing, Volume. 45, Issue 1, 161(2024)
Study on Machine Learning Cloud Detection Considering Optimal Selection of Samples
[2] [2] KEGELMEYER J W. Extraction of Cloud Statistics from Whole Sky Imaging Cameras: SNLCA[R]. Liverme, CA (United States): Sia National Lab., 1994.
[3] K T KRIEBEL, R W SAUNDERS, G GESELL. Optical Properties of Clouds Derived from Fully Cloudy AVHRR Pixels. Beiträge zur Physik der Atmosphäre, 62, 165-171(1989).
[5] [5] SHANG H, CHEN L, LETU H, et al. Development of a Daytime Cloud Haze Detection Algithm f Himawari8 Satellite Measurements over Central Eastern China[J]. Journal of Geophysical Research Atmospheres, 2017, 122(6): 35283543.
[11] [11] DEVASTHALE A, JOHANSSON E, KARLSSON K, et al. Advancing the Uncertainty acterisation of Cloud Masking in Passive Satellite Imagery: Probabilistic Fmulations f NOAA AVHRR Data[J]. Remote Sensing of Environment, 2015, 158: 126139.
[17] [17] LIU C, YANG S, DI D, et al. A Machine LearningBased Cloud Detection Algithm f the Himawari8 Spectral Image[J]. Advances in Atmospheric Sciences, 2021, 39: 19942007.
[21] I TAKAHITO, Y RYO. Algorithm Theoretical Basis for Himawari-8 Cloud Mask Product. Meteorological Satellite Center Technical Note, 61, 1-17.(2016).
[24] [24] AMIT Y, GEMAN D. Shape Quantization Recognition with Romized Trees[J]. Neural Computation, 1997, 9(7): 15451588.
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Hui ZHANG, Fangrong ZHOU, Zhen XU, Gang WEN, Yutang MA, Xu HAN, Lei WU. Study on Machine Learning Cloud Detection Considering Optimal Selection of Samples[J]. Spacecraft Recovery & Remote Sensing, 2024, 45(1): 161
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Received: Mar. 27, 2023
Accepted: Jul. 15, 2023
Published Online: Apr. 22, 2024
The Author Email: HAN Xu (228239634@qq.com)