Research on cloud parameter inversion method based on deep learning

Abstract

Abstract: Effective cloud detection and cloud phase identification are of great significance to agriculture, climate and human life, and the acquisition of these data is mainly from satellite remote sensing. Satellite remote sensing data plays a vital role in the production and life of current society, and the development of many fields is inseparable from the support of satellite remote sensing data. With the development of high-precision sensors, traditional research methods cannot meet the requirements of large-scale and high-dimensional data mining and processing, so deep learning technology has been rapidly developed in the field of remote sensing. Based on deep learning technology, a method of cloud detection and cloud phase recognition combined with multi-band remote sensing images is proposed in this work. MODIS cloud product images are used as samples, the different waveband information is used as eigenvalue to establish multiple databases for cloud detection and cloud phase state recognition research tasks, and then Deeplab V3+ model is used for training and prediction, so as to complete the high-precision cloud detection and cloud phase state recognition. Compared with the traditional methods, the proposed method is more efficient and convenient, and has stronger feature extraction ability. When multi-band is used as the eigenvalue input model for prediction, the method shows good performance.

Key words: moderate-resolution imaging spectroradiometer, Deeplab V3+, cloud detection, cloud phase identification

CLC Number:

X831

WU Wenhan, MA Jinji∗, SUN Erchang, GUO Jinyu, YANG Guang, WANG Yuyao. Research on cloud parameter inversion method based on deep learning[J]. Journal of Atmospheric and Environmental Optics, 2022, 17(4): 453-464.

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