Cloud Cover Detection Using a Neural Network Based on MSU-GS Instrument Data of Arktika-M No. 1 Satellite

Abstract

Cloud detection in satellite imagery is one the most important problems of satellite meteorology. The accuracy of cloud detection significantly determines the quality of other hydrometeorological products. The paper presents an algorithm for detecting clouds in satellite images, which is based on a convolutional neural network with a modified U-Net architecture. Multispectral satellite imagery from the MSU-GS instrument operating onboard Arktika-M No 1 satellite are used as input data. The algorithm accuracy was estimated through machine learning metrics and comparison with reference masks compiled via manual decryption of the satellite images by an experienced image interpreter. In addition, the results are compared with similar products based on data of SEVIRI and VIIRS instruments. The accuracy of a cloud mask obtained following the suggested algorithm is 92% compared to a reference mask for sun-illuminated areas and 89% for dark areas.