The paper is devoted to the development of the methods for segmentation of images of atmospheric clouds, which are obtained by remote sensing methods using aircraft or satellite onboard systems. The proposed approach is some extent further improvement of the convolutional neural network of the U-net type. The uses known quality criteria for segmentation, which allows us to compare the proposed approach with already known methods in the field of segmentation of images of atmospheric clouds. A large number of experiments on real images shows the feasi-bility of using the proposed method of segmentation for automated processing with the require-ments for real-time operation. Applied use of the results is possible in the tasks of monitoring and classification for weather forecasting, agriculture, and other areas related to observations of atmospheric clouds.

1. Long, C.N.; Sabburg, J.M.; Calbo, J.; Pages, D. Retieving cloud characteristics from ground-based daytime color all-sky images. Journal of atmospheric and oceanic technology. 2006, 23, 633-652. https://doi.org/10.1175/JTECH1875.1

2. Rusyn, B.; Lutsyk, O.; Kosarevych, R.; Korniy, V. Segmentation of atmospheric clouds images obtained by remote sensing. 14th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (TCSET). 2018, 213-216. https://doi.org/10.1109/TCSET.2018.8336189

3. Cui, Z.; Yang, J.; Qiao, Y. Brain mri segmentation with patch-based cnn approach. Chinese Control Conference (CCC), IEEE. 2016, 7026-7031. https://doi.org/10.1109/ChiCC.2016.7554465

4. Bengio, Y.; Courville, A.; Vincent, P. Representation learning: A review and new perspectives. Pattern Analysis and Machine Intelligence, IEEE. 2013, 35(8), 1798-1828. https://doi.org/10.1109/TPAMI.2013.50

5. Christodoulou, C.I.; Michaelides, S.; Pattichi, C. Multifeature texture analysis for the classification of clouds in satellite imagery. IEEE Transactions on Geoscience and Remote Sensing. 2003, 41(11), 2662-2668. https://doi.org/10.1109/TGRS.2003.815404

6. Ricciardelli, F.; Romano, F.; Cuomo, V. Physical and statistical approaches for cloud identification using Meteosat Second Generation-Spinning Enhanced Visible and Infrared Imager Data. Remote Sensing of Environment. 2008, 112 (6), 2741-2760. https://doi.org/10.1016/j.rse.2008.01.015

7. Rusyn, B.P.; Tayanov, V.A.; Lutsyk, O.A. Upper-bound estimates for classifiers based on a dissimilarity function. Cybernetics and Systems Analysis. 2012, 48(4), 592-600. https://doi.org/10.1007/s10559-012-9439-2

8. Yuan, Y.; Hu, X. Bag-of-words and object-based classification for cloud extraction from satellite imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2015, 8 (8), 4197-4205. https://doi.org/10.1109/JSTARS.2015.2431676

9. Benediktsson, J.; Palmason, J.; Sveinsson, J. Classification of hyperspectral data from urban areas based on extended morphological profiles. IEEE Transactions on Geoscience and Remote Sensing. 2005, 43, 480-491. https://doi.org/10.1109/TGRS.2004.842478

10. Flouzat, G.; Amram, O.; Cherchali, S. Spatial and spectral segmentation of satellite remote sensing imagery using processing graphs by mathematical morphology. Proceedings of the IEEE Geoscience and Remote Sensing Symposium, IGARSS 98. 1998, 4, 1769-1771. https://doi.org/10.1109/IGARSS.1998.703646

11. He, K.; Gkioxari, G.; Dollar, P.; Girshick. R. Mask R-CNN. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2017, 43(2), 386-397. https://doi.org/10.1109/TPAMI.2018.2844175

12. Savoy, F. M.; Lemaitre, J.; Dev, S.; Lee, Y. H.; Winkler, S.; Cloudbase height estimation using high-resolution whole sky imagers. Proc. IEEE International Geoscience and Remote Sensing Symposium. 2015, 1622-1625. https://doi.org/10.1109/IGARSS.2015.7326095

13. Liu, S.; Zhang, L.; Zhang, Z.; Wang, C.; Xiao, B. Automatic cloud detection for all-sky images using superpixel segmentation. IEEE Geoscience and Remote Sensing Letters. 2015, 12(2), 354-358. https://doi.org/10.1109/LGRS.2014.2341291

14. Rusyn, B.; Lutsyk, O.; Lysak, Y.; Korniy, V. Quantitative analysis of metallographic images. Modern Problems of Radio Engineering, Telecommunications and Computer Science Proceedings of International Conference (TCSET). 2006, 553-557.

15. Nazarkevych, M. Research of Ateb-Gabor Filter in Biometric Protection Systems. IEEE 13th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT). 2018, 1, 310-313. https://doi.org/10.1109/STC-CSIT.2018.8526607

16. Long, J.; Shelhamer, E.; Darrell, T. Fully convolutional networks for semantic segmentation. IEEE Conference on Computer Vision and Pattern Recognition. 2015, 3431-3440. https://doi.org/10.1109/CVPR.2015.7298965

17. Falk, T.; Mai, D.; Bensch, R.; Brox, T.; Ronneberger, O. U-Net: deep learning for cell counting, detection, and morphometry. Nature Methods. 2019, 16, 67-70. https://doi.org/10.1038/s41592-018-0261-2