Article Review Procedure
Academic Areas and Subjects
Herald of Advanced Information Technology
Search by article
Vol. 3 № 1
Vol. 2 № 1
Vol. 2 № 2
Vol. 2 № 3
Vol. 2 № 4
Vol. 1 № 1
26 Feb 2020
Informatics, Culture and Technology
20 May 2019
Informatics, Culture and Technology
30 Mar 2019
VIII International Scientific-Practical Conference «Information Control Systems and Technology»
SEGMENTATION OF CLOUD ORGANIZATION PATTERNS FROM SATELLITE IMAGES USING DEEP NEURAL NETWORKS
Climate change is one of the most important challenges that humanity faces now. The essential part of climate models is the movement of clouds, which affects climate parameters dramatically. Shallow clouds play a huge role in determining the Earth's climate. They're also difficult to understand and to represent in climate models. Unfortunately, the exact modeling of clouds movement is notoriously tricky and requires perfect knowledge of underling physical processes and initial states. Boundaries between different types of clouds are usually blurry and difficult to define with rule-based decision systems. Simplification of the segmentation step is crucial and can help researchers to develop better climate models. Convolutional neural networks have been successfully applied in many similar areas, and for cloud segmentation itself, too. However, there is a high cost of good, pixel-level labeled datasets, so the industry often uses coarse-labeled datasets with the either region or image-level labels. In this paper, we propose an end-to-end deep-learning-based method for classification and segmentation of different types of clouds from a single colored satellite image. Here, we propose the multi-task learning approach to cloud segmentation. Additionally to the segmentation model, we introduce a separate classifier that uses features from the middle layer of the segmentation model. The presented method can use coarse, uneven and overlapping masks for clouds. From the experimental results, the proposed method demonstrates stable results and learns good general features from noisy data. As we observed during the experiments, our model finds types of clouds, which are not annotated on the images but seem to be correctly defined. It is ranked in top three percent competing methods on Understanding Clouds from Satellite Images Dataset
Dmitry V. Spodarets
( email@example.com )
Philip O. Marchenko
( firstname.lastname@example.org )
Tymchenko, Borys I.
( email@example.com )
deep learning; satellite imaging; deep convolutional neural network; multi-target learning; cloud formations classification; Kaggle; meteorology
1. Rasp S., Schulz H., Bony S. & Stevens B. (2019) “Combining crowd-sourcing and deep learning to explore the mesoscale organization of shallow convection”, eprint arXiv:1906.01906, USA.
2. Zhe Z., Shi Q., Binbin H. & Chengbing D. (2018). “Cloud and Cloud Shadow Detection for Landsat Images: The Fundamental Basis for Analyzing Landsat Time Series”, Remote Sensing Time Series Image Processing, USA.
3. Harb M., Gamba P. & Dell'Acqua F. (2016). “Automatic Delineation of Clouds and Their Shadows in Landsat and CBERS (HRCC) Data”, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, USA. DOI:10.1109/JSTARS.2016.2514274.
4. Hu X., Wang Y. & Shan J. (2015). “Automatic Recognition of Cloud Images by Using Visual Saliency Features”, IEEE Geoscience and Remote Sensing Letters, Vol. 12, No. 8, pp. 1760- 1764. DOI: 10.1109/LGRS.2015.2424531.
5. Zhang, Jinglin Z., Liu P., Feng Z. & Qianqian S. (2018). “CloudNet: Ground-Based Cloud Classification With Deep Convolutional Neural Network” Geophysical Research Letters, USA. DOI: 10.1029/2018GL077787.
6. (2020). “Understanding Clouds from Satellite Images | Kaggle”. [Digital resource] – Available at: https://www.kaggle.com/c/understanding_cloud_org anization. – Active link – 15.03.2020.
7. (2020). “Sugar, Flower Fish, or Gravel?” [Digital resource]/ – Available at: https://www.zooniverse.org/projects/raspstephan/sug ar-flower-fish-or-gravel. – Active link – 15.03.2020.
8. (2020). “Albumentations: fast image augmentation library and easy to use wrapper around other libraries”. [Digital resource] – Available at: https://github.com/albumentationsteam/albumentations. – Active link – 15.03.2020.
9. Iglovikov V. & Shvets A. (2018). “TernausNet: U-Net with VGG11 Encoder PreTrained on ImageNet for Image Segmentation”, eprint arXiv: 1801.05746, USA.
10. Ronneberger O., Fischer P. & Brox T. (2015). “U-Net: Convolutional Networks for Biomedical Image Segmentation”, eprint arXiv: 1505.04597, USA.
11. Lin T., Dollár P., Girshick R., He K., Hariharan B. & Belongie S. (2016). “Feature Pyramid Networks for Object Detection”, eprint arXiv: 1612.03144, USA.
12. He K., Zhang X., Ren S. & Sun J. (2015). “Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification”, eprint arXiv: 1502.01852, USA.
13. Rich C., Lawrence S. & Giles C. (2000). “Overfitting in Neural Nets: Backpropagation, Conjugate Gradient, and Early Stopping”, Advances in Neural Information Processing Systems. 13. pp. 402-408, USA.
14. Liu L., Jiang H., He P., Chen W., Liu X., Gao J. & Han J. (2019). “On the Variance of the Adaptive Learning Rate and Beyond”, eprint arXiv: 1908.03265, USA.
15. You Y., Gitman I. & Ginsburg B. (2017). “Large Batch Training of Convolutional Networks”, eprint arXiv: 1708.03888, USA.
16. Zhang M., Lucas J., Hinton G., Ba J. & Han J. (2019). “Lookahead Optimizer: k steps forward, 1 step back”, eprint arXiv: 1907.08610, USA.
17. (2020). “Mgrankin/over9000: Over9000 optimizer” [Digital resource]. – Available at: https://github.com/mgrankin/over9000. – Active link – 15.03.2020.
18. Loshchilov I. & Hutter F. (2016). “SGDR: Stochastic Gradient Descent with Warm Restarts”, eprint arXiv: 1608.03983, USA.
19. Jeroen Bertels, Tom Eelbode, Maxim Berman, Dirk Vandermeulen, Frederik Maes, Raf Bisschops & Matthew Blaschko. (2019). “Optimizing the Dice Score and Jaccard Index for Medical Image Segmentation: Theory & Practice”, eprints arXiv: 1911.01685, USA.
20. Dong-Hyun L. (2013). “Pseudo-Label: The Simple and Efficient Semi-Supervised Learning Method for Deep Neural Networks”, ICML 2013 Workshop: Challenges in Representation Learning (WREPL), USA.
21. Arazo E., Ortego D., Albert P., O'Connor N. & McGuinness K. (2020). “Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning”, eprints arXiv: 1908.02983, USA.
22. Moshkov N., Mathe B., Kertesz-Farkas A., Hollandi R. & Horvath P. (2020). “Test-time augmentation for deep learning-based cell segmentation on microscopy images”, bioRxiv 814962, USA, DOI: 10.1101/814962.
23. He K., Zhang X., Ren S. & Sun J. (2015). “Deep Residual Learning for Image Recognition", eprints arXiv: 1512.03385, USA.
24. Huang G., Liu Z., van der Maaten L. & Weinberger K. (2016). “Densely Connected Convolutional Networks”, eprints arXiv: 1608.06993, USA.
25. Tan M. & Le Q. (2019). “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks”, eprints arXiv: 1905.11946, USA.
26. (2018). “Accelerated DL R&D”. [Digital resource]. – Available at: https://github.com/catalyst-team/catalyst. – Active link – 15.03.2020.
27. (2017). “PyTorch”. [Digital resource] – Available at: https://pytorch.org. – Active link – 15.03.2020. 28. Tymchenko B., Hramatik A., Tulchiy H. & Antoshchuk S. (2019). “Classifying Mixed Patterns of Proteins in Microscopic Images with Deep Neural Networks”, Herald of advanced information technology, Vol. 2, No. 1, pp. 29-36. DOI: 10.15276/hait.02.2019.3.
Received after revision 12.02.2020
Vol. 3 № 1, 2020
3 July 2020
Search by author
Methodology of Information Technologies
Design of Information Technologies and Systems
Information Technology in Design of Different Nature Automated Systems, Intelligent Sensors
Intellectual Information Technologies: Neural Networks, Machine Learning, Forecasting
Research and Modeling of Information Processes and Technologies
Information Technology in Distributed Systems
Design of Computer Systems, Networks and their Components
Distributed data Processing
Information Technologies in Management
Information Support of Production Facilities Management Systems
Simulation and diagnostics of complex systems and processes
Information Technology in Socio-Economic and Organizational-Technical Systems
Geoinformatics and Geoinformation Systems
Information Technology in Education
Information Technology in Life Safety
Project, Program and Portfolio Management Methodology
KarelWintersky ] [
[ © Odessa National Polytechnic University, 2018.]