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ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2020, Vol. 17, No. 7, pp. 26-38

Estimation of the accuracy of cloud masking algorithms using Sentinel-2 and PlanetScope data

A.V. Tarasov 1 
1 Perm State National Research Univercity, Perm, Russia
Accepted: 05.11.2020
DOI: 10.21046/2070-7401-2020-17-7-26-38
Nowadays, many automated satellite-based monitoring systems (e. g. for forestry or agriculture) widely use the images from Sentinel-2 and PlanetScope satellites, which combine high spatial and temporal resolution. An important challenge in the building of such monitoring systems is high-quality preprocessing of the images, including cloud masking. Traditional (threshold-based) cloud masking algorithms use only spectral reflectance in the visible and infrared (IR) bands. Also the machine learning methods (which take into account not only spectral characteristics but also geometry and texture) are successfully used for cloud masking on satellite images in recent decade. In this study, we estimated accuracy of cloud masking on Sentinel-2 images obtained at different seasons with the use of traditional Fmask and Sen2Cor algorithms and machine-learning-based S2cloudless algorithm, on the example of the Perm Region territory. For Sentinel-2 data, S2cloudless showed the best result (average accuracy 83 %), and the lowest accuracy was obtained with Fmask (70 %). The seasonal variability of cloud masking accuracy did not exceed 6 %. The applicability of the listed algorithms for PlanetScope data having only visible and near-IR spectral bands was also estimated. It was found that S2cloudless can be used for PlanetScope images obtained in winter, since the cloud masking accuracy is higher than that the standard cloud mask product delivered with the data.
Keywords: cloud masking, machine learning, PlanetScope, Sentinel-2
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References:

  1. Andreev A. I., Shamilova Yu. A., Kholodov E. I., Using convolutional neural networks for cloud detection from “Meteor-M” No. 2 MSU-MR Data, Russian Meteorology and Hydrology, 2019, Vol. 44, No. 7, pp. 459–466.
  2. Aas C., Jochemsen A., Mantas V., Lewyckyj N., Jozefiak M., Buchhorn M., Maximizing forest value through using Sentinel-2 in combination with hyperspectral UAVs, Proc. 69th Intern. Astronautical Congress, Bremen, Germany, 2018, pp. 4492−4498.
  3. Chuanqi T., Fuchun S., Tao K., Wenchang Z., Chao Y., Chunfang L., A Survey on Deep Transfer Learning, Proc. 27th Intern. Conf. Artificial Neural Networks (ICANN 2018), Rhodes, Greece, 4–7 Oct. 2018, Vol. 11141, pp. 270–279, DOI: 10.1007/978-3-030-01424-7_27.
  4. Dagobert T., Morel J.-M., Franchis C. D., von Gioi R. G., Visibility Detection in Time Series of Planetscope Images, Proc. Intern. Geoscience and Remote Sensing Symp. (IGARSS), Yokohama, 2019, pp. 1673−1676, DOI: 10.1109/IGARSS.2019.8898892.
  5. Drönner J., Korfhage N., Egli S., Mühling M., Thies B., Bendix J., Freisleben B., Seeger B., Fast Cloud Segmentation Using Convolutional Neural Networks, Remote Sensing, 2018, Vol. 10(11), Art. No. 1782, DOI: 10.3390/rs10111782.
  6. Feng Z., Yang J., Yao L., Patch-based fully convolutional neural network with skip connections for retinal blood vessel segmentation, IEEE Intern. Conf. Image Processing, Beijing, 2017, pp. 1742–1746, DOI: 10.1109/ICIP.2017.8296580.
  7. Guolin K., Qi M., Finley T., Wang T., Chen W., Weidong M., Qiwei Y., Tie-Yan L., LightGBM: A Highly Efficient Gradient Boosting Decision Tree, Proc. 31st Annual Conf. Neural Information Processing Systems (NIPS 2017), California, 2017, pp. 3149−3157.
  8. Gursky J., Boosting Showdown: Scikit-Learn vs XGBoost vs LightGBM vs CatBoost in Sentiment Classification, Towards Data Science, 2020, available at: https://towardsdatascience.com/boosting-showdown-scikit-learn-vs-xgboost-vs-lightgbm-vs-catboost-in-sentiment-classification-f7c7f46fd956 (accessed 20.07.2020).
  9. He K., Zhang X., Ren S., Sun J., Deep residual learning for image recognition, Proc. IEEE Conf. Computer Vision and Pattern Recognition, Las Vegas, 2016, pp. 770−778, DOI: 10.1109/CVPR.2016.90.
  10. Hethcoat M. G., Edwards D. P., Carreiras J. M. B., Bryant R. G., França F. M., Quegan S., A machine learning approach to map tropical selective logging, Remote Sensing of Environment, 2019, Vol. 221, pp. 569−582, DOI: 10.1016/j.rse.2018.11.044.
  11. Hoerl E., Kennard W. R., Ridge Regression: Biased Estimation for Nonorthogonal Problems, Technometrics, 1970, Vol. 12, pp. 55−67, DOI: 10.1080/00401706.1970.10488634.
  12. Huang G., Liu Z., Van Der Maaten L., Weinberger K. Q., Densely connected convolutional networks, Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Honolulu, 2017, pp. 4700−4708.
  13. Li Z., Shen H., Cheng Q., Liu Y., You S., He Z., Deep learning based cloud detection for medium and high resolution remote sensing images of different sensors, ISPRS J. Photogrammetry and Remote Sensing, 2019, Vol. 150, pp. 197–212, DOI: 10.1016/j.isprsjprs.2019.02.017.
  14. Liu C.-C., Zhang Y.-C., Chen P.-Y., Lai C.-C., Chen Y.-H., Cheng J.-H., Ko M.-H., Clouds Classification from Sentinel-2 Imagery with Deep Residual Learning and Semantic Image Segmentation, Remote Sensing, 2019, Vol. 11(2), Art. No. 119, DOI: 10.3390/rs11020119.
  15. Liu S., Li M., Zhang Z., Xiao B., Cao X., Multimodal Ground-Based Cloud Classification Using Joint Fusion Convolutional Neural Network, Remote Sensing, 2018, Vol. 10, Art. No. 822, DOI: 10.3390/rs10060822.
  16. Lonjou V., Desjardins C., Hagolle O., Petrucci B., Thierry T., Dejus M., Makarau A., Auer S., MACCS-ATCOR joint algorithm (MAJA), Proc. Conf. SPIE Remote Sensing, 28–29 Sept. 2016, Edinburgh, 2016, Vol. 1, p. 119.
  17. Main-Knorn M., Pflug B., Louis J., Debaecker V., Müller-Wilm U., Gascon F., Sen2Cor for Sentinel-2, Image and Signal Processing for Remote Sensing XXIII: Proc. SPIE, Warsaw, 2017, Vol. 10427, p. 218.
  18. Planet Imagery Product Specification, Planet Labs Inc., 2020, 97 p., available at: https://assets.planet.com/docs/Planet_Combined_Imagery_Product_Specs_letter_screen.pdf (accessed 20.07.2020).
  19. Powers D., Evaluation: from precision, recall and f-measure to roc, informedness, markedness and correlation, J. Machine Learning Technologies, 2011, Vol. 2(1), pp. 37−63.
  20. Qiu S., Zhu Z., Binbin H., Fmask 4.0: Improved cloud and cloud shadow detection in Landsats 4–8 and Sentinel-2 imagery, Remote Sensing of Environment, 2019, Vol. 231, pp. 83−94, DOI: 10.1016/j.rse.2019.05.024.
  21. S2 MPS. Sen2Cor Configuration and User Manual, Ref.: S2-PDGS-MPC-L2A-SUM-V2.4, Iss. 1, ESA, 2017, 53 p. available at: https://step.esa.int/thirdparties/sen2cor/2.4.0/Sen2Cor_240_Documenation_PDF/S2-PDGS-MPC-L2A-SUM-V2.4.0.pdf (accessed 20.07.2020).
  22. Sentinel-2 Products Specification Document, ESA, 2018, Ref.: S2-PDGS-TAS-DI-PSD, Issue 14.5, 510 p., available at: https://sentinel.esa.int/documents/247904/685211/Sentinel-2-Products-Specification-Document (accessed 20.07.2020).
  23. Shendryk Y., Rist Y., Ticehurst C., Thorbum P., Deep learning for multi-modal classification of cloud, shadow and land cover scenes in PlanetScope and Sentinel-2 imagery, ISPRS J. Photogrammetry and Remote Sensing, 2019, Vol. 157, pp. 124–136, DOI: 10.1016/j.isprsjprs.2019.08.018.
  24. Simonyan K., Zisserman A., Very deep convolutional networks for large-scale image recognition, Proc. 3rd Intern. Conf. Learning Representations, San Diego, 2014, arXiv: 1409.15556.
  25. Syrris V., Hasenohr P., Delipetrev B., Kotsev A., Kempeneers P., Soille P., Evaluation of the potential of convolutional neural networks and random forests for multi-class segmentation of Sentinel-2 imagery, Remote Sensing, 2019, Vol. 11(8), Art. No. 907, DOI: 10.3390/rs11080976.
  26. Xu K., Guan K., Peng J., Luo Y., Wang S., DeepMask: an algorithm for cloud and cloud shadow detection in optical satellite remote sensing images using deep residual network, ArXiv: 1911.03607, 2019, Vol. 1911, Article No. 3607.
  27. Zhe Z., Curtis E. W., Object-based cloud and cloud shadow detection in Landsat imagery, Remote Sensing of Environment, 2012. Vol. 118, pp. 83−94, DOI: 10.1016/j.rse.2011.10.028.