Detection of Cloud Cover in Satellite Imagery Using Semantic Segmentation

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Published Nov 26, 2022
https://doi.org/10.47164/ijngc.v13i5.903
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Volume 13, Special Issue 5, November 2022

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Sanay Jaju
Mohit Sahu
Akshat Surana
Kanak Mishra
Aarti Karandikar
Shri Ramdeobaba College of Engineering and Management, Nagpur
Dr. Avinash Agrawal
Shri Ramdeobaba College of Engineering and Management, Nagpur

Abstract

Accurate detection of cloud cover is an important task in the field of Remote Sensing of the environment. Currently, a lot of development is going on in this field by using various methods. Some of the methods apply concepts of machine learning (ML) whereas some apply deep learning. Since the accuracy of ML being lower than deep learning, the latter is preferred. This paper also uses the method of deep learning to detect cloud cover using images of satellite. This paper proposes a modified U-Net based deep learning model for cloud cover detection in satellite images. The model proposed is not as accurate as the original model, but it compensates for it by reducing the time for learning. The accuracy of the model came out to be 89.73%.

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite
Sanay Jaju, Mohit Sahu, Akshat Surana, Kanak Mishra, Aarti Karandikar, & Dr. Avinash Agrawal. (2022). Detection of Cloud Cover in Satellite Imagery Using Semantic Segmentation. International Journal of Next-Generation Computing, 13(5). https://doi.org/10.47164/ijngc.v13i5.903
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References

  1. Baetens, L., Desjardins, C., and Hagolle, O. 2019. Validation of copernicus sentinel-2 cloud masks obtained from maja, sen2cor, and fmask processors using reference cloud masks generated with a supervised active learning procedure. Remote Sensing 11, 4. DOI: https://doi.org/10.3390/rs11040433
  2. Diakogiannis, F. I., Waldner, F., Caccetta, P., and Wu, C. 2020. Resunet-a: A deep learning framework for semantic segmentation of remotely sensed data. ISPRS Journal of Photogrammetry and Remote Sensing 162, 94–114. DOI: https://doi.org/10.1016/j.isprsjprs.2020.01.013
  3. Francis, A., Sidiropoulos, P., and Muller, J.-P. 2019. Cloudfcn: Accurate and robust cloud detection for satellite imagery with deep learning. Remote Sensing 11, 19. DOI: https://doi.org/10.3390/rs11192312
  4. Frantz, D., Haß, E., Uhl, A., Stoffels, J., and Hill, J. 2018. Improvement of the fmask algorithm for sentinel-2 images: Separating clouds from bright surfaces based on parallax effects. Remote Sensing of Environment 215, 471–481. DOI: https://doi.org/10.1016/j.rse.2018.04.046
  5. Guo, Y., Cao, X., Liu, B., and Gao, M. 2020. Cloud detection for satellite imagery using attention-based u-net convolutional neural network. Symmetry 12, 6. DOI: https://doi.org/10.3390/sym12061056
  6. Hagolle, O., Huc, M., Desjardins, C., Auer, S., and Richter, R. 2017. Maja algorithm theoretical basis document. cnes, cesbio & dlr report ref maja-tn-wp2-030 issue 1.0.
  7. He, K., Zhang, X., Ren, S., and Sun, J. 2015. Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 37, 9, 1904–1916. DOI: https://doi.org/10.1109/TPAMI.2015.2389824
  8. Hua, Y., Marcos, D., Mou, L., Zhu, X. X., and Tuia, D. 2022. Semantic segmentation of remote sensing images with sparse annotations. IEEE Geoscience and Remote Sensing Letters 19, 1–5. DOI: https://doi.org/10.1109/LGRS.2021.3051053
  9. Jaju, S. and Chandak, M. 2022. A transfer learning model based on resnet-50 for flower detection. 2022 International Conference on Applied Artificial Intelligence and Computing (ICAAIC), 307–311. DOI: https://doi.org/10.1109/ICAAIC53929.2022.9792697
  10. Kemker, R., Salvaggio, C., and Kanan, C. 2018. Algorithms for semantic segmentation of multispectral remote sensing imagery using deep learning. ISPRS Journal of Photogrammetry and Remote Sensing 145, 60–77. Deep Learning RS Data. DOI: https://doi.org/10.1016/j.isprsjprs.2018.04.014
  11. Laliberte, A. S., Goforth, M. A., Steele, C. M., and Rango, A. 2011. Multispectral remote sensing from unmanned aircraft: Image processing workflows and applications for rangeland environments. Remote Sensing 3, 11, 2529–2551. DOI: https://doi.org/10.3390/rs3112529
  12. Long, J., Shelhamer, E., and Darrell, T. 2015. Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). DOI: https://doi.org/10.1109/CVPR.2015.7298965
  13. Qiu, S., Zhu, Z., and He, B. 2019. Fmask 4.0: Improved cloud and cloud shadow detection in landsats 4–8 and sentinel-2 imagery. Remote Sensing of Environment 231, 111205. DOI: https://doi.org/10.1016/j.rse.2019.05.024
  14. Tian, Z., He, T., Shen, C., and Yan, Y. 2019. Decoders matter for semantic segmentation: Data-dependent decoding enables flexible feature aggregation. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). DOI: https://doi.org/10.1109/CVPR.2019.00324
  15. Wang, T., Shi, J., Letu, H., Ma, Y., Li, X., and Zheng, Y. 2019. Detection and removal of clouds and associated shadows in satellite imagery based on simulated radiance fields. Journal of Geophysical Research: Atmospheres 124, 13, 7207–7225. DOI: https://doi.org/10.1029/2018JD029960
  16. Xu, Z., Zhang, W., Zhang, T., and Li, J. 2021. Hrcnet: High-resolution context extraction network for semantic segmentation of remote sensing images. Remote Sensing 13, 1. DOI: https://doi.org/10.3390/rs13010071
  17. Zhu, X. X., Tuia, D., Mou, L., Xia, G.-S., Zhang, L., Xu, F., and Fraundorfer, F. 2017. Deep learning in remote sensing: A comprehensive review and list of resources. IEEE Geoscience and Remote Sensing Magazine 5, 4, 8–36. DOI: https://doi.org/10.1109/MGRS.2017.2762307
  18. Zhu, Z. and Woodcock, C. E. 2014. Automated cloud, cloud shadow, and snow detection in multitemporal landsat data: An algorithm designed specifically for monitoring land cover change. Remote Sensing of Environment 152, 217–234. DOI: https://doi.org/10.1016/j.rse.2014.06.012
  19. Zupanc, A. 2017. Improving cloud detection with machine learning. Accessed: Oct 10, 2019.