Cloud Detection in High-Resolution Multispectral Satellite Imagery Using Deep Learning

  • Giorgio MoralesEmail author
  • Samuel G. Huamán
  • Joel Telles
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11141)


Cloud detection in high-resolution satellite images is a critical step for many remote sensing applications, but also a challenge, as such images have limited spectral bands. The contribution of this paper is twofold: We present a dataset called CloudPeru as well as a methodology for cloud detection in multispectral satellite images (approximately 2.8 meters per pixel) using deep learning. We prove that an agile Convolutional Neural Network (CNN) is able to distinguish between non-clouds and different types of clouds, including thin and very small ones, and achieve a classification accuracy of 99.94%. Each image is subdivided into superpixels by the SLICO algorithm, which are then processed by the trained CNN. Finally, we obtain the cloud mask by applying a threshold of 0.5 on the probability map. The results are compared with manually annotated images, showing a Kappa coefficient of 0.944, which is higher than that of compared methods.


Cloud detection High-resolution Convolutional neural networks Deep learning 



The authors would like to thank the National Commission for Aerospace Research and Development (CONIDA) and the National Institute of Research and Training in Telecommunications of the National University of Engineering (INICTEL-UNI) for the support provided.


  1. 1.
    Marais, I.V.Z., Du Preez, J.A., Steyn, W.H.: An optimal image transform for threshold-based cloud detection. Int. J. Remote Sens. 32(6), 1713–1729 (2011)CrossRefGoogle Scholar
  2. 2.
    Zhang, Q., Xiao, C.: Cloud detection of RGB color aerial photographs by progressive refinement scheme. IEEE Trans. Geosci. Remote Sens. 52(11), 7264–7275 (2014)CrossRefGoogle Scholar
  3. 3.
    Hang, Y., Kim, B., Kim, Y., Lee, W.H.: Automatic cloud detection for high spatial resolution multi-temporal. Remote Sens. Lett. 5(7), 601–608 (2014)CrossRefGoogle Scholar
  4. 4.
    Li, P., Dong, L., Xiao, H., Xu, M.: A cloud image detection method based on SVM vector machine. Neurocomputing 169, 34–42 (2015)CrossRefGoogle Scholar
  5. 5.
    Yuan, Y., Hu, X.: Bag-of-words and object-based classification for cloud extraction from satellite imagery. IEEE J. Sel. Topics Appl. Earth Observations Remote Sens. 8(8), 4197–4205 (2015)CrossRefGoogle Scholar
  6. 6.
    Bai, T., Deren, L., Sun, K., Chen, Y., Wenzhuo, L.: Cloud detection for high-resolution satellite imagery using machine learning and multi-feature fusion. Remote Sens. 8(9), 715 (2016)CrossRefGoogle Scholar
  7. 7.
    Morales, G., Huamán, S., Telles, J.: Cloud detection for PERUSAT-1 imagery using spectral and texture descriptors, ANN and panchromatic fusion. In: Proceedings of the 3rd Brazilian Technology Symposium - Emerging Trends and Challenges in Technology (BTSym). Springer, Campinas (2018, in press)Google Scholar
  8. 8.
    Shi, M., Xie, F., Zi, Y., Yin, J.: Cloud detection of remote sensing images by deep learning. In: 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 701–704. IEEE Press, Beijing (2016)Google Scholar
  9. 9.
    Xie, F., Shi, M., Shi, Z.: Multilevel cloud detection in remote sensing images based on deep learning. IEEE J. Sel. Topics Appl. Earth Observations Remote Sens. 10(8), 3631–3640 (2017)CrossRefGoogle Scholar
  10. 10.
    Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Süsstrunck, S.: SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Patt. Anal. Mach. Intell. 34(11), 2274–2282 (2012)CrossRefGoogle Scholar
  11. 11.
  12. 12.
    He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 1026–1034. IEEE Press, Vancouver (2015)Google Scholar
  13. 13.
    Kingma, D., Ba, J.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations (ICLR), San Diego (2015)Google Scholar
  14. 14.
    Kanungo, T., Mount, D.M., Netanyahu, N.S., Piatko, C.D., Silverman, R., Wu, A.Y.: An efficient k-means clustering algorithm: analysis and implementation. IEEE Trans. Patt. Anal. Mach. Intell. 24(7), 881–892 (2002)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Giorgio Morales
    • 1
    Email author
  • Samuel G. Huamán
    • 1
  • Joel Telles
    • 1
  1. 1.National Institute of Research and Training in Telecommunications (INICTEL-UNI)National University of EngineeringLimaPeru

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