Skip to main content
Book cover

Brazilian Technology Symposium

BTSym 2017: Proceedings of the 3rd Brazilian Technology Symposium pp 1–7Cite as

Cloud Detection for PERUSAT-1 Imagery Using Spectral and Texture Descriptors, ANN, and Panchromatic Fusion

Abstract

The cloud detection process is a prerequisite for many remote sensing applications in order to use only those cloud-free parts of satellite images and reduce errors of further automatic detection algorithms. In this paper, we present a method to detect clouds in high-resolution images of 2.8 m per pixel approximately. The process is performed over those pixels that exceed a defined threshold of blue normalized difference vegetation index to reduce the execution time. From each pixel, a set of texture descriptors and reflectance descriptors are processed in an Artificial Neural Network. The texture descriptors are extracted using the Gray-Level Co-occurrence Matrix. Each detection result passes through a false-positive discard procedure on the blue component of the panchromatic fusion based on image processing techniques such as Region growing, Hough transform, among others. The results show a minimum Kappa coefficient of 0.80 and an average of 0.94 over a set of 25 images from the Peruvian satellite PERUSAT-1, operational since December 2016.

Keywords

  • Cloud detection
  • High-resolution
  • Artificial neural networks
  • Texture analysis

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-93112-8_1
  • Chapter length: 7 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   169.00
Price excludes VAT (USA)
  • ISBN: 978-3-319-93112-8
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   219.99
Price excludes VAT (USA)
Hardcover Book
USD   219.99
Price excludes VAT (USA)
Learn about institutional subscriptions
Fig. 1
Fig. 2
Fig. 3
Fig. 4

References

  1. Tseng, D.C., Tseng, H.T., Chien, C.L.: Automatic cloud removal from multi-temporal spot images. Appl. Math. Comput. 205(2), 584–600 (2008)

    MathSciNet  MATH  Google Scholar 

  2. 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)

    CrossRef  Google Scholar 

  3. 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)

    CrossRef  Google Scholar 

  4. Li, P., Dong, L., Xiao, H., Xu, M.: A cloud image detection method based on SVM vector machine. Neurocomputing 169, 34–42 (2015)

    CrossRef  Google Scholar 

  5. Bai, T., et al.: Cloud detection for high-resolution satellite imagery using machine learning and multi-feature fusion. Remote Sens. 8(9), 715 (2016)

    CrossRef  Google Scholar 

  6. Shi, M., et al.: Cloud detection of remote sensing images by deep learning. In: 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 701–704. IEEE, Beijing (2016)

    Google Scholar 

  7. Wang, F., et al.: New vegetation index and its application in estimating leaf area index of rice. Rice Sci. 14(3), 195–203 (2007)

    CrossRef  Google Scholar 

  8. Tsai, F., Chou, M.J.: Texture augmented analysis of high resolution satellite imagery in detecting invasive plant species. J Chin. Inst. Eng. 29(4), 581–592 (2006)

    CrossRef  Google Scholar 

  9. Vivone, G., et al.: Critical comparison among pansharpening algorithms. IEEE Trans. Geosci. Remote Sens. 53(5), 2565–2586 (2015)

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. National Institute of Research and Training at Telecommunications (INICTEL-UNI), National University of Engineering, San Luis 1771, Lima, 15021, Peru

    Giorgio Morales, Samuel G. Huamán & Joel Telles

Authors
  1. Giorgio Morales
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Samuel G. Huamán
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Joel Telles
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Giorgio Morales .

Editor information

Editors and Affiliations

  1. Faculty of Electrical and Computer Engineering, University of Campinas, Campinas, São Paulo, Brazil

    Prof. Dr. Yuzo Iano

  2. Faculty of Technology, University of Campinas, Campinas, São Paulo, Brazil

    Prof. Dr. Rangel Arthur

  3. Divisão de Engenharia Eletrônica, Instituto Tecnológico de Aeronáutica (ITA), São José dos Campos, São Paulo, Brazil

    Prof. Osamu Saotome

  4. Universidade Federal Fluminense (UFF), Duque de Caxias, Rio de Janeiro, Brazil

    Prof. Vania Vieira Estrela

  5. Faculty of Electrical and Computer Engineering, University of Campinas, Campinas, São Paulo, Brazil

    Prof. Dr. Hermes José Loschi

Rights and permissions

Reprints and Permissions

Copyright information

© 2019 Springer International Publishing AG, part of Springer Nature

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Morales, G., Huamán, S.G., Telles, J. (2019). Cloud Detection for PERUSAT-1 Imagery Using Spectral and Texture Descriptors, ANN, and Panchromatic Fusion. In: Iano, Y., Arthur, R., Saotome, O., Vieira Estrela, V., Loschi, H. (eds) Proceedings of the 3rd Brazilian Technology Symposium. BTSym 2017. Springer, Cham. https://doi.org/10.1007/978-3-319-93112-8_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-93112-8_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-93111-1

  • Online ISBN: 978-3-319-93112-8

  • eBook Packages: EngineeringEngineering (R0)