[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content

Performance Comparison of Fusion Operators in Bimodal Remote Sensing Snow Detection

  • Conference paper
Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications (IPMU 2010)

Abstract

This contribution describes the system developed and implemented for the detection of snow based on the fusion of optical and Synthetic Aperture Radar (SAR) remote sensing modalities. The work is focused on the performance comparison of different fusion operators for the implementation of the fusion stage. In case of the optical signal the so-called Normalized Difference Snow Index (NDSI) is used, whereas in SAR, the binary presence of wet and dry snow are used. We take into account soft data fusion, a framework where several operators are included. The comparison is undertaken on a set of satellite images by computing the standard Receiver Operating Curves (ROC) and the corresponding Area Under the Curves (AUC).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
GBP 19.95
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
GBP 71.50
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
GBP 89.99
Price includes VAT (United Kingdom)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Bloch, I.: Information combination operators for data fusion: a comparative review with classification. IEEE Transactions on Systems, Man and Cybernetics, Part A 26(1), 52–67 (1996)

    Article  Google Scholar 

  2. Bullen, P.: Handbook of Means and their Inequalities. Kluwer, Dordrecht (2003)

    MATH  Google Scholar 

  3. De Baets, B., Fodor, J.: Van melle’s combining function in mycin is a representable uninorm: an alternative proof. Fuzzy Sets Syst. 104(1), 133–136 (1999)

    Article  MATH  Google Scholar 

  4. Fawcett, T.: An introduction to roc analysis. Pattern Recogn. Lett. 27(8), 861–874 (2006), http://dx.doi.org/10.1016/j.patrec.2005.10.010

    Article  MathSciNet  Google Scholar 

  5. Haefner, H., Piesbergen, J.: High alpine snow cover monitoring osing ers-1 sar and landsat tm data. In: Baumgartner, M., Schultz, G.A., Johnson, A.I. (eds.) Remote Sensing and Gographie Information Systems for Design and Operation of Water Resources Systems (Proceedings of Rabat Symposium S3) April 1997, vol. 242. IAHS Publ. (1997)

    Google Scholar 

  6. Hall, D.K., Riggs, G.A., Salomonson, V.V.: Development of methods for mapping global snow cover using moderate resolution imaging spectroradiometer data. Remote Sensing of Environment 54(2), 127–140 (1995)

    Article  Google Scholar 

  7. Klement, E.P., Mesiar, R., Pap, E.: Triangular Norms. Trends in Logic, vol. 8, Ist edn. Springer, Heidelberg (July 2000)

    MATH  Google Scholar 

  8. Kropatsch, W.G., Strobl, D.: The generation of sar layover and shadow maps from digital elevation models. IEEE Transactions on Geoscience and Remote Sensing 28(1), 98–107 (2002)

    Article  Google Scholar 

  9. Menger, K.: Statistical metrics. Proceedings of the National Academy of Sciences of the United States of America 28(12), 535–537 (1942)

    Article  MATH  MathSciNet  Google Scholar 

  10. Nagler, T., Rott, H.: Retrieval of wet snow by means of multitemporal sar data. IEEE Transactions on Geoscience and Remote Sensing 38(2), 754–765 (2002)

    Article  Google Scholar 

  11. Rudas, I.J.: New types of aggregation operators in intelligent systems: absorbing norms and evolutionary operators. In: Proceedings of IEEE International Symposium on Industrial Electronics, ISIE 2001, vol. 1, pp. 404–412 (2001)

    Google Scholar 

  12. Rudas, I.J., Fodor, J.: Information aggregation in intelligent systems using generalized operators. International Journal of Computers, Communications & Control 1(1), 47–57 (2006)

    Google Scholar 

  13. Salomonson, V., Appel, I.: Estimating fractional snow cover from modis using the normalized difference snow index. Remote Sens. Envir. 89(3), 351–360 (2004)

    Article  Google Scholar 

  14. Soria-Frisch, A.: Soft Data Fusion for Computer Vision. Ph.D. thesis, TU Berlin (2005)

    Google Scholar 

  15. Soria-Frisch, A.: Optical-SAR fusion based snow detection. Tech. Rep. TN00186, Starlab Barcelona (2009)

    Google Scholar 

  16. Storvold, R., Malnes, E.: Snow covered area retrieval using envisat asar wideswath in mountainous areas. In: Proc. IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2004, vol. 3, pp. 1845–1848 (2004)

    Google Scholar 

  17. Thorp, E.: Best possible triangle inequalities for statistical metric spaces. Proceedings of the American Mathematical Society 11(5), 734–740 (1960)

    Article  MATH  MathSciNet  Google Scholar 

  18. Yager, R.R.: On ordered weighted averaging aggregation operators in multicriteria decisionmaking. IEEE Trans. Syst. Man Cybern. 18(1), 183–190 (1988)

    Article  MATH  MathSciNet  Google Scholar 

  19. Yager, R.R., Rybalov, A.: Uninorm aggregation operators. Fuzzy Sets Syst. 80(1), 111–120 (1996)

    Article  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Soria-Frisch, A., Repucci, A., Moreno, L., Caparrini, M. (2010). Performance Comparison of Fusion Operators in Bimodal Remote Sensing Snow Detection. In: Hüllermeier, E., Kruse, R., Hoffmann, F. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications. IPMU 2010. Communications in Computer and Information Science, vol 81. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14058-7_22

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-14058-7_22

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-14057-0

  • Online ISBN: 978-3-642-14058-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics