Using Dempster–Shafer to incorporate knowledge into satellite image classification | Artificial Intelligence Review Skip to main content
Log in

Using Dempster–Shafer to incorporate knowledge into satellite image classification

  • Published:
Artificial Intelligence Review Aims and scope Submit manuscript

Abstract

Remote sensing imaging techniques make use of data derived from high resolution satellite sensors. Image classification identifies and organises pixels of similar spatial distribution or similar statistical characteristics into the same spectral class (theme). Contextual data can be incorporated, or ‘fused’, with spectral data to improve the accuracy of classification algorithms. In this paper we use Dempster–Shafer’s theory of evidence to achieve this data fusion. Incorporating a Knowledge Base of evidence within the classification process represents a new direction for the development of reliable systems for image classification and the interpretation of remotely sensed data.

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

Access this article

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Anand S, Scotney B, Tan M, McClean S, Bell D, Hughes J and Magill I (1997). Designing a kernel for data mining. IEEE Expert 12: 65–74

    Article  Google Scholar 

  2. Benediktsson J, Swain P and Ersoy O (1990). Neural network approaches versus statistical methods in classification of multisource remote sensing data. IEEE Trans Geosci Remote Sens 28(4): 540–552

    Article  Google Scholar 

  3. Bourman C and Shapiro M (1994). A multiscale random field model for bayesian image segmentation. IEEE Trans Image Process 3(2): 162–177

    Article  Google Scholar 

  4. Cheeseman P and Stutz J (1996). Bayesian classification: theory and results. In: Fayyad, U, Piatetsky, S, Smyth, P and Uthurusamy, R (eds) Advances in knowledge Discovery and Data Mining, pp 153–189. AAAI/MIT Press, Cambridge, MA

    Google Scholar 

  5. Congalton R (2001). Accuracy assessment and validation of remotely sensed and other spatial information. Int J Wildland Fire 10: 321–328

    Article  Google Scholar 

  6. Debeir O, Latinne P and Van Den Steen I (2001). Remotes sensing classification of spectral, spatial and contextual data using multiple classifier systems. Image Anal Stereo 1(20): 584–589

    Google Scholar 

  7. Dempster A (1968). A generalisation of Bayesian inference. J R Stat Soc B 30: 205–247

    MathSciNet  Google Scholar 

  8. Dengsheng L and Qihao W (2005). Urban classification using full spectral information of Landsat ETM + Imagery in Marion county, Indiana. P E & RS 71(11): 1275–1284

    Google Scholar 

  9. Deren LI, Kaichang DI, Deyi LI (2000) Land use classification of remote sensing image with GIS data based on spatial data mining techniques. Int Arch Photogrametric Remote Sens XXXIII (Part B3)

  10. Dhodhi M, Saghri J, Ahmed I and Ul-Mustafa R (1999). D-ISODATA: a distributed algorithms for unsupervised classification of remotely sensed data on network of workstations. J Parallel Distrib Comput 59: 280–301

    Article  Google Scholar 

  11. Duda T and Canty M (2002). Unsupervised classification of satellite imagery: choosing a good algorithm. Int J Remote Sens 23(11): 2193–2212

    Article  Google Scholar 

  12. Egmont-Peterson M, De Ridder D and Handles H (2002). Image processing with neural networks—a review. Pattern Recognit 35: 2279–2301

    Article  Google Scholar 

  13. Fangju W (1999). Fuzzy supervised classification of remotely sensing images. IEEE Trans Geosci Remote Sens 28(2): 194–201

    Google Scholar 

  14. Gibson P and Power C (2000). Introductory remote sensing, digital image processing applications. Taylor and Francis Group, Routledge

    Google Scholar 

  15. Gorte B and Stein A (1998). Bayesian classification and class area estimation of satellite images using stratification. IEEE Trans Geosci Remote Sens 36(3): 803–812

    Article  Google Scholar 

  16. Guan J and Bell D (1991). Evidence theory and its applications. Elsevier Science Inc., New York

    MATH  Google Scholar 

  17. Guan J and Bell D (1992). Evidence theory and its applications. Elsevier Science Inc., New York

    MATH  Google Scholar 

  18. H’egarat-Mascle S, Richard D Le and Ottl’e C (2003). Multi-scale data fusion using Dempster–Shafer evidence theory. Integr Comput Aided Eng 10: 9–22

    Google Scholar 

  19. Huadong W, Siegel M, Stiefelhagen R and Jie Y (2002). Sensor fusion using Dempster–Shafer theory. Proc 19th IEEE Instrum Meas Technol Conf 1: 7–12

    Google Scholar 

  20. Jackson Q and Landgrebe D (2002). Adaptive Bayesian contextual classification based on Markov random fields. IEEE Trans Geosci Remote Sens 40(11): 2454–2463

    Article  Google Scholar 

  21. Jensen J (1996). Introductory digital image Processing, a remote sensing prospective. Prentice Hall, Englewood cliffs

    Google Scholar 

  22. Jia X and Richards J (1994). Efficient maximum likelihood classification for image imaging spectrometer data sets. IEEE Trans Geosci Remote Sens 3(2): 274–281

    Google Scholar 

  23. Lillesand TM and Kiefer RW (2000). Remote sensing and image interpretation. John Wiley & Sons, New York

    Google Scholar 

  24. Mascle S, Bloch I and Vidal-Madjar D (1997). Application of Dempster–Shafer evidence theory to unsupervised classification in multisource remote sensing. IEEE Trans Geosci Remote Sens 35(4): 1018–1031

    Article  Google Scholar 

  25. Mather P (1999). Computer processing of remotely-sensed images analysis, An Introduction,. John Wiley & Sons, Chichester

    Google Scholar 

  26. McClean S and Scotney B (1997). Using evidence theory for the integration of distributed databases. Int J Intell Syst 12: 763–776

    Article  Google Scholar 

  27. Murai H and Omatsu S (1997). Remote sensing image analysis using neural and knowledge-based processing. Int J Remote Sens 18(4): 811–828

    Article  Google Scholar 

  28. Richards J and Jia X (1999). Remote sensing digital image analysis an Introduction. Springer, New York

    Google Scholar 

  29. Shafer G (1982). Belief functions and parametric models. J R Stat Soc B 44: 322–352

    MATH  MathSciNet  Google Scholar 

  30. Torsun I (1995). Foundation of intelligent Knowledge-based systems. Academic Press, San Diego

    Google Scholar 

  31. U.S. Geological Survey Web Site (2006) http://seamless.usgs.gov/. Cited 30 Nov 2006

  32. Warrender C and Augusteijn M (1999). Fusion of image classification using Bayesian techniques with Markov random fields. Int J Remote Sens 20(10): 1987–2002

    Article  Google Scholar 

  33. Engemann K, Filev D and Yager (1995). On the concept of immediate probabilities. Int J Intell Syst 10: 373–397

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Bilal Al Momani.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Al Momani, B., McClean, S. & Morrow, P. Using Dempster–Shafer to incorporate knowledge into satellite image classification. Artif Intell Rev 25, 161–178 (2006). https://doi.org/10.1007/s10462-007-9027-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10462-007-9027-4

Keywords

Navigation