A Generalized Appriou’s Model for Evidential Classification of Multispectral Images: A Case Study of Algiers City

  • Abdenour Bouakache
  • Radja Khedam
  • Aichouche Belhadj-Aissa
  • Grégoire Mercier
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5259)


In this paper, we shall describe an evidential supervised classifier of multispectral satellite images. The evidence theory of Dempster-Shafer (DST) is used to take into account the ignorance and the uncertainty related to data, and so, overcome the Bayesian classifier limits. Notice that application fields of DST are initially related on multisensor, multitemporal and multiscale data fusion. In this study, our contribution lies in developing an evidential classification process that can be seen as a multisource fusion process where each predefined thematic class is considered as one source of information. The evidential mass functions of the considered thematic hypotheses are estimated using Appriou’s transfer model whose we propose to generalize to a multi-class case. Developed DST-classifier has been tested on multispectral ETM+ image covering the urban north-eastern part of Algiers (Algeria). The spectral validation of obtained evidential classes allows us to confirm the accuracy of the resulting land cover map.


Bare Soil Mass Function Multispectral Image Belief Function Evidence Theory 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Abdenour Bouakache
    • 1
  • Radja Khedam
    • 1
  • Aichouche Belhadj-Aissa
    • 1
  • Grégoire Mercier
    • 2
  1. 1.Image Processing and Radiation Laboratory, Faculty of Electronic and Computer ScienceUniversity of Science and Technology Houari Boumediene (USTHB)AlgiersAlgeria
  2. 2.ITI DptGET/ENST Bretagne CS 83818Brest Cedex3France

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