Application of Dempster-Shafer evidence theory to unsupervised classification in multisource remote sensing

@article{HgaratMascle1997ApplicationOD,
  title={Application of Dempster-Shafer evidence theory to unsupervised classification in multisource remote sensing},
  author={Sylvie Le H{\'e}garat-Mascle and Isabelle Bloch and Daniel Vidal-Madjar},
  journal={IEEE Trans. Geoscience and Remote Sensing},
  year={1997},
  volume={35},
  pages={1018-1031}
}
The aim of this paper is to show that Dempster–Shafer evidence theory may be successfully applied to unsupervised classification in multisource remote sensing. Dempster–Shafer formulation allows to consider unions of classes, and to represent both imprecision and uncertainty, through the definition of belief and plausibility functions. These two functions, derived from mass function, are generally chosen in a supervised way. In this paper, we describe an unsupervised method, based on the… CONTINUE READING
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