Stochastic Approaches of Minimum Distance Method for Region Based Classification

  • Rogério G. Negri
  • Luciano V. Dutra
  • Sidnei J. S. Sant’Anna
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7441)


Normally remote sensing image classification is performed pixelwise which produces a noisy classification. One way of improving such results is dividing the classification process in two steps. First, uniform regions by some criterion are detected and afterwards each unlabeled region is assigned to class of the “nearest” class using a so-called stochastic distance. The statistics are estimated by taking in account all the reference pixels. Three variations are investigated. The first variation is to assign to the unlabeled region a class that has the minimum average distance between this region and each one of reference samples of that class. The second is to assign the class of the closest reference sample. The third is to assign the most frequent class of the k closest reference regions. A simulation study is done to assess the performances. The simulations suggested that the most robust and simple approach is the second variation.


region based classification stochastic distances image simulation remote sensing 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Rogério G. Negri
    • 1
  • Luciano V. Dutra
    • 1
  • Sidnei J. S. Sant’Anna
    • 1
  1. 1.Divisão de Processamento de Imagens – DPIInstituto Nacional de Pesquisas Espaciais – INPESão José dos CamposBrasil

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