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Satellite Image Classification Using a Divergence-Based Fuzzy c-Means Algorithm

  • Dong-Chul Park
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7340)

Abstract

A satellite image classifier scheme by using a Fuzzy c-Means (FcM) algorithm is proposed in this paper. The FcM algorithm adopted in this paper is a Gradient-based FcM with Divergence measure (GFcM(D)) and it utilizes the Divergence measure to exploit the statistical nature of the image data and thereby improves the classification accuracy. Experiments and results on a set of satellite images demonstrate that the proposed GFcM(D)-based classifier outperforms conventional algorithms such as the traditional Self-Organizing Map (SOM) and Fuzzy c-Means (FcM) in terms of classification accuracy.

Keywords

Satellite Image Discrete Cosine Transform Image Class Code Vector Harbor Area 
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 2012

Authors and Affiliations

  • Dong-Chul Park
    • 1
  1. 1.Dept. of Electronics EngineeringMyong Ji UniversityKorea

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