Unsupervised Classification of Remote Sensing Data Using Graph Cut-Based Initialization

  • Mayank Tyagi
  • Ankit K Mehra
  • Subhasis Chaudhuri
  • Lorenzo Bruzzone
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3776)

Abstract

In this paper we propose a multistage unsupervised classifier which uses graph-cut to produce initial segments which are made up of pixels with similar spectral properties, subsequently labelled by a fuzzy c-means clustering algorithm into a known number of classes. These initial segmentation results are used as a seed to the expectation maximization (EM) algorithm. Final classification map is produced by using the maximum likelihood (ML) classifier, performance of which is quite good as compared to other unsupervised classification techniques.

Keywords

Expectation Maximization Algorithm Multi Spectral Image Remote Sensing Data Remote Sensing Image Ground Truth Information 
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 2005

Authors and Affiliations

  • Mayank Tyagi
    • 1
  • Ankit K Mehra
    • 1
  • Subhasis Chaudhuri
    • 1
  • Lorenzo Bruzzone
    • 2
  1. 1.IIT-BombayIndia
  2. 2.University of TrentoItaly

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