Collaborative-Based Multi-scale Clustering in Very High Resolution Satellite Images

  • Jérémie SublimeEmail author
  • Antoine Cornuéjols
  • Younès Bennani
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9949)


In this article, we show an application of collaborative clustering applied to real data from very high resolution images. Our proposed method makes it possible to have several algorithms working at different scales of details while exchanging their information on the clusters.

Our method that aims at strengthening the hierarchical links between the clusters extracted at different level of detail has shown good results in terms of clustering quality based on common unsupervised learning indexes, but also when using external indexes: We compared our results with other algorithms and analyzed them based on an expert ground truth.


Multi-scale clustering Cluster analysis Image segmentation 



This work has been supported by the ANR Project COCLICO, ANR-12-MONU-0001.


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Jérémie Sublime
    • 1
    • 2
    • 3
    Email author
  • Antoine Cornuéjols
    • 1
  • Younès Bennani
    • 2
  1. 1.UMR MIA-Paris, AgroParisTech, INRA, Université Paris-SaclayParisFrance
  2. 2.Université Paris 13 - Sorbonne Paris Cité, Laboratoire d’Informatique de Paris-Nord - CNRS (UMR 7030)VilletaneuseFrance
  3. 3.LISITE Laboratory - RDI Team, ISEPParisFrance

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