Analysing Satellite Image Time Series by Means of Pattern Mining

  • François Petitjean
  • Pierre Gançarski
  • Florent Masseglia
  • Germain Forestier
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6283)


Change detection in satellite image time series is an important domain with various applications in land study. Most previous works proposed to perform this detection by studying two images and analysing their differences. However, those methods do not exploit the whole set of images that is available today and they do not propose a description of the detected changes. We propose a sequential pattern mining approach for these image time series with two important features. First, our proposal allows for the analysis of all the images in the series and each image can be considered from multiple points of view. Second, our technique is specifically designed towards image time series where the changes are not the most frequent patterns that can be discovered. Our experiments show the relevance of our approach and the significance of our patterns.


Remote Sensing Change Detection Sequential Pattern Frequent Pattern Minimum Support 
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 2010

Authors and Affiliations

  • François Petitjean
    • 1
  • Pierre Gançarski
    • 1
  • Florent Masseglia
    • 2
  • Germain Forestier
    • 1
  1. 1.LSIIT (UMR 7005 CNRS/UdS), Bd Sébastien BrantIllkirchFrance
  2. 2.INRIA Sophia AntipolisSophia AntipolisFrance

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