Temporal Rule Discovery for Time-Series Satellite Images and Integration with RDB

  • Rie Honda
  • Osamu Konishi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2168)


Feature extraction and knowledge discovery from a large amount of image data such as remote sensing images have become highly required recent years. In this study, a framework for data mining from a set of time-series images including moving objects was presented. Time-series images are transformed into time-series cluster addresses by using clustering by two-stage SOM (Self-organizing map) and time-dependent association rules were extracted from it. Semantically indexed data and extracted rules are stored in the object-relational database, which allows high-level queries by entering SQL through the user interface. This method was applied to weather satellite cloud images taken by GMS-5 and its usefulness was evaluated.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Rie Honda
    • 1
  • Osamu Konishi
    • 1
  1. 1.Department of Mathematics and Information ScienceKochi UniversityJAPAN

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