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


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2001-08-28

AUTHORS

Rie Honda , Osamu Konishi

ABSTRACT

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. More... »

PAGES

204-215

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/3-540-44794-6_17

DOI

http://dx.doi.org/10.1007/3-540-44794-6_17

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1037092098


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