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.
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Honda, R., Konishi, O. (2001). Temporal Rule Discovery for Time-Series Satellite Images and Integration with RDB. In: De Raedt, L., Siebes, A. (eds) Principles of Data Mining and Knowledge Discovery. PKDD 2001. Lecture Notes in Computer Science(), vol 2168. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44794-6_17
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DOI: https://doi.org/10.1007/3-540-44794-6_17
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