Semantic indexing and temporal rule discovery for time-series satellite images
Rie Honda, Hirokazu Takimito, Osamu Konishi · 2000
Feature extraction and knowledge discovery from a large amount of image data such as remote sensing images have become highly required recentyears. In this study, we present a framework for data mining from a set of time-series images including moving objects using clustering by self-organizing mapping(SOM) and extraction of time-dependent association rules. We applied this method to weather satellite cloud images taken by GMS5 and evaluated its usefulness. The images are classified automatically bytwo-stage SOM. The results were examined and the cluster addresses were described in regard to season and prominent features such as typhoons or high-pressure masses. Sequential images are then transformed into a data series expressed by cluster addresses and time of occurrence, from which timedependent association rules (simple serial rules) are extracted using a method for finding frequently co-occurring term-pairs from text. Semantic indexed data and extracted rules are stored in the dat...