Title :
Image Time-Series Data Mining Based on the Information-Bottleneck Principle
Author :
Gueguen, Lionel ; Datcu, Mihai
fDate :
4/1/2007 12:00:00 AM
Abstract :
Satellite image time series (SITS) consist of a time sequence of high-resolution spatial data. SITS may contain valuable information, but it may be deeply hidden. This paper addresses the problem of extracting relevant information from SITS based on the information-bottleneck principle. The method depends on suitable model selection, coupled with a rate-distortion analysis for determining the optimal number of clusters. We present how to use this method with the Gauss-Markov random fields and the autobinomial random fields model families in order to characterize the spatio-temporal structures contained in SITS. Experimental results on synthetic data and SITS from SPOT demonstrate the performance of the proposed methodology
Keywords :
data mining; geophysical techniques; information retrieval; time series; Gauss-Markov random fields; SPOT; Satellite Pour l´Observation de la Terre; autobinomial random fields model; image time-series data mining; information-bottleneck principle; rate-distortion analysis; satellite image time series; Bayesian methods; Coupled mode analysis; Data mining; Gaussian processes; Image sensors; Information analysis; Layout; Monitoring; Satellites; Spatial resolution; Gibbs–Markov random field; information bottleneck; s atellite image time series (SITS); soft clustering; unsupervised clustering;
Journal_Title :
Geoscience and Remote Sensing, IEEE Transactions on
DOI :
10.1109/TGRS.2006.890557