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Image Time-Series Data Mining Based on the Information-Bottleneck Principle
2007
IEEE Transactions on Geoscience and Remote Sensing
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
doi:10.1109/tgrs.2006.890557
fatcat:vnlsszlgh5alrbyskbtkulmz24