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• W2155118599 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" @default.
• W2155118599 created "2016-06-24" @default.
• W2155118599 creator A5028849596 @default.
• W2155118599 creator A5032370631 @default.
• W2155118599 date "2007-04-01" @default.
• W2155118599 modified "2023-10-01" @default.
• W2155118599 title "Image Time-Series Data Mining Based on the Information-Bottleneck Principle" @default.
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• W2155118599 doi "https://doi.org/10.1109/tgrs.2006.890557" @default.
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