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Satellite image time series clustering via affinity propagation

Satellite image time series (SITS) analysis is attracting more researchers recently because SITS have the advantage of fully capturing the dynamic changes of land cover and SITS data is becoming increasingly available. As an unsupervised classification method, clustering gains more importance due to frequent updates of labeled data or training samples are too expensive. When discussing SITS clustering, most researches focus on the similarity measure rather than clustering algorithm. However, the drawbacks of currently popular clustering algorithms tend to be amplified when tackling with SITS datasets. Therefore in this paper, we focus on the clustering algorithms and we find a novel method called affinity propagation is more suitable for SITS clustering. To demonstrate the accuracy and other advantages of affinity propagation, we conduct clustering experiments on MODIS and Landsat-TM SITS datasets. The obtained clustering maps are evaluated both visually and statistically comparing with other widely used clustering algorithms.