Swap randomization of bases of sequences for mining satellite image times series

Abstract

Swap randomization has been shown to be an effective technique for assessing the significance of data mining results such as Boolean matrices, frequent itemsets, correlations or clusterings. Basically, instead of applying statistical tests on selected attributes, the global structure of the actual dataset is taken into account by checking whether obtained results are likely or not to occur in randomized datasets whose column and row margins are equal to the ones of the actual dataset. In this paper, a swap randomization approach for bases of sequences is proposed with the aim of assessing sequential patterns extracted from Satellite Image Time Series (SITS). This assessment relies on the spatiotemporal locations of the extracted patterns. Using an entropy-based measure, the locations obtained on the actual dataset and a single swap randomized dataset are compared. The potential and generality of the proposed approach is evidenced by experiments on both optical and radar SITS.