Urban area monitoring by sequential patterns extracted from multi-temporal satellite data using connectivity measures

Abstract

The temporal evolution of pixel values in Satellite Image Time Series (SITS) is considered criterion for the characterization, discrimination and identification of terrestrial objects and phenomena. Due to the exponential behavior of sequences number with specialization, Sequential Data Mining techniques need to be applied. The spatial aspect of the data was taken into account by the introduction of connectivity measures that characterize the pixels tendency to form objects. The conjunction of corresponding Connectivity Constraints (CC) with the Support Constraint (SC) leads to the extraction of Grouped Frequent Sequential Patterns (GFSP), a concept with proved capability for preliminary description and localization of terrestrial events. This work is focused on efficient SITS extraction of evolutions that fulfil SC and CC. Experiments performed on Bucharest urban interferometric SITS are used to illustrate the potential of the approach to find interesting evolution patterns.