Connectivity constraint-based sequential pattern extraction from Satellite Image Time Series (SITS)

Andreea Julea; Nicolas Méger

doi:10.1117/12.2028980

17 October 2013 Connectivity constraint-based sequential pattern extraction from Satellite Image Time Series (SITS)

Andreea Julea, Nicolas Méger

Proceedings Volume 8892, Image and Signal Processing for Remote Sensing XIX; 889213 (2013) https://doi.org/10.1117/12.2028980
Event: SPIE Remote Sensing, 2013, Dresden, Germany

Abstract

The temporal evolution of pixel values in Satellite Image Time Series (SITS) is considered as 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 (SDM) techniques need to be applied. The huge search and solution spaces imply the use of constraints according to the user’s knowledge, interest and expectation. The spatial aspect of the data was taken into account by the introduction of connectivity measures that characterize the pixels tendency to form objects. These measures can highlight stratifications in data structure, can be useful for shape recognition and offer a base for post-processing operations similar to those from mathematical morphology (dilation, erosion etc.). 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 fulfill SC and CC. Different types of extractions using anti-monotone constraints are analyzed. Experiments performed on two interferometric SITS are used to illustrate the potential of the approach to find interesting evolution patterns.

Citation Download Citation

Andreea Julea and Nicolas Méger "Connectivity constraint-based sequential pattern extraction from Satellite Image Time Series (SITS)", Proc. SPIE 8892, Image and Signal Processing for Remote Sensing XIX, 889213 (17 October 2013); https://doi.org/10.1117/12.2028980

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