A Pattern-Based Method For Handling Confidence Measures While Mining Satellite Displacement Field Time Series: Application to Greenland Ice Sheet and Alpine Glaciers

Abstract

For more than 40 years, Earth observation satellites have been regularly providing images of glaciers that can be used to derive surface displacement fields and study their dynamics. In the context of global warming, the analysis of displacement field time series (DFTS) can provide useful information. Efficient data mining techniques are, thus, required to extract meaningful displacement evolutions from such large and complex datasets. In this paper, a pattern-based data mining approach, which handles confidence measures, is proposed to analyze DFTS. In order to focus on the most reliable measurements, a displacement evolution reliability measure is defined. It is aimed at assessing the quality of each evolution and pruning the search space. Experiments on two different DFTS (annual displacement fields derived from optical data over Greenland ice sheet and 11-day displacement fields derived from synthetic aperture radar data over Alpine glaciers) show the potential of the proposed approach.