Iterative summarization of satellite image time series

In this paper we present a method to summarize a satellite image time series. This summary is a small set of maps depicting salient phenomena occurring in the series over space and time. The approach is composed of a first step of extraction of spatiotemporal patterns, followed by an iterative ranking of these patterns using a swap randomization technique and a ranking based on a normalized mutual information measure. The best ranked patterns in the earliest iterations are in some sense the most informative and are used to build the summary. We present results showing that the approach is effective on both optical and radar data.