Catherine Pothier

Swap Randomization of Bases of Sequences for Mining Satellite Image Times Series

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.

SITS-P2miner: Pattern-Based Mining of Satellite Image Time Series

This paper presents a mining system for extracting patterns from Satellite Image Time Series. This system is a fully-fledged tool comprising four main modules for pre-processing, pattern extraction, pattern ranking and pattern visualization. It is based on the extraction of grouped frequent sequential patterns and on swap randomization.

A swap randomization approach for mining motion field time series over the Argentiere glacier

Grouped Frequent Sequential patterns can be extracted in an unsupervised way from Image Time Series (ITS). Plotting the occurrence maps of these patterns allows to describe the dataset spatially and temporally while discarding random uncertainties. However these maps can be too numerous and a swap randomization ranking approach has been proposed recently to select the most promising patterns. This previous work experimented the technique on Satellite ITS, giving credit to the maps that are least likely to appear on a randomized ITS. In this paper, extraction and ranking of GFS patterns is performed on a motion field time series obtained by terrestrial photogrammetry over the Argentière glacier. The focus is extended to the maps that are most likely to occur on the randomized time series and the experiment is repeated thousand times to assess the stability of the ranking.

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.

Ranking evolution maps for Satellite Image Time Series exploration: application to crustal deformation and environmental monitoring

Satellite Image Time Series (SITS) are large datasets containing spatiotemporal information about the surface of the Earth. In order to exploit the potential of such series, SITS analysis techniques have been designed for various applications such as earthquake monitoring, urban expansion assessment or glacier dynamic analysis. In this paper, we present an unsupervised technique for browsing SITS in preliminary explorations, before deciding whether to start deeper and more time consuming analyses. Such methods are lacking in today’s analyst toolbox, especially when it comes to stimulating the reuse of the ever growing list of available SITS. The method presented in this paper builds a summary of a SITS in the form of a set of maps depicting spatiotemporal phenomena. These maps are selected using an entropy-based ranking and a swap randomization technique. The approach is general and can handle either optical or radar SITS. As illustrated on both kinds of SITS, meaningful summaries capturing crustal deformation and environmental phenomena are produced. They can be computed on demand or precomputed once and stored together with the SITS for further usage.

Handling coherence measures of displacement field time series: Application to Greenland ice sheet glaciers

Displacement Field Time Series (DFTS) are often derived from Satellite Image Time Series to study dynamic systems such as glaciers. This analysis can be performed with pattern-based data mining techniques that search DFTS for all possible displacement evolutions. Nevertheless, existing pattern oriented methods do not take into account the coherence measures coming along with such DFTS data. This paper introduces an approach for handling the coherence when extracting displacement evolutions. In addition to defining a coherence notion for these evolutions, we show that focusing on the coherent patterns allows to prune the search space. Reported experiments exhibit consistent displacement evolutions of Greenland ice sheet glaciers.