Analysing satellite image time series by means of pattern mining

François Petitjean, Pierre Gançarski, Florent Masseglia, Germain Forestier

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

21 Citations (Scopus)


Change detection in satellite image time series is an important domain with various applications in land study. Most previous works proposed to perform this detection by studying two images and analysing their differences. However, those methods do not exploit the whole set of images that is available today and they do not propose a description of the detected changes. We propose a sequential pattern mining approach for these image time series with two important features. First, our proposal allows for the analysis of all the images in the series and each image can be considered from multiple points of view. Second, our technique is specifically designed towards image time series where the changes are not the most frequent patterns that can be discovered. Our experiments show the relevance of our approach and the significance of our patterns.

Original languageEnglish
Title of host publicationIntelligent Data Engineering and Automated Learning, IDEAL 2010 - 11th International Conference, Proceedings
Number of pages8
Volume6283 LNCS
Publication statusPublished - 2010
Externally publishedYes
Event11th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2010 - Paisley, United Kingdom
Duration: 1 Sep 20103 Sep 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6283 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349


Conference11th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2010
Country/TerritoryUnited Kingdom

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