Introducing prior knowledge in temporal distances for Satellite Image Time Series analysis

Francois Petitjean, Jordi Inglada, Pierre Gancarski

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

2 Citations (Scopus)

Abstract

Satellite Image Time Series are becoming increasingly available and will continue to do so in the coming years thanks to the launch of space missions which aim at providing a coverage of the Earth every few days with high spatial resolution. In the case of optical imagery, it will be possible to produce land use and cover change maps with detailed nomenclatures. It has been shown that the Dynamic Time Warping similarity measure is a consistent tool for the comparison of radiometric profiles of temporal evolution. Actually, it makes it possible to compare time series with both different lengths and different sampling. This property allows us to make the most of partially cloud-covered images, but also to transfer the knowledge learned on an agronomical year in order to classify the next year without using reference data. This article pursues this work on satellite image time series analysis and focuses on the introduction of constraints in the distance in order to fit to the expert's knowledge about the observed phenomena.

Original languageEnglish
Title of host publicationIGARSS 2012 - 2012 IEEE International Geoscience and Remote Sensing Symposium
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages5426-5429
Number of pages4
DOIs
Publication statusPublished - 2012
Externally publishedYes
EventIEEE International Geoscience and Remote Sensing Symposium 2012 - International Congress Centre, Munich, Germany
Duration: 22 Jul 201227 Jul 2012
Conference number: 32nd
http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6334512 (IEEE Conference Proceedings)

Conference

ConferenceIEEE International Geoscience and Remote Sensing Symposium 2012
Abbreviated titleIGARSS 2012
CountryGermany
CityMunich
Period22/07/1227/07/12
Internet address

Keywords

  • Crops
  • Image classification
  • Knowledge management
  • Remote Sensing
  • Time series analysis

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