A context-based approach for the classification of satellite image time series

Camille Kurtz, François Petitjean, Pierre Gançarski

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

6 Citations (Scopus)

Abstract

Satellite Image Time Series (SITS) analysis is an important domain with various applications in land study. In the coming years, both high temporal and high spatial resolution SITS will be available. This article aims at providing both temporal and spatial analysis of SITS. We propose first segmenting each image of the series, and then using these segmentations in order to characterize each pixel of the data with a spatial dimension (i.e. with contextual information). Providing spatially characterized pixels, pixel-based temporal analysis can be performed. Experiments carried out with this methodology show the relevance of this approach and the significance of the resulting extracted patterns in the context of the analysis of SITS.

Original languageEnglish
Title of host publication2011 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011 - Proceedings
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages495-498
Number of pages4
ISBN (Print)9781457710056
DOIs
Publication statusPublished - 2011
Externally publishedYes
EventIEEE International Geoscience and Remote Sensing Symposium 2011 - Vancouver Convention Center, Vancouver, Canada
Duration: 24 Jul 201129 Jul 2011
http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6034618 (IEEE Conference Proceedings)

Conference

ConferenceIEEE International Geoscience and Remote Sensing Symposium 2011
Abbreviated titleIGARSS 2011
CountryCanada
CityVancouver
Period24/07/1129/07/11
Internet address

Keywords

  • Data Mining
  • Mean-Shift
  • Multi-temporal analysis
  • Satellite Image Time Series
  • Segmentation

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