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

Camille Kurtz; François Petitjean; Pierre Gançarski

doi:10.1109/IGARSS.2011.6049173

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 proceeding › Conference Paper › Other › peer-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 language	English
Title of host publication	2011 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011 - Proceedings
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	495-498
Number of pages	4
ISBN (Print)	9781457710056
DOIs	https://doi.org/10.1109/IGARSS.2011.6049173
Publication status	Published - 2011
Externally published	Yes
Event	IEEE International Geoscience and Remote Sensing Symposium 2011 - Vancouver Convention Center, Vancouver, Canada Duration: 24 Jul 2011 → 29 Jul 2011 http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6034618 (IEEE Conference Proceedings)

Conference

Conference	IEEE International Geoscience and Remote Sensing Symposium 2011
Abbreviated title	IGARSS 2011
Country	Canada
City	Vancouver
Period	24/07/11 → 29/07/11
Internet address	http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6034618 (IEEE Conference Proceedings)

Keywords

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

Access to Document

10.1109/IGARSS.2011.6049173

Link to publication in Scopus

Cite this

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title = "A context-based approach for the classification of satellite image time series",

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.",

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note = "IEEE International Geoscience and Remote Sensing Symposium 2011, IGARSS 2011 ; Conference date: 24-07-2011 Through 29-07-2011",

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Kurtz, C, Petitjean, F & Gançarski, P 2011, A context-based approach for the classification of satellite image time series. in 2011 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011 - Proceedings., 6049173, IEEE, Institute of Electrical and Electronics Engineers, pp. 495-498, IEEE International Geoscience and Remote Sensing Symposium 2011, Vancouver, Canada, 24/07/11. https://doi.org/10.1109/IGARSS.2011.6049173

A context-based approach for the classification of satellite image time series. / Kurtz, Camille; Petitjean, François; Gançarski, Pierre.

2011 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011 - Proceedings. IEEE, Institute of Electrical and Electronics Engineers, 2011. p. 495-498 6049173.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Other › peer-review

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