Finding a needle by removing the haystack: a spatio-temporal normalization method for geophysical data.

Research output: Contribution to journalArticleAcademicpeer-review

3 Citations (Scopus)

Abstract

We introduce a normalization algorithm which highlights short-term, localized, non-periodic fluctuations in hyper-temporal satellite data by dividing each pixel by the mean value of its spatial neighbourhood set. In this way we suppress signal patterns that are common in the central and surrounding pixels, utilizing both spatial and temporal information at different scales. We test the method on two subsets of a hyper-temporal thermal infra-red (TIR) dataset. Both subsets are acquired from the SEVIRI instrument onboard the Meteosat-9 geostationary satellite; they cover areas with different spatiotemporal TIR variability. We impose artificial fluctuations on the original data and apply a window-based technique to retrieve them from the normalized time series. We show that localized short-term fluctuations as low as 2 K, which were obscured by large-scale variable patterns, can be retrieved in the normalized time series. Sensitivity of retrieval is determined by the intrinsic variability of the normalized TIR signal and by the amount of missing values in the dataset. Finally, we compare our approach with widely used techniques of statistical and spectral analysis and we discuss the improvements introduced by our method.
Original languageEnglish
Pages (from-to)78-86
JournalComputers & geosciences
Volume90
Issue numberA
DOIs
Publication statusPublished - 2016

Fingerprint

Needles
pixel
time series
Infrared radiation
SEVIRI
Meteosat
geostationary satellite
Time series
Pixels
spectral analysis
satellite data
Geostationary satellites
statistical analysis
Spectrum analysis
Statistical methods
Satellites
method
normalisation
Hot Temperature
test

Keywords

  • METIS-316138
  • ITC-ISI-JOURNAL-ARTICLE

Cite this

@article{95b48f8bef754fe39aa651aa49ba9845,
title = "Finding a needle by removing the haystack: a spatio-temporal normalization method for geophysical data.",
abstract = "We introduce a normalization algorithm which highlights short-term, localized, non-periodic fluctuations in hyper-temporal satellite data by dividing each pixel by the mean value of its spatial neighbourhood set. In this way we suppress signal patterns that are common in the central and surrounding pixels, utilizing both spatial and temporal information at different scales. We test the method on two subsets of a hyper-temporal thermal infra-red (TIR) dataset. Both subsets are acquired from the SEVIRI instrument onboard the Meteosat-9 geostationary satellite; they cover areas with different spatiotemporal TIR variability. We impose artificial fluctuations on the original data and apply a window-based technique to retrieve them from the normalized time series. We show that localized short-term fluctuations as low as 2 K, which were obscured by large-scale variable patterns, can be retrieved in the normalized time series. Sensitivity of retrieval is determined by the intrinsic variability of the normalized TIR signal and by the amount of missing values in the dataset. Finally, we compare our approach with widely used techniques of statistical and spectral analysis and we discuss the improvements introduced by our method.",
keywords = "METIS-316138, ITC-ISI-JOURNAL-ARTICLE",
author = "E. Pavlidou and {van der Meijde}, M. and {van der Werff}, H.M.A. and C.A. Hecker",
year = "2016",
doi = "10.1016/j.cageo.2016.02.016",
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journal = "Computers & geosciences",
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Finding a needle by removing the haystack: a spatio-temporal normalization method for geophysical data. / Pavlidou, E.; van der Meijde, M.; van der Werff, H.M.A.; Hecker, C.A.

In: Computers & geosciences, Vol. 90, No. A, 2016, p. 78-86.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

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AU - Pavlidou, E.

AU - van der Meijde, M.

AU - van der Werff, H.M.A.

AU - Hecker, C.A.

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AB - We introduce a normalization algorithm which highlights short-term, localized, non-periodic fluctuations in hyper-temporal satellite data by dividing each pixel by the mean value of its spatial neighbourhood set. In this way we suppress signal patterns that are common in the central and surrounding pixels, utilizing both spatial and temporal information at different scales. We test the method on two subsets of a hyper-temporal thermal infra-red (TIR) dataset. Both subsets are acquired from the SEVIRI instrument onboard the Meteosat-9 geostationary satellite; they cover areas with different spatiotemporal TIR variability. We impose artificial fluctuations on the original data and apply a window-based technique to retrieve them from the normalized time series. We show that localized short-term fluctuations as low as 2 K, which were obscured by large-scale variable patterns, can be retrieved in the normalized time series. Sensitivity of retrieval is determined by the intrinsic variability of the normalized TIR signal and by the amount of missing values in the dataset. Finally, we compare our approach with widely used techniques of statistical and spectral analysis and we discuss the improvements introduced by our method.

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