Finding a needle by removing the haystack: A spatio-temporal normalization method for geophysical data. (May 2016)

• Record Type: Journal Article
• Authors: Pavlidou, E.; van der Meijde, M.; van der Werff, H.; Hecker, C.
• Is Part Of: Computers & geosciences. Volume 90(2016)Part A
• Page Start: 78
• Page End: 86
• Publication Date: 2016-05
• Subjects: Time series -- Anomaly detection -- Kernel-based approach -- Near-real time -- Satellite imagery; Environmental policy -- Periodicals; 550.5
• DOI: 10.1016/j.cageo.2016.02.016
• Languages: English
• ISSNs: 0098-3004

Abstract

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. Abstract : Highlights: We introduce a normalization approach for detection of extremes. We consider both the spatial and temporal dimensions of geophysical data. We apply the method and test its sensitivity on hyper-temporal satellite data.