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Transforming the autocorrelation function of a time series to detect land cover change


Salmon, BP and Kleynhans, W and Olivier, JC and Schwegmann, CP, Transforming the autocorrelation function of a time series to detect land cover change, Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 10-15 July 2016, Beijing, China, pp. 5181-5184. ISBN 978-1-5090-3332-4 (2016) [Refereed Conference Paper]

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Copyright 2016 IEEE

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DOI: doi:10.1109/IGARSS.2016.7730350


Regional monitoring of land cover conversion of natural vegetation to new informal human settlements is essential when investigating the migration of people to urbanized cities. Detecting these new settlements require reliable change detection methods. A robust change detection metric can be derived by analyzing the area under the autocorrelation function for a time series. The time dependence on the first and second moment causes a non-stationary event within the time series which results in non-symmetrical variations. In this work we explore the behavior of the autocorrelation function using new integration, differentiation and windowing approaches. Experiments were conducted in the Gauteng province of South Africa and we found a proper windowing function improved the overall detection accuracy.

Item Details

Item Type:Refereed Conference Paper
Keywords:autocorrelation, change detection, MODIS, time series
Research Division:Engineering
Research Group:Geomatic engineering
Research Field:Photogrammetry and remote sensing
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in engineering
UTAS Author:Salmon, BP (Dr Brian Salmon)
UTAS Author:Olivier, JC (Professor JC Olivier)
ID Code:114641
Year Published:2016
Deposited By:Engineering
Deposited On:2017-02-22
Last Modified:2018-04-05
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