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Improving NDVI time series class separation using an Extended Kalman Filter


Kleynhans, W and Olivier, JC and Salmon, BP and Wessels, KJ and van den Bergh, F, Improving NDVI time series class separation using an Extended Kalman Filter, Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 12-17 July 2009, Cape Town, South Africa, pp. 256-259. ISBN 978-1-4244-3395-7 (2009) [Refereed Conference Paper]

Copyright Statement

Copyright 2012 IEEE

DOI: doi:10.1109/IGARSS.2009.5417323


It is proposed that the NDVI time series derived from MODIS multitemporal remote sensing data can be modelled as a triply (mean, phase and amplitude) modulated cosine function. A non-linear Extended Kalman Filter was developed to estimate the parameters of the modulated cosine function as a function of time. It was shown that the maximum separability of the parameters for different vegetation land cover was better than that of a spectral method based on the Fast Fourier Transform (FFT). Thus it is theorized that the cosine function parameters estimated using the EKF is superior for both classifying land cover and detecting change over time when compared to methods based on the FFT. Results from two study areas in Southern Africa are provided to show the improved separability using MODIS data.

Item Details

Item Type:Refereed Conference Paper
Keywords:Extended Kalman Filter, Normalized Difference Vegetation Index, modulated cosine function
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:Olivier, JC (Professor JC Olivier)
UTAS Author:Salmon, BP (Dr Brian Salmon)
ID Code:84575
Year Published:2009
Deposited By:Engineering
Deposited On:2013-05-21
Last Modified:2014-12-11

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