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IGARSS
2009

Improving NDVI Time Series Class Separation using an Extended Kalman Filter

9 years 5 months ago
Improving NDVI Time Series Class Separation using an Extended Kalman Filter
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.
Waldo Kleynhans, J. Corne Olivier, Brian P. Salmon
Added 20 Feb 2011
Updated 20 Feb 2011
Type Journal
Year 2009
Where IGARSS
Authors Waldo Kleynhans, J. Corne Olivier, Brian P. Salmon, Konrad J. Wessels, Frans van den Bergh
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