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Improving NDVI time series class separation using an Extended Kalman Filter
conference contribution
posted on 2023-05-23, 07:36 authored by Kleynhans, W, Jan OlivierJan Olivier, Brian SalmonBrian Salmon, Wessels, KJ, van den Bergh, FIt 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.
History
Publication title
Proceedings of the IEEE International Geoscience and Remote Sensing SymposiumVolume
4Pagination
256-259ISBN
978-1-4244-3395-7Department/School
School of EngineeringPublisher
IEEEPlace of publication
USAEvent title
IEEE International Geoscience and Remote Sensing SymposiumEvent Venue
Cape Town, South AfricaDate of Event (Start Date)
2009-07-12Date of Event (End Date)
2009-07-17Rights statement
Copyright 2012 IEEERepository Status
- Restricted