In this paper, the internal operations of an Extended Kalman Filter is investigated to see if any useful information can be derived to detect land cover change in a MODIS time series. The Extended Kalman Filter expands its internal covariance if a significant change in reflectance value is observed, followed by adapting the state parameters to compensate for this change. The analysis shows a change detection accuracy above 90% can be attained when evaluating the elements within the internal covariance matrix to detect new human settlements, with a corresponding false alarm rate below 11%.
Reference:
Salmon, BP, Kleynhans, W, Van den Bergh, F, Olivier, JC and Wessels, KJ. 2012. Detecting land cover change by evaluating the internal covariance matrix of the extended Kalman filter. In: IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Munich, Germany, 22-27 July 2012
Salmon, B., Kleynhans, W., Van den Bergh, F., Olivier, J., & Wessels, K. J. (2012). Detecting land cover change by evaluating the internal covariance matrix of the extended Kalman filter. IEEE Xplore. http://hdl.handle.net/10204/6569
Salmon, BP, W Kleynhans, F Van den Bergh, JC Olivier, and Konrad J Wessels. "Detecting land cover change by evaluating the internal covariance matrix of the extended Kalman filter." (2012): http://hdl.handle.net/10204/6569
Salmon B, Kleynhans W, Van den Bergh F, Olivier J, Wessels KJ, Detecting land cover change by evaluating the internal covariance matrix of the extended Kalman filter; IEEE Xplore; 2012. http://hdl.handle.net/10204/6569 .
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