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Detecting land cover change by evaluating the internal covariance matrix of the extended Kalman filter

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dc.contributor.author Salmon, BP
dc.contributor.author Kleynhans, W
dc.contributor.author Van den Bergh, F
dc.contributor.author Olivier, JC
dc.contributor.author Wessels, Konrad J
dc.date.accessioned 2013-02-25T05:46:49Z
dc.date.available 2013-02-25T05:46:49Z
dc.date.issued 2012-07
dc.identifier.citation 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 en_US
dc.identifier.uri http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=6352676&contentType=Conference+Publications
dc.identifier.uri http://hdl.handle.net/10204/6569
dc.description IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Munich, Germany, 22-27 July 2012 en_US
dc.description.abstract 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%. en_US
dc.language.iso en en_US
dc.publisher IEEE Xplore en_US
dc.relation.ispartofseries Workflow;9563
dc.subject Change detection algorithms en_US
dc.subject Covariance matrix en_US
dc.subject Kalman Filter en_US
dc.subject Spatial information en_US
dc.subject Time series analysis en_US
dc.title Detecting land cover change by evaluating the internal covariance matrix of the extended Kalman filter en_US
dc.type Conference Presentation en_US
dc.identifier.apacitation 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 en_ZA
dc.identifier.chicagocitation 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 en_ZA
dc.identifier.vancouvercitation 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 . en_ZA
dc.identifier.ris TY - Conference Presentation AU - Salmon, BP AU - Kleynhans, W AU - Van den Bergh, F AU - Olivier, JC AU - Wessels, Konrad J AB - 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%. DA - 2012-07 DB - ResearchSpace DP - CSIR KW - Change detection algorithms KW - Covariance matrix KW - Kalman Filter KW - Spatial information KW - Time series analysis LK - https://researchspace.csir.co.za PY - 2012 T1 - Detecting land cover change by evaluating the internal covariance matrix of the extended Kalman filter TI - Detecting land cover change by evaluating the internal covariance matrix of the extended Kalman filter UR - http://hdl.handle.net/10204/6569 ER - en_ZA


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