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Meta-optimization of the extended kalman filter's parameters for improved feature extraction on hyper-temporal images

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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 Marais, WJ
dc.contributor.author Wessels, Konrad J
dc.date.accessioned 2012-05-07T09:00:17Z
dc.date.available 2012-05-07T09:00:17Z
dc.date.issued 2011-07
dc.identifier.citation Salmon, BP, Kleynhans, W, Van den Bergh, F, Olivier, JC, Marais, WJ and Wessels, KJ. 2011. Meta-optimization of the extended kalman filter's parameters for improved feature extraction on hyper-temporal images. 2011 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Vancouver, Canada, 24-29 July 2011 en_US
dc.identifier.isbn 978-1-4577-1003-2
dc.identifier.uri http://ieeexplore.ieee.org/application/enterprise/entconfirmation.jsp?arnumber=6049730
dc.identifier.uri http://hdl.handle.net/10204/5839
dc.description 2011 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Vancouver, Canada, 24-29 July 2011 en_US
dc.description.abstract Time series derived from the first two spectral bands of the MODerate-resolution Imaging Spectroradiometer (MODIS) land surface reflectance product can be modelled as a pair of triply (mean, phase and amplitude) modulated cosine functions. This paper proposes a meta-optimization approach for setting the parameters of the non-linear Extended Kalman Filter to rapidly and efficiently estimate the features for the pair of triply modulated cosine functions. The approach is based on a unsupervised search algorithm over an appropriately defined manifold using spatial and temporal information. Performance of the new method is compared to other applicable methods and is tested on the Gauteng province which is South Africa’s province with the fastest growing economy. en_US
dc.language.iso en en_US
dc.publisher IEEE en_US
dc.relation.ispartofseries Workflow;8092
dc.subject Hellinger distance en_US
dc.subject Kalman filter en_US
dc.subject Time series analysis en_US
dc.subject Unsupervised learning en_US
dc.subject Spatial information en_US
dc.title Meta-optimization of the extended kalman filter's parameters for improved feature extraction on hyper-temporal images en_US
dc.type Conference Presentation en_US
dc.identifier.apacitation Salmon, B., Kleynhans, W., Van den Bergh, F., Olivier, J., Marais, W., & Wessels, K. J. (2011). Meta-optimization of the extended kalman filter's parameters for improved feature extraction on hyper-temporal images. IEEE. http://hdl.handle.net/10204/5839 en_ZA
dc.identifier.chicagocitation Salmon, BP, W Kleynhans, F Van den Bergh, JC Olivier, WJ Marais, and Konrad J Wessels. "Meta-optimization of the extended kalman filter's parameters for improved feature extraction on hyper-temporal images." (2011): http://hdl.handle.net/10204/5839 en_ZA
dc.identifier.vancouvercitation Salmon B, Kleynhans W, Van den Bergh F, Olivier J, Marais W, Wessels KJ, Meta-optimization of the extended kalman filter's parameters for improved feature extraction on hyper-temporal images; IEEE; 2011. http://hdl.handle.net/10204/5839 . en_ZA
dc.identifier.ris TY - Conference Presentation AU - Salmon, BP AU - Kleynhans, W AU - Van den Bergh, F AU - Olivier, JC AU - Marais, WJ AU - Wessels, Konrad J AB - Time series derived from the first two spectral bands of the MODerate-resolution Imaging Spectroradiometer (MODIS) land surface reflectance product can be modelled as a pair of triply (mean, phase and amplitude) modulated cosine functions. This paper proposes a meta-optimization approach for setting the parameters of the non-linear Extended Kalman Filter to rapidly and efficiently estimate the features for the pair of triply modulated cosine functions. The approach is based on a unsupervised search algorithm over an appropriately defined manifold using spatial and temporal information. Performance of the new method is compared to other applicable methods and is tested on the Gauteng province which is South Africa’s province with the fastest growing economy. DA - 2011-07 DB - ResearchSpace DP - CSIR KW - Hellinger distance KW - Kalman filter KW - Time series analysis KW - Unsupervised learning KW - Spatial information LK - https://researchspace.csir.co.za PY - 2011 SM - 978-1-4577-1003-2 T1 - Meta-optimization of the extended kalman filter's parameters for improved feature extraction on hyper-temporal images TI - Meta-optimization of the extended kalman filter's parameters for improved feature extraction on hyper-temporal images UR - http://hdl.handle.net/10204/5839 ER - en_ZA


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