Meta-optimization of the Extended Kalman filter's parameters through the use of the Bias-Variance Equilibrium Point criterion

dc.contributor.authorSalmon, Brian Paxton
dc.contributor.authorKleynhans, Waldo
dc.contributor.authorVan den Bergh, Frans
dc.contributor.authorOlivier, Jan Corne
dc.contributor.authorMarais, W.J.
dc.contributor.authorGrobler, Trienko Lups
dc.contributor.authorWessels, K.J. (Konrad)
dc.date.accessioned2014-06-04T08:37:57Z
dc.date.available2014-06-04T08:37:57Z
dc.date.issued2014-08
dc.description.abstractThe extraction of information on land cover classes using unsupervised methods has always been of relevance to the remote sensing community. In this paper, a novel criterion is proposed, which extracts the inherent information in an unsupervised fashion from a time series. The criterion is used to fit a parametric model to a time series, derive the corresponding covariance matrices of the parameters for the model, and estimate the additive noise on the time series. The proposed criterion uses both spatial and temporal information when estimating the covariance matrices and can be extended to incorporate spectral information. The algorithm used to estimate the parameters for the model is the extended Kalman filter (EKF). An unsupervised search algorithm, specifically designed for this criterion, is proposed in conjunction with the criterion that is used to rapidly and efficiently estimate the variables. The search algorithm attempts to satisfy the criterion by employing density adaptation to the current candidate system. The application in this paper is the use of an EKF to model Moderate Resolution Imaging Spectroradiometer time series with a triply modulated cosine function as the underlying model. The results show that the criterion improved the fit of the triply modulated cosine function by an order of magnitude on the time series over all seven spectral bands when compared with the other methods. The state space variables derived from the EKF are then used for both land cover classification and land cover change detection. The method was evaluated in the Gauteng province of South Africa where it was found to significantly improve on land cover classification and change detection accuracies when compared with other methods.en_US
dc.description.librarianhb2014en_US
dc.description.urihttp://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=36en_US
dc.identifier.citationSalmon, BP, Kleynhans, W, Van Den Bergh, F, Olivier, JC, Marais, WJ, Grobler, TL & Wessels, KJ 2014, 'Meta-optimization of the Extended Kalman filter's parameters through the use of the Bias-Variance Equilibrium Point criterion', IEEE Transactions on Geoscience and Remote Sensing, vol. 52, no. 8, art. 6670111, pp. 5072-5087.en_US
dc.identifier.issn0196-2892 (print)
dc.identifier.issn1558-0644 (online)
dc.identifier.other10.1109/TGRS.2013.2286821
dc.identifier.urihttp://hdl.handle.net/2263/39976
dc.language.isoenen_US
dc.publisherIEEE / Institute of Electrical and Electronics Engineers Incorporateden_US
dc.rights© 2014 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.en_US
dc.subjectClassification algorithms and geospatial analysisen_US
dc.subjectKalman filtersen_US
dc.subjectTime series analysisen_US
dc.subjectUnsupervised learningen_US
dc.subjectBias-variance equilibrium point (BVEP)en_US
dc.subjectExtended Kalman filter (EKF)en_US
dc.titleMeta-optimization of the Extended Kalman filter's parameters through the use of the Bias-Variance Equilibrium Point criterionen_US
dc.typePostprint Articleen_US

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