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A modified temporal criterion to meta-optimize the extended Kalman filter for land cover classification of remotely sensed time series

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dc.contributor.author Salmon, BP
dc.contributor.author Kleynhans, Waldo
dc.contributor.author Olivier, JC
dc.contributor.author Van den Bergh, Frans
dc.contributor.author Wessels, Konrad J
dc.date.accessioned 2018-08-24T08:04:31Z
dc.date.available 2018-08-24T08:04:31Z
dc.date.issued 2018-05
dc.identifier.citation Salmon, BP, Kleynhans, W, Olivier, JC. Van den Bergh, F and Wessels, KJ. 2018. A modified temporal criterion to meta-optimize the extended Kalman filter for land cover classification of remotely sensed time series. International Journal of Applied Earth Observation and Geoinformation, v. 67, pp 20-29. en_US
dc.identifier.issn 0303-2434
dc.identifier.uri https://www.sciencedirect.com/science/article/pii/S0303243417302969
dc.identifier.uri https://doi.org/10.1016/j.jag.2017.12.007
dc.identifier.uri http://hdl.handle.net/10204/10388
dc.description Copyright: 2018 Elsevier. Due to copyright restrictions, the attached PDF file only contains the abstract of the full text item. For access to the full text item, kindly consult the publisher's website. en_US
dc.description.abstract Humans are transforming land cover at an ever-increasing rate. Accurate geographical maps on land cover, especially rural and urban settlements are essential to planning sustainable development. Time series extracted from MODerate resolution Imaging Spectroradiometer (MODIS) land surface reflectance products have been used to differentiate land cover classes by analyzing the seasonal patterns in reflectance values. The proper fitting of a parametric model to these time series usually requires several adjustments to the regression method. To reduce the workload, a global setting of parameters is done to the regression method for a geographical area. In this work we have modified a meta-optimization approach to setting a regression method to extract the parameters on a per time series basis. The standard deviation of the model parameters and magnitude of residuals are used as scoring function. We successfully fitted a triply modulated model to the seasonal patterns of our study area using a non-linear extended Kalman filter (EKF). The approach uses temporal information which significantly reduces the processing time and storage requirements to process each time series. It also derives reliability metrics for each time series individually. The features extracted using the proposed method are classified with a support vector machine and the performance of the method is compared to the original approach on our ground truth data. en_US
dc.language.iso en en_US
dc.publisher Elsevier en_US
dc.relation.ispartofseries Worklist;20317
dc.subject Kalman filtering en_US
dc.subject Remote sensing en_US
dc.subject Satellites en_US
dc.subject Time series en_US
dc.title A modified temporal criterion to meta-optimize the extended Kalman filter for land cover classification of remotely sensed time series en_US
dc.type Article en_US
dc.identifier.apacitation Salmon, B., Kleynhans, W., Olivier, J., Van den Bergh, F., & Wessels, K. J. (2018). A modified temporal criterion to meta-optimize the extended Kalman filter for land cover classification of remotely sensed time series. http://hdl.handle.net/10204/10388 en_ZA
dc.identifier.chicagocitation Salmon, BP, Waldo Kleynhans, JC Olivier, Frans Van den Bergh, and Konrad J Wessels "A modified temporal criterion to meta-optimize the extended Kalman filter for land cover classification of remotely sensed time series." (2018) http://hdl.handle.net/10204/10388 en_ZA
dc.identifier.vancouvercitation Salmon B, Kleynhans W, Olivier J, Van den Bergh F, Wessels KJ. A modified temporal criterion to meta-optimize the extended Kalman filter for land cover classification of remotely sensed time series. 2018; http://hdl.handle.net/10204/10388. en_ZA
dc.identifier.ris TY - Article AU - Salmon, BP AU - Kleynhans, Waldo AU - Olivier, JC AU - Van den Bergh, Frans AU - Wessels, Konrad J AB - Humans are transforming land cover at an ever-increasing rate. Accurate geographical maps on land cover, especially rural and urban settlements are essential to planning sustainable development. Time series extracted from MODerate resolution Imaging Spectroradiometer (MODIS) land surface reflectance products have been used to differentiate land cover classes by analyzing the seasonal patterns in reflectance values. The proper fitting of a parametric model to these time series usually requires several adjustments to the regression method. To reduce the workload, a global setting of parameters is done to the regression method for a geographical area. In this work we have modified a meta-optimization approach to setting a regression method to extract the parameters on a per time series basis. The standard deviation of the model parameters and magnitude of residuals are used as scoring function. We successfully fitted a triply modulated model to the seasonal patterns of our study area using a non-linear extended Kalman filter (EKF). The approach uses temporal information which significantly reduces the processing time and storage requirements to process each time series. It also derives reliability metrics for each time series individually. The features extracted using the proposed method are classified with a support vector machine and the performance of the method is compared to the original approach on our ground truth data. DA - 2018-05 DB - ResearchSpace DP - CSIR KW - Kalman filtering KW - Remote sensing KW - Satellites KW - Time series LK - https://researchspace.csir.co.za PY - 2018 SM - 0303-2434 T1 - A modified temporal criterion to meta-optimize the extended Kalman filter for land cover classification of remotely sensed time series TI - A modified temporal criterion to meta-optimize the extended Kalman filter for land cover classification of remotely sensed time series UR - http://hdl.handle.net/10204/10388 ER - en_ZA


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