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Land cover change detection using the internal covariance matrix of the extended kalman filter over multiple spectral bands

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dc.contributor.author Salmon, Brian P
dc.contributor.author Kleynhans, Waldo
dc.contributor.author Van den Bergh, Frans
dc.contributor.author Olivier, JC
dc.contributor.author Grobler, TL
dc.contributor.author Wessels, Konrad J
dc.date.accessioned 2017-07-28T09:11:03Z
dc.date.available 2017-07-28T09:11:03Z
dc.date.issued 2013-06
dc.identifier.citation Salmon, B.P., Kleynhans, W., Van den Bergh, F. et al. 2013. Land cover change detection using the internal covariance matrix of the extended kalman filter over multiple spectral bands. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 6(3): 1078-1085. DOI: 10.1109/JSTARS.2013.2241023 en_US
dc.identifier.issn 1939-1404
dc.identifier.uri DOI: 10.1109/JSTARS.2013.2241023
dc.identifier.uri http://ieeexplore.ieee.org/document/6450128/
dc.identifier.uri http://hdl.handle.net/10204/9385
dc.description Copyright: 2013 IEEE. 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 In this paper, the internal operations of an Extended Kalman Filter is investigated to observe if information can be derived to detect land cover change in a MODerate-resolution Imaging Spectroradiometer (MODIS) time series. The concept is based on the internal covariance matrix used by the Extended Kalman Filter, which adjusts the internal state of the filter for any changes occurring in the time series. The Extended Kalman Filter expands the internal covariance matrix if a significant change in reflectance value is observed, followed by adapting the state parameters to compensate for this change. The analysis shows that a change detection accuracy above 90% can be attained when evaluating the elements within the internal covariance matrix to detect new human ssettlements, with a corresponding false alarm rate below 6%. en_US
dc.language.iso en en_US
dc.publisher IEEE en_US
dc.relation.ispartofseries Worklist;10577
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 Land cover change detection using the internal covariance matrix of the extended kalman filter over multiple spectral bands en_US
dc.type Article en_US
dc.identifier.apacitation Salmon, B. P., Kleynhans, W., Van den Bergh, F., Olivier, J., Grobler, T., & Wessels, K. J. (2013). Land cover change detection using the internal covariance matrix of the extended kalman filter over multiple spectral bands. http://hdl.handle.net/10204/9385 en_ZA
dc.identifier.chicagocitation Salmon, Brian P, Waldo Kleynhans, Frans Van den Bergh, JC Olivier, TL Grobler, and Konrad J Wessels "Land cover change detection using the internal covariance matrix of the extended kalman filter over multiple spectral bands." (2013) http://hdl.handle.net/10204/9385 en_ZA
dc.identifier.vancouvercitation Salmon BP, Kleynhans W, Van den Bergh F, Olivier J, Grobler T, Wessels KJ. Land cover change detection using the internal covariance matrix of the extended kalman filter over multiple spectral bands. 2013; http://hdl.handle.net/10204/9385. en_ZA
dc.identifier.ris TY - Article AU - Salmon, Brian P AU - Kleynhans, Waldo AU - Van den Bergh, Frans AU - Olivier, JC AU - Grobler, TL AU - Wessels, Konrad J AB - In this paper, the internal operations of an Extended Kalman Filter is investigated to observe if information can be derived to detect land cover change in a MODerate-resolution Imaging Spectroradiometer (MODIS) time series. The concept is based on the internal covariance matrix used by the Extended Kalman Filter, which adjusts the internal state of the filter for any changes occurring in the time series. The Extended Kalman Filter expands the internal covariance matrix if a significant change in reflectance value is observed, followed by adapting the state parameters to compensate for this change. The analysis shows that a change detection accuracy above 90% can be attained when evaluating the elements within the internal covariance matrix to detect new human ssettlements, with a corresponding false alarm rate below 6%. DA - 2013-06 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 - 2013 SM - 1939-1404 T1 - Land cover change detection using the internal covariance matrix of the extended kalman filter over multiple spectral bands TI - Land cover change detection using the internal covariance matrix of the extended kalman filter over multiple spectral bands UR - http://hdl.handle.net/10204/9385 ER - en_ZA


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