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Detecting land cover change using a sliding window temporal autocorrelation approach

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dc.contributor.author Kleynhans, W
dc.contributor.author Salmon, BP
dc.contributor.author Olivier, JC
dc.contributor.author Van den Bergh, F
dc.contributor.author Wessels, Konrad J
dc.contributor.author Grobler, T
dc.date.accessioned 2013-03-19T09:54:33Z
dc.date.available 2013-03-19T09:54:33Z
dc.date.issued 2012-07
dc.identifier.citation Kleynhans, W, Salmon, BP, Olivier, JC, Van den Bergh, F, Wessels, KJ and Grobler, T. 2012. Detecting land cover change using a sliding window temporal autocorrelation approach. 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?arnumber=6352552
dc.identifier.uri http://hdl.handle.net/10204/6578
dc.description IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Munich, Germany, 22-27 July 2012. Published in IEEE Xpolre en_US
dc.description.abstract There has been recent developments in the use of hypertemporal satellite time series data for land cover change detection and classification. Recently, an Autocorrelation function (ACF) change detection method was proposed to detect the development of new human settlements in South Africa. In this paper, an extension to this change detection method is proposed that produces an estimate of the change date in addition to the change metric. Preliminary results indicate that comparable accuracy is achievable relative to the original formulation, with the added advantage of providing an estimate of the change date. en_US
dc.language.iso en en_US
dc.publisher IEEE Xplore en_US
dc.relation.ispartofseries Workflow;9569
dc.subject Satellite time series data en_US
dc.subject Land cover change detection en_US
dc.subject Change detection methods en_US
dc.title Detecting land cover change using a sliding window temporal autocorrelation approach en_US
dc.type Conference Presentation en_US
dc.identifier.apacitation Kleynhans, W., Salmon, B., Olivier, J., Van den Bergh, F., Wessels, K. J., & Grobler, T. (2012). Detecting land cover change using a sliding window temporal autocorrelation approach. IEEE Xplore. http://hdl.handle.net/10204/6578 en_ZA
dc.identifier.chicagocitation Kleynhans, W, BP Salmon, JC Olivier, F Van den Bergh, Konrad J Wessels, and T Grobler. "Detecting land cover change using a sliding window temporal autocorrelation approach." (2012): http://hdl.handle.net/10204/6578 en_ZA
dc.identifier.vancouvercitation Kleynhans W, Salmon B, Olivier J, Van den Bergh F, Wessels KJ, Grobler T, Detecting land cover change using a sliding window temporal autocorrelation approach; IEEE Xplore; 2012. http://hdl.handle.net/10204/6578 . en_ZA
dc.identifier.ris TY - Conference Presentation AU - Kleynhans, W AU - Salmon, BP AU - Olivier, JC AU - Van den Bergh, F AU - Wessels, Konrad J AU - Grobler, T AB - There has been recent developments in the use of hypertemporal satellite time series data for land cover change detection and classification. Recently, an Autocorrelation function (ACF) change detection method was proposed to detect the development of new human settlements in South Africa. In this paper, an extension to this change detection method is proposed that produces an estimate of the change date in addition to the change metric. Preliminary results indicate that comparable accuracy is achievable relative to the original formulation, with the added advantage of providing an estimate of the change date. DA - 2012-07 DB - ResearchSpace DP - CSIR KW - Satellite time series data KW - Land cover change detection KW - Change detection methods LK - https://researchspace.csir.co.za PY - 2012 T1 - Detecting land cover change using a sliding window temporal autocorrelation approach TI - Detecting land cover change using a sliding window temporal autocorrelation approach UR - http://hdl.handle.net/10204/6578 ER - en_ZA


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