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.
Reference:
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
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
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
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 .
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