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Please use this identifier to cite or link to this item: http://hdl.handle.net/10204/5400

Title: Unsupervised land cover change detection: meaningful sequential time series analysis
Authors: Salmon, BP
Olivier, JC
Wessels, KJ
Kleynhans, W
Van den Bergh, F
Steenkamp, KC
Keywords: Change detection
Clustering
Satellite
Time series
Land cover
Earth observations
Remote sensing
Issue Date: Jun-2011
Publisher: IEEE
Citation: Salmon, BP, Olivier, JC, Wessels, KJ et al. 2011. Unsupervised land cover change detection: meaningful sequential time series analysis. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 4(2), pp 327-335
Series/Report no.: Workflow request;5978
Abstract: An automated land cover change detection method is proposed that uses coarse spatial resolution hyper-temporal earth observation satellite time series data. The study compared three different unsupervised clustering approaches that operate on short term Fourier transform coefficients computed over subsequences of 8-day composite MODerate-resolution Imaging Spectroradiometer (MODIS) surface reflectance data that were extracted with a temporal sliding window. The method uses a feature extraction process that creates meaningful sequential time series that can be analyzed and processed for change detection. The method was evaluated on real and simulated land cover change examples and obtained a change detection accuracy exceeding 76% on real land cover conversion and more than 70% on simulated land cover conversion.
Description: Copyright: 2011 IEEE. This is an ABSTRACT ONLY
URI: http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?reload=true&arnumber=5535230
http://hdl.handle.net/10204/5400
ISSN: 1939-1404
Appears in Collections:Earth observation
General science, engineering & technology

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