dc.contributor.author |
Salmon, BP
|
|
dc.contributor.author |
Olivier, JC
|
|
dc.contributor.author |
Wessels, Konrad J
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|
dc.contributor.author |
Kleynhans, W
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|
dc.contributor.author |
Van den Bergh, F
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|
dc.contributor.author |
Steenkamp, Karen C
|
|
dc.date.accessioned |
2011-12-13T12:50:59Z |
|
dc.date.available |
2011-12-13T12:50:59Z |
|
dc.date.issued |
2011-06 |
|
dc.identifier.citation |
Salmon, B.P., Olivier, J.C., Wessels, K.J. 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 |
en_US |
dc.identifier.issn |
1939-1404 |
|
dc.identifier.uri |
http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?reload=true&arnumber=5535230
|
|
dc.identifier.uri |
http://hdl.handle.net/10204/5400
|
|
dc.description |
Copyright: 2011 IEEE. This is an ABSTRACT ONLY |
en_US |
dc.description.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. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
IEEE |
en_US |
dc.relation.ispartofseries |
Workflow request;5978 |
|
dc.subject |
Change detection |
en_US |
dc.subject |
Clustering |
en_US |
dc.subject |
Satellite |
en_US |
dc.subject |
Time series |
en_US |
dc.subject |
Land cover |
en_US |
dc.subject |
Earth observations |
en_US |
dc.subject |
Remote sensing |
en_US |
dc.title |
Unsupervised land cover change detection: meaningful sequential time series analysis |
en_US |
dc.type |
Article |
en_US |
dc.identifier.apacitation |
Salmon, B., Olivier, J., Wessels, K. J., Kleynhans, W., Van den Bergh, F., & Steenkamp, K. C. (2011). Unsupervised land cover change detection: meaningful sequential time series analysis. http://hdl.handle.net/10204/5400 |
en_ZA |
dc.identifier.chicagocitation |
Salmon, BP, JC Olivier, Konrad J Wessels, W Kleynhans, F Van den Bergh, and Karen C Steenkamp "Unsupervised land cover change detection: meaningful sequential time series analysis." (2011) http://hdl.handle.net/10204/5400 |
en_ZA |
dc.identifier.vancouvercitation |
Salmon B, Olivier J, Wessels KJ, Kleynhans W, Van den Bergh F, Steenkamp KC. Unsupervised land cover change detection: meaningful sequential time series analysis. 2011; http://hdl.handle.net/10204/5400. |
en_ZA |
dc.identifier.ris |
TY - Article
AU - Salmon, BP
AU - Olivier, JC
AU - Wessels, Konrad J
AU - Kleynhans, W
AU - Van den Bergh, F
AU - Steenkamp, Karen C
AB - 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.
DA - 2011-06
DB - ResearchSpace
DP - CSIR
KW - Change detection
KW - Clustering
KW - Satellite
KW - Time series
KW - Land cover
KW - Earth observations
KW - Remote sensing
LK - https://researchspace.csir.co.za
PY - 2011
SM - 1939-1404
T1 - Unsupervised land cover change detection: meaningful sequential time series analysis
TI - Unsupervised land cover change detection: meaningful sequential time series analysis
UR - http://hdl.handle.net/10204/5400
ER -
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en_ZA |