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Show simple item record Salmon, BP Holloway, DS Kleynhans, Waldo Olivier, JC Wessels, Konrad J 2017-11-15T12:33:10Z 2017-11-15T12:33:10Z 2017-09
dc.identifier.citation Salmon, B.P. et al. 2017. Applying model parameters as a driving force to a deterministic nonlinear system to detect land cover change. IEEE Transactions on Geoscience and Remote Sensing, vol. PP(99): 1-12 en_US
dc.identifier.issn 0196-2892
dc.identifier.uri DOI: 10.1109/TGRS.2017.2743218
dc.description Copyright: 2017 IEEE. Due to copyright restrictions, the attached PDF file only contains the postprint version of the full text item. For access to the published item, please consult the publisher's website. en_US
dc.description.abstract In this paper, we propose a new method for extracting features from time-series satellite data to detect land cover change. We propose to make use of the behavior of a deterministic nonlinear system driven by a time-dependent force. The driving force comprises a set of concatenated model parameters regressed from fitting a model to a Moderate Resolution Imaging Spectroradiometer time series. The goal is to create behavior in the nonlinear deterministic system, which appears predictable for the time series undergoing no change, while erratic for the time series undergoing land cover change. The differential equation used for the deterministic nonlinear system is that of a large-amplitude pendulum, where the displacement angle is observed over time. If there has been no change in the land cover, the mean driving force will approximate zero, and hence the pendulum will behave as if in free motion under the influence of gravity only. If, however, there has been a change in the land cover, this will for a brief initial period introduce a nonzero mean driving force, which does work on the pendulum, changing its energy and future evolution, which we demonstrate is observable. This we show is sufficient to introduce an observable change to the state of the pendulum, thus enabling change detection. We extend this method to a higher dimensional differential equation to improve the false alarm rate in our experiments. Numerical results show a change detection accuracy of nearly 96% when detecting new human settlements, with a corresponding false alarm rate of 0.2% (omission error rate of 4%). This compares very favorably with other published methods, which achieved less than 90% detection but with false alarm rates all above 9% (omission error rate of 66%). en_US
dc.language.iso en en_US
dc.publisher IEEE en_US
dc.relation.ispartofseries Worklist;19628
dc.subject Feature extraction en_US
dc.subject Time series analysis en_US
dc.subject MODIS en_US
dc.subject Satellites en_US
dc.subject Remote sensing en_US
dc.subject Learning systems en_US
dc.subject Force en_US
dc.title Applying model parameters as a driving force to a deterministic nonlinear system to detect land cover change en_US
dc.type Article en_US

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