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Please use this identifier to cite or link to this item: http://hdl.handle.net/10204/5838

Title: The use of a multilayer perceptron for detecting new human settlements from a time series of MODIS images
Authors: Salmon, BP
Olivier, JC
Kleynhans, W
Wessels, KJ
Van den Bergh, F
Steenkamp, KC
Keywords: Change detection
Feedforward neural networks
MODIS images
MODIS data
Issue Date: Dec-2011
Publisher: Elsevier
Citation: Salmon, BP, Olivier, JC, Kleynhans, W, Wessels, KJ, Van den Bergh, F and Steenkamp, KC. 2011. The use of a multilayer perceptron for detecting new human settlements from a time series of MODIS images. International Journal of Applied Earth Observation and Geoinformation, vol. 13(6), pp 873-883
Series/Report no.: Workflow;8094
Abstract: This paper presents a novel land cover change detection method that employs a sliding window over hyper-temporal multi-spectral images acquired from the 7 bands of the MODerate-resolution Imaging Spectroradiometer (MODIS) land surface reflectance product. The method uses a Feedforward Multilayer Perceptron (MLP) for supervised change detection that operates on multi-spectral time series extracted with a sliding window from the dataset. The method was evaluated on both real and simulated land cover change examples. The simulated land cover change comprises of concatenated time series that are produced by blending actual time series of pixels from human settlements to those from adjacent areas covered by natural vegetation. The method employs an iteratively retrained MLP to capture all local patterns and to compensate for the time-varying climate change in the geographical area. The iteratively retrained MLP was compared to a classical batch mode trained MLP. Depending on the length of the temporal sliding window used, an overall change detection accuracy between 83% and 90% was achieved. It is shown that a sliding window of 6 months using all 7 bands of MODIS data is sufficient to detect land cover change reliably. Window sizes of 18 months and longer provide minor improvements to classification accuracy and change detection performance at the cost of longer time delays.
Description: Copyright: 2011 Elsevier. This is the pre-print version of the work. The definitive version is published in International Journal of Applied Earth Observation and Geoinformation, vol. 13(6), pp 873-883
URI: http://www.sciencedirect.com/science/article/pii/S0303243411000900
http://hdl.handle.net/10204/5838
ISSN: 0303-2434
Appears in Collections:Earth observation
General science, engineering & technology
Earth observation technologies

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