Title: Increasing Robustness of Postclassification Change Detection Using Time Series of Land Cover Maps
Authors: KEMPENEERS PieterSEDANO FernandoSTROBL PeterMC INERNEY DANIELSAN-MIGUEL-AYANZ Jesus
Citation: IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING vol. 50 no. 9 p. 3327-3339
Publisher: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Year: 2012
JRC N°: JRC70129
ISSN: 0196-2892
URI: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6155738
http://publications.jrc.ec.europa.eu/repository/handle/JRC70129
DOI: 10.1109/TGRS.2011.2181854
Type: Articles in periodicals and books
Abstract: The monitoring of land cover requires that stable land cover classes be distinguished from changes over time. Within this paper, a postclassification method is presented that provides land cover change information, based on a time series of land cover maps. The method applies a kernel filter to sequential land cover maps. Under some basic assumptions, it shows robustness against classification errors. Despite seasonality, land cover changes often occur at a low temporal frequency (e.g., maximum once every 5–10 years). If land cover maps are available more frequently, some of the information will become redundant (oversampling). The proposed method uses this redundancy for tolerating (nonsystematic) misclassifications. In order to demonstrate the benefits and limitations of the proposed method, analytical expressions have been derived. When compared to a simple postclassification comparison, one of the key strengths of the proposed approach is that it is able to improve both the overall and user's accuracy of change, while also maintaining the same level of producer's accuracy. As a case study, MODerate Resolution Imaging Spectroradiometer remote sensing data from 2006-2010 were classified into forest (F)/nonforest (NF) at pan-European scale. Promising results were obtained for detecting forest loss due to natural disasters. Quality was assessed using burnt area maps in southern Europe and a forest damage report after a windstorm in France. Results indicated a considerable reduction of change detection errors, confirming the theoretical results.
JRC Directorate:Sustainable Resources

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