Impervious surface change mapping with an uncertainty-based spatial-temporal consistency model: a case study in Wuhan city using Landsat time-series datasets from 1987 to 2016Tools Shi, Lingfei, Ling, Feng, Ge, Yong, Foody, Giles M., Li, Xiaodong, Wang, Lihui, Zhang, Yihang and Du, Yun (2017) Impervious surface change mapping with an uncertainty-based spatial-temporal consistency model: a case study in Wuhan city using Landsat time-series datasets from 1987 to 2016. Remote Sensing, 9 (11). 1148/1-1148/19. ISSN 2072-4292 Full text not available from this repository.
Official URL: http://www.mdpi.com/2072-4292/9/11/1148
AbstractDetailed information on the spatial-temporal change of impervious surfaces is important for quantifying the effects of rapid urbanization. Free access of the Landsat archive provides new opportunities for impervious surface mapping with fine spatial and temporal resolution. To improve the classification accuracy, a temporal consistency (TC) model may be applied on the original classification results of Landsat time-series datasets. However, existing TC models only use class labels, and ignore the uncertainty of classification during the process. In this study, an uncertainty-based spatial-temporal consistency (USTC) model was proposed to improve the accuracy of the long time series of impervious surface classifications. In contrast to existing TC methods, the proposed USTC model integrates classification uncertainty with the spatial-temporal context information to better describe the spatial-temporal consistency for the long time-series datasets. The proposed USTC model was used to obtain an annual map of impervious surfaces in Wuhan city with Landsat Thematic Mapper (TM), Enhanced Thematic Mapper (ETM+), and Operational Land Imager (OLI) images from 1987 to 2016. The impervious surfaces mapped by the proposed USTC model were compared with those produced by the support vector machine (SVM) classifier and the TC model. The accuracy comparison of these results indicated that the proposed USTC model had the best performance in terms of classification accuracy. The increase of overall accuracy was about 4.23% compared with the SVM classifier, and about 1.79% compared with the TC model, which indicates the effectiveness of the proposed USTC model in mapping impervious surfaces from long-term Landsat sensor imagery.
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