Land Cover Classification Information Decision Making Fusion Based on Dempster-Shafer Theory: Results and Uncertainty

Youhua Ran 1, Xin Li 1, Ling Lu 1 and Zhigang Bai 2
 1 Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 730000, P.R. China
2 Beijing Soil and Water Conservation and Ecological Engineering LTD CO., Beijing, 100055, P.R. China

Abstract. Land cover plays a significant role in the earth system science, which reflects the influence of human activities and environmental changes (Sellers et al., 1997; IGBP, 1990; Aspinall et al, 2004). In China, Many land use/cover maps can be used in recent years derived from remote sensing observation. These data will be whether or how combined effectively to produce better land cover map that is a key question. Dempster-Shafer evidential reasoning is a method of multisource data decision fusion. The method is based on the recognition that the knowledge and information we use in making decisions such as image classification is often uncertain, incomplete, and occasionally imprecise. Past research has shown that evidential reasoning can produce more accurate results compared to traditional classifiers. This paper makes an experiment in HEIHE river basin to develop land cover map using Dempster-Shafer (DS) evidence theory. The China 1:100000 land use data, the 1: 1000000 vegetation map and MODIS land cover classification product as multisource of evidence to support each land cover class. These evidences are combined using DS combination rule. Results shows that the evidence theory can be used for fusing multi classification information and can effective report the spatial distribution of interval of uncertainty. The most important issues is how to accurately determine and expression the uncertainties in the make-decision process, such as the uncertainty of input data, the uncertainty of evidence and the uncertainty of frame of discernment.

Keywords: land cover, data fusion, Dempster-Shafer theory, uncertainty

In: Wan, Y. et al. (eds) Proceeding of the 8th international symposium on spatial accuracy assessment in natural resources and environmental sciences, World Academic Union (Press).

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