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http://dx.doi.org/10.7780/kjrs.2004.20.6.419

Dempster-Shafer Fusion of Multisensor Imagery Using Gaussian Mass Function  

Lee Sang-Hoon (Department of Industrial Engineering, Kyungwon University)
Publication Information
Korean Journal of Remote Sensing / v.20, no.6, 2004 , pp. 419-425 More about this Journal
Abstract
This study has proposed a data fusion method based on the Dempster-Shafer evidence theory The Dempster-Shafer fusion uses mass functions obtained under the assumption of class-independent Gaussian assumption. In the Dempster-Shafer approach, uncertainty is represented by 'belief interval' equal to the difference between the values of 'belief' function and 'plausibility' function which measure imprecision and uncertainty By utilizing the Dempster-Shafer scheme to fuse the data from multiple sensors, the results of classification can be improved. It can make the users consider the regions with mixed classes in a training process. In most practices, it is hard to find the regions with a pure class. In this study, the proposed method has applied to the KOMPSAT-EOC panchromatic image and LANDSAT ETM+ NDVI data acquired over Yongin/Nuengpyung. area of Kyunggi-do. The results show that it has potential of effective data fusion for multiple sensor imagery.
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