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Proceedings Paper

Multi-source remote sensing image fusion classification based on DS evidence theory
Author(s): Chunping Liu; Xiaohu Ma; Zhiming Cui
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Paper Abstract

A new adaptive remote sensing image fusion classification based on the Dempster-Shafer theory of evidence is presented. This method uses a limited number of prototypes as items of evidence, which is automatically generated by modified Fuzzy Kohonen Clustering Network (FKCN). The class fuzzy membership of each prototype is also determined using reference pattern set. For each input vector a basic probability assignment (BPA) function are computed based on these distances and on the degree of membership of prototypes to each class. And lastly this evidence is combined using Dempster's rule. This proposed method can be implemented in a modified FKCN with specific architecture consisting of one input layer, a prototype layer, a BPA layer, a combination and output layer, and decision layer. The experimental results show that the excellent performance of classification as compared to existing FKCN and basic DS fusion techniques.

Paper Details

Date Published: 14 November 2007
PDF: 8 pages
Proc. SPIE 6790, MIPPR 2007: Remote Sensing and GIS Data Processing and Applications; and Innovative Multispectral Technology and Applications, 67903C (14 November 2007); doi: 10.1117/12.751283
Show Author Affiliations
Chunping Liu, Soochow Univ. (China)
Xiaohu Ma, Soochow Univ. (China)
Zhiming Cui, Soochow Univ. (China)


Published in SPIE Proceedings Vol. 6790:
MIPPR 2007: Remote Sensing and GIS Data Processing and Applications; and Innovative Multispectral Technology and Applications

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