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Proceedings Paper

Beijing-1 small satellite multi-spectrum image classification based on neighborhood EM algorithm and its uncertainty assessment
Author(s): Binbin He
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Paper Abstract

In order to overcome the deficiencies of traditional uncertainty assessment methods of remote sensing images classification by error-matrix and kappa coefficient, classification uncertainties at pixel scale of Beijing-1 small satellite multi-spectrum remote sensing images were measured and represented. Firstly, an unsupervised classification algorithm-neighborhood EM considering spatial autocorrelation and classification fuzziness-was introduced. Then, four uncertainty assessment indexes of neighborhood EM classification-fuzzy membership residual, relative maximum fuzzy membership deviation, fuzzy membership entropy and relative fuzzy membership entropy - were constructed. Finally, the experiments concerned were performed using Beijing-1 small satellite multi-spectrum remote sensing image data in Dongkunlun, Qinghai province, China.

Paper Details

Date Published: 7 November 2008
PDF: 8 pages
Proc. SPIE 7147, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Classification of Remote Sensing Images, 71470I (7 November 2008); doi: 10.1117/12.813219
Show Author Affiliations
Binbin He, Univ. of Electronic Science and Technology of China (China)
Beijing Landview Mapping Information Technology Cooperation (China)


Published in SPIE Proceedings Vol. 7147:
Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Classification of Remote Sensing Images
Lin Liu; Xia Li; Kai Liu; Xinchang Zhang, Editor(s)

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