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Proceedings Paper

Uncertainty research of remote sensing image classification based on hybrid entropy evaluation model
Author(s): Zeying Lan; Yanfang Liu; Xiangyun Tang; Gang Liu
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Paper Abstract

This study put forward an integrated evaluation model. Bases on a framework of fuzzy set theory and entropy theory, we firstly complete the classification using fuzzy surveillance approach, taking it as a formalized description of classification uncertainty. Then introduce hybrid entropy model for classification uncertainty evaluation, which can meet the requirement of comprehensive reflection of both random and fuzzy uncertainty, meanwhile construct evaluation index from pixel scale with the full consideration of different contribution to error rate of each pixel. Finally, we use such method to evaluate land-use classification result of remote sensing image, which is in Huangshi city, Hubei province of China, by using hybrid entropy evaluation model, the classification quality can be fully reflected, and pixelscale evaluation indexes were easier constructed.

Paper Details

Date Published: 10 November 2008
PDF: 9 pages
Proc. SPIE 7146, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Advanced Spatial Data Models and Analyses, 714616 (10 November 2008); doi: 10.1117/12.813133
Show Author Affiliations
Zeying Lan, Wuhan Univ. (China)
Key Lab. of Geographic Information Systems, Ministry of Education (China)
Yanfang Liu, Wuhan Univ. (China)
Key Lab. of Geographic Information Systems, Ministry of Education (China)
Xiangyun Tang, Wuhan Univ. (China)
Key Lab. of Geographic Information Systems, Ministry of Education (China)
Gang Liu, Wuhan Univ. (China)


Published in SPIE Proceedings Vol. 7146:
Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Advanced Spatial Data Models and Analyses
Lin Liu; Xia Li; Kai Liu; Xinchang Zhang, Editor(s)

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