3 November 2005 Discussion of remote sensing image classification method based on evidence theory
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Proceedings Volume 6044, MIPPR 2005: Image Analysis Techniques; 60442C (2005) https://doi.org/10.1117/12.655316
Event: MIPPR 2005 SAR and Multispectral Image Processing, 2005, Wuhan, China
Remote sensing image classification is an important and complex problem. Conventional remote sensing image classification methods are mostly based on Bayes' subjective probability theory. Because there are many defects on solving uncertainty problem, new tendency is that mathematical theory of evidence is applied to remote sensing image classification. At first, this paper introduces differences between Dempster-Shafer's(D-S) evidence theory and Bayes' subjective probability theory in solving uncertainty problem, main definitions and algorithms of D-S evidence theory. Especially degree of belief, degree of plausibility and degree of support are the bridges that D-S evidence theory is used in other fields. It emphatically introduced Support function that D-S evidence theory is used on pattern recognition, and degree of support is applied to classification. We acquire degree of support surfaces according to large classes, such as urban land, farmland, forest land, and water, then use "hard classification" to gain initial classification result. If initial classification accuracy is unfitted to acquirement, do reclassification for degree of support surfaces of less than threshold until final classification result reaches satisfying accuracy. We conclude that main advantages of this method are that it can go on reclassification after classification and its classification accuracy is very high. This method has dependable theory, intensive application, easy operation and research potential.
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Wensheng Deng, Wensheng Deng, Xiaoli Shao, Xiaoli Shao, Zequn Guan, Zequn Guan, } "Discussion of remote sensing image classification method based on evidence theory", Proc. SPIE 6044, MIPPR 2005: Image Analysis Techniques, 60442C (3 November 2005); doi: 10.1117/12.655316; https://doi.org/10.1117/12.655316

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