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Proceedings Paper

Remote sensing image classification method based on evidence theory and decision tree
Author(s): Xuerong Li; Qianguo Xing; Lingyan Kang
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Paper Abstract

Remote sensing image classification is an important and complex problem. Conventional remote sensing image classification methods are mostly based on Bayesian subjective probability theory, but there are many defects for its uncertainty. This paper firstly introduces evidence theory and decision tree method. Then it emphatically introduces the function of support degree that evidence theory is used on pattern recognition. Combining the D-S evidence theory with the decision tree algorithm, a D-S evidence theory decision tree method is proposed, where the support degree function is the tie. The method is used to classify the classes, such as water, urban land and green land with the exclusive spectral feature parameters as input values, and produce three classification images of support degree. Then proper threshold value is chosen and according image is handled with the method of binarization. Then overlay handling is done with these images according to the type of classifications, finally the initial result is obtained. Then further accuracy assessment will be done. If initial classification accuracy is unfit for the requirement, reclassification for images with support degree of less than threshold is conducted until final classification meets the accuracy requirements. Compared to Bayesian classification, main advantages of this method are that it can perform reclassification and reach a very high accuracy. This method is finally used to classify the land use of Yantai Economic and Technological Development Zone to four classes such as urban land, green land and water, and effectively support the classification.

Paper Details

Date Published: 13 November 2010
PDF: 8 pages
Proc. SPIE 7857, Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques, and Applications III, 78570Y (13 November 2010); doi: 10.1117/12.869544
Show Author Affiliations
Xuerong Li, Graduate Univ. of the Chinese Academy of Sciences (China)
Yantai Institute of Coastal Zone Research (China)
Qianguo Xing, Yantai Institute of Coastal Zone Research (China)
Lingyan Kang, Graduate Univ. of the Chinese Academy of Sciences (China)
Yantai Institute of Coastal Zone Research (China)


Published in SPIE Proceedings Vol. 7857:
Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques, and Applications III
Allen M. Larar; Hyo-Sang Chung; Makoto Suzuki, Editor(s)

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