SPATIAL INFORMATION BASED SUPPORT VECTOR MACHINE FOR HYPERSPECTRAL IMAGE CLASSIFICATION

被引：32

作者：

Kuo, Bor-Chen ^{[1
]}

Huang, Chih-Sheng ^{[1
]}

Hung, Chih-Cheng ^{[2
]}

Liu, Yu-Lung ^{[3
]}

Chen, I-Ling ^{[1
]}

机构：

[1] Natl Taichung Univ, Grad Inst Educ Measurement & Stat, Taichung, Taiwan

[2] Southern Polytech State Univ, Sch Comp & Software Engn, Marietta, GA 30060 USA

[3] Asian Univ, Dept Comp Sci & Informat Engn, Taichung, Taiwan

来源：

2010 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM | 2010年

关键词：

spatial information; hyperspectral image classification; support vector machine; spatial-contextual semi-supervised support vector machine;

D O I：

10.1109/IGARSS.2010.5651433

中图分类号：

P [天文学、地球科学];

学科分类号：

07 ;

摘要：

In this study, a novel spatial information based support vector machine for hyperspectral image classification, named spatial-contextual semi-supervised support vector machine ((SCSVM)-S-3), is proposed. This approach modifies the SVM algorithm by using the spectral information and spatial-contextual information. The concept of SC3SVM is to utilize other information, obtain from the pixels of a neighborhood system in the spatial domain, to modify the effective of each patterns. Experimental results show a sound performance of classification on the famous hyperspectral images, Indian Pine site. Especially, the overall classification accuracy of whole hyperspectral image (Indian Pine site with 16 classes) is up to 96.4%, the kappa accuracy is up to 95.9%.

引用

页码：832 / 835

页数：4

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