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Proceedings Paper

A new method of remote sensing image classification based on FSVM
Author(s): Huajie Cai; Yihua Tan; Chao Tao; Jinwen Tian
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Paper Abstract

Since SVM is very sensitive to outliers and noises in the training set and the fuzzy feature exists in remote sensing images, we hereby studied fuzzy support vector machine based on the affinity among samples. The fuzzy membership is defined by not only the relation between a sample and its cluster center, but also the affinity among samples. A method defining the affinity among samples is proposed using a sphere with minimum volume while containing maximum of the samples. Then, the fuzzy membership is defined according to the position of samples in sphere space, which distinguished between the valid samples and the outliers or noises. The experiment results show, it discriminates support vectors with noise or outliers much better. Experimental results show that our method performs better than SVM in classification of the images in Wuhan and with less influnence by the noise interference.

Paper Details

Date Published: 30 October 2009
PDF: 8 pages
Proc. SPIE 7496, MIPPR 2009: Pattern Recognition and Computer Vision, 749617 (30 October 2009); doi: 10.1117/12.830049
Show Author Affiliations
Huajie Cai, Huazhong Univ. of Science and Technology (China)
Yihua Tan, Huazhong Univ. of Science and Technology (China)
Chao Tao, Huazhong Univ. of Science and Technology (China)
Jinwen Tian, Huazhong Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 7496:
MIPPR 2009: Pattern Recognition and Computer Vision
Mingyue Ding; Bir Bhanu; Friedrich M. Wahl; Jonathan Roberts, Editor(s)

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