TY - GEN
T1 - A novel hyperspectral classification method based on C5.0 decision tree of multiple combined classifiers
AU - Wang, Meng
AU - Gao, Kun
AU - Wang, Li Jing
AU - Miu, Xiang Hu
PY - 2012
Y1 - 2012
N2 - It is difficult for a single classifier to resolve the problem of high dimension in the hyperspectral image classification applications. Combination of multiple classifiers can make full use of the complementary of the existing classifiers, thus owns better classification performance. A novel multiple classifiers based on C5.0 decision tree is proposed. It reduces the hyperspectral dimension through wavelet-PCA transform algorithm firstly. Then three supervised classifiers, namely Minimum Distance, Maximum Likelihood and SVM, combined by C5.0 decision tree, are used to realize hyperspectral classification. Experiments based on AVIRIS hyperspectral image data show that higher classification accuracy may be achieved via the multiple combined classifiers than a single sub-classifier. The proposed method can reduce the dimension of features and improve the classification performance efficiently.
AB - It is difficult for a single classifier to resolve the problem of high dimension in the hyperspectral image classification applications. Combination of multiple classifiers can make full use of the complementary of the existing classifiers, thus owns better classification performance. A novel multiple classifiers based on C5.0 decision tree is proposed. It reduces the hyperspectral dimension through wavelet-PCA transform algorithm firstly. Then three supervised classifiers, namely Minimum Distance, Maximum Likelihood and SVM, combined by C5.0 decision tree, are used to realize hyperspectral classification. Experiments based on AVIRIS hyperspectral image data show that higher classification accuracy may be achieved via the multiple combined classifiers than a single sub-classifier. The proposed method can reduce the dimension of features and improve the classification performance efficiently.
KW - C5.0 decision tree
KW - classification accuracy
KW - multiple classifiers
UR - http://www.scopus.com/inward/record.url?scp=84868528611&partnerID=8YFLogxK
U2 - 10.1109/ICCIS.2012.33
DO - 10.1109/ICCIS.2012.33
M3 - Conference contribution
AN - SCOPUS:84868528611
SN - 9780769547893
T3 - Proceedings - 4th International Conference on Computational and Information Sciences, ICCIS 2012
SP - 373
EP - 376
BT - Proceedings - 4th International Conference on Computational and Information Sciences, ICCIS 2012
T2 - 4th International Conference on Computational and Information Sciences, ICCIS 2012
Y2 - 17 August 2012 through 19 August 2012
ER -