TY - GEN
T1 - Multi-class classification for Wuhan area's TM image based on support vector machine
AU - Liu, Liu
AU - Huang, Zhengjun
AU - Tan, Xiaojun
AU - Zeng, Zhiyuan
PY - 2009
Y1 - 2009
N2 - This paper proposes a multi-class classification method based on Support Vector Machine (SVM), with an emphasis on classes of Wuhan area's water resources. First, this method builds a SVM model by selecting proper testing sample data of Wuhan area's TM image. Then, the image is classified as 5 classes based on the algorithm of SVM model. The experimental results show that this method has obvious advantages in accuracy, compared with the traditional method-Maximum likelihood, especially on classes of water resources.
AB - This paper proposes a multi-class classification method based on Support Vector Machine (SVM), with an emphasis on classes of Wuhan area's water resources. First, this method builds a SVM model by selecting proper testing sample data of Wuhan area's TM image. Then, the image is classified as 5 classes based on the algorithm of SVM model. The experimental results show that this method has obvious advantages in accuracy, compared with the traditional method-Maximum likelihood, especially on classes of water resources.
KW - Image classification
KW - Support Vector Machine (SVM)
KW - Wuhan area's TM image
UR - https://www.scopus.com/pages/publications/76549107972
U2 - 10.1109/FSKD.2009.48
DO - 10.1109/FSKD.2009.48
M3 - 会议稿件
AN - SCOPUS:76549107972
SN - 9780769537351
T3 - 6th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2009
SP - 401
EP - 404
BT - 6th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2009
T2 - 6th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2009
Y2 - 14 August 2009 through 16 August 2009
ER -