TY - JOUR
T1 - Using FCMC, FVS, and PCA techniques for feature extraction of multispectral images
AU - Sun, Zhan Li
AU - Huang, De Shuang
AU - CHEUNG, Yiu Ming
AU - LIU, Jiming
AU - Huang, Guang Bin
N1 - Funding Information:
Manuscript received July 8, 2004; revised December 22, 2004. This work was supported by the National Science Foundation of China under Grants 60472111 and 60405002. Z.-L. Sun is with the Intelligent Computing Laboratory, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, Anhui 230031, China, and is also with the Department of Automatization, University of Science and Technology of China, Anhui 230026, China (e-mail: [email protected]). D.-S. Huang is with the Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, Anhui 230031, China (e-mail: [email protected]). Y.-M. Cheung and J. Liu are with the Department of Computer Science, Hong Kong Baptist University, Hong Kong. G.-B. Huang is with the School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore. Digital Object Identifier 10.1109/LGRS.2005.844169
PY - 2005/4
Y1 - 2005/4
N2 - In this letter, a new nonlinear approach based on a combination of the fuzzy c-means clustering (FCMC), feature vector selection and principal component analysis (PCA) is proposed to extract features of multispectral images when a very large number of samples need to be processed. The main contribution of this letter is to provide a preprocessing method for classifying these images with higher accuracy compared to the single PCA and kernel PCA. Finally, some experimental results demonstrate that our proposed approach is effective and efficient in analyzing multispectral images.
AB - In this letter, a new nonlinear approach based on a combination of the fuzzy c-means clustering (FCMC), feature vector selection and principal component analysis (PCA) is proposed to extract features of multispectral images when a very large number of samples need to be processed. The main contribution of this letter is to provide a preprocessing method for classifying these images with higher accuracy compared to the single PCA and kernel PCA. Finally, some experimental results demonstrate that our proposed approach is effective and efficient in analyzing multispectral images.
KW - Feature extraction
KW - Feature vector selection (FVS)
KW - Fuzzy c-means clustering (FCMC)
KW - Multispectral image
KW - Principal component analysis (PCA)
UR - http://www.scopus.com/inward/record.url?scp=18844429785&partnerID=8YFLogxK
U2 - 10.1109/LGRS.2005.844169
DO - 10.1109/LGRS.2005.844169
M3 - Journal article
AN - SCOPUS:18844429785
SN - 1545-598X
VL - 2
SP - 108
EP - 112
JO - IEEE Geoscience and Remote Sensing Letters
JF - IEEE Geoscience and Remote Sensing Letters
IS - 2
ER -