Using FCMC, FVS, and PCA techniques for feature extraction of multispectral images

Zhan Li Sun*, De Shuang Huang, Yiu Ming CHEUNG, Jiming LIU, Guang Bin Huang

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

58 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)108-112
Number of pages5
JournalIEEE Geoscience and Remote Sensing Letters
Issue number2
Publication statusPublished - Apr 2005

Scopus Subject Areas

  • Geotechnical Engineering and Engineering Geology
  • Electrical and Electronic Engineering

User-Defined Keywords

  • Feature extraction
  • Feature vector selection (FVS)
  • Fuzzy c-means clustering (FCMC)
  • Multispectral image
  • Principal component analysis (PCA)


Dive into the research topics of 'Using FCMC, FVS, and PCA techniques for feature extraction of multispectral images'. Together they form a unique fingerprint.

Cite this