Abstract
Classification is a very important task for scene interpretation and other applications of multispectral images. Feature extraction is a key step for classification. By extracting more nonlinear features than corresponding number of linear features in original feature space, classification accuracy for multispectral images can be improved greatly. Therefore, in this paper, an approach based on the fuzzy c-means clustering (FCMC) and kernel principal component analysis (KPCA) is proposed to resolve the problem of multispectral images. The main contribution of this paper is to provide a good preprocessed method for classifying these images. Finally, some experimental results demonstrate that our proposed method is effective and efficient for analyzing the multispectral images.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.