Remotely Sensed Image Classification by Complex Network Eigenvalue and Connected Degree
Joint Authors
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2012, Issue 2012 (31 Dec. 2012), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2012-01-02
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
It is a well-known problem of remotely sensed images classification due to its complexity.
This paper proposes a remotely sensed image classification method based on weighted complex network clustering using the traditional K-means clustering algorithm.
First, the degree of complex network and clustering coefficient of weighted feature are used to extract the features of the remote sensing image.
Then, the integrated features of remote sensing image are combined to be used as the basis of classification.
Finally, K-means algorithm is used to classify the remotely sensed images.
The advantage of the proposed classification method lies in obtaining better clustering centers.
The experimental results show that the proposed method gives an increase of 8% in accuracy compared with the traditional K-means algorithm and the Iterative Self-Organizing Data Analysis Technique (ISODATA) algorithm.
American Psychological Association (APA)
Xu, Mengxi& Wei, Chenglin. 2012. Remotely Sensed Image Classification by Complex Network Eigenvalue and Connected Degree. Computational and Mathematical Methods in Medicine،Vol. 2012, no. 2012, pp.1-9.
https://search.emarefa.net/detail/BIM-486777
Modern Language Association (MLA)
Xu, Mengxi& Wei, Chenglin. Remotely Sensed Image Classification by Complex Network Eigenvalue and Connected Degree. Computational and Mathematical Methods in Medicine No. 2012 (2012), pp.1-9.
https://search.emarefa.net/detail/BIM-486777
American Medical Association (AMA)
Xu, Mengxi& Wei, Chenglin. Remotely Sensed Image Classification by Complex Network Eigenvalue and Connected Degree. Computational and Mathematical Methods in Medicine. 2012. Vol. 2012, no. 2012, pp.1-9.
https://search.emarefa.net/detail/BIM-486777
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-486777