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Computational and Mathematical Methods in Medicine
Volume 2012 (2012), Article ID 632703, 9 pages
http://dx.doi.org/10.1155/2012/632703
Research Article

Remotely Sensed Image Classification by Complex Network Eigenvalue and Connected Degree

1School of Computer Engineering, Nanjing Institute of Technology, Nanjing 211167, China
2Nanjing Rail Traffic Technology Company, Department of Electrical and Mechanical Control, NARI Technology Development Co., Ltd., Nanjing 210061, China

Received 29 July 2011; Accepted 22 September 2011

Academic Editor: Sheng-yong Chen

Copyright © 2012 Mengxi Xu and Chenglin Wei. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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.