A Comparative Study on Clustering Algorithms for Multispectral Remote Sensing Image Recognition

Authors:
Lintao Wen;Xinyu Chen;Ping Guo
Affiliations:
Image Processing and Pattern Recognition Laboratory, Beijing Normal University, Beijing, China 100875;Image Processing and Pattern Recognition Laboratory, Beijing Normal University, Beijing, China 100875;Image Processing and Pattern Recognition Laboratory, Beijing Normal University, Beijing, China 100875 and School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China ...
Venue:
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
Year:
2008

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Abstract

Since little prior knowledge about remote sensing images can be obtained before performing recognition tasks, various unsupervised classification methods have been applied to solve such problem. Therefore, choosing an appropriate clustering method is very critical to achieve good results. However, there is no standard criterion on which clustering method is more suitable or more effective. In this paper, we conduct a comparative study on three clustering methods, including C-Means, Finite Mixture Model clustering, and Affinity Propagation. The advantages and disadvantages of each method are evaluated by experiments and classification results.