A Comparative Study on Clustering Algorithms for Multispectral Remote Sensing Image Recognition
L Wen, X Chen, P Guo - International Symposium on Neural Networks, 2008 - Springer
L Wen, X Chen, P Guo
International Symposium on Neural Networks, 2008•SpringerSince 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 …
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 …
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.
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