Multispectral images segmentation using fuzzy probabilistic local cluster for unsupervised clustering

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Mantilla S.C.L.
Yari Y.
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Institute of Electrical and Electronics Engineers Inc.
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In Pattern Recognition there are many algorithms it try to solve the problem of grouping objects of the same type, this is called clustering, however the task of dividing these lies not only in the objective function, but also the methodology used to calculate the similarity between objects. Because multispectral images contain information that has low statistical separation and a large amount of data it is necessary to enter local information. In this paper, the use of the Gaussian dispersion equation is proposed in order to calculate the contribution of each sample to the sample analyzed. The results show that the integration of local weights within the clustering model decreases the entropy of each group generated.
The authors would like to CONCYTEC (Consejo Nacional de Ciencia, Tecnolog ıa e Innovacion Tecnoloogica ), FONDE- ´ CYT (Fondo Nacional de Desarrollo Cient´ıfico y Tecnologico) ´and ANA (Autoridad Nacional del Agua) for satellite imagesand supporting this project
Palabras clave
Weight information, Artificial intelligence, Classification (of information), Pattern recognition, Gaussian dispersions, Multispectral images, Objective functions, Satellite images, Similarity between objects, Unsupervised classification, Unsupervised clustering, Image segmentation