Title
Multispectral images segmentation using fuzzy probabilistic local cluster for unsupervised clustering
Date Issued
2018
Access level
restricted access
Resource Type
conference paper
Author(s)
Mantilla S.C.L.
Publisher(s)
Institute of Electrical and Electronics Engineers Inc.
Abstract
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. © 2017 IEEE.
Start page
1
End page
5
Volume
2017-November
Number
1
Language
English
Subjects
Scopus EID
2-s2.0-85050374001
Source
2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2017 - Proceedings
ISBN of the container
9781538637340
Conference
2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2017
Sponsor(s)
The authors would like to CONCYTEC (Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica), FONDECYT (Fondo Nacional de Desarrollo Científico y Tecnológico) and ANA (Autoridad Nacional del Agua) for satellite images and supporting this project.
Sources of information:
Directorio de Producción Científica