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A Preferential Interval-Valued Fuzzy C-Means Algorithm for Remotely Sensed Imagery Classification

A Preferential Interval-Valued Fuzzy C-Means Algorithm for Remotely Sensed Imagery Classification Remotely sensed imagery classification have a large amount of uncertainty related to the intraclass heterogeneity and the interclass ambiguity of objects. Fuzzy set theory can address the uncertainty effectively, while interval-valued model can improve the separability of samples. Therefore, we propose a novel interval-valued fuzzy c-means algorithm, which integrates the interval-valued model and preferential adaptive method. It preferentially adjusts the interval width according to MSE (mean-square-error) and boundary factor for determining the optimal set of features for the data. In this paper, it is proved that the method can make the intraclass MSE and boundary factor always proportional to the separability of objects, so that it can dynamically adjust the interval-valued separability by controlling the interval width. Experimental data consisting of SPOT5 (10-m spatial resolution) satellite data for three case study areas in China are used to test this algorithm. Compared with other state-of-the-art fuzzy classification methods, our algorithm demonstrates the markedly improved overall accuracy and Kappa coefficients. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Fuzzy Systems Springer Journals

A Preferential Interval-Valued Fuzzy C-Means Algorithm for Remotely Sensed Imagery Classification

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References (30)

Publisher
Springer Journals
Copyright
Copyright © 2019 by Taiwan Fuzzy Systems Association
Subject
Engineering; Computational Intelligence; Artificial Intelligence; Operations Research, Management Science
ISSN
1562-2479
eISSN
2199-3211
DOI
10.1007/s40815-019-00706-x
Publisher site
See Article on Publisher Site

Abstract

Remotely sensed imagery classification have a large amount of uncertainty related to the intraclass heterogeneity and the interclass ambiguity of objects. Fuzzy set theory can address the uncertainty effectively, while interval-valued model can improve the separability of samples. Therefore, we propose a novel interval-valued fuzzy c-means algorithm, which integrates the interval-valued model and preferential adaptive method. It preferentially adjusts the interval width according to MSE (mean-square-error) and boundary factor for determining the optimal set of features for the data. In this paper, it is proved that the method can make the intraclass MSE and boundary factor always proportional to the separability of objects, so that it can dynamically adjust the interval-valued separability by controlling the interval width. Experimental data consisting of SPOT5 (10-m spatial resolution) satellite data for three case study areas in China are used to test this algorithm. Compared with other state-of-the-art fuzzy classification methods, our algorithm demonstrates the markedly improved overall accuracy and Kappa coefficients.

Journal

International Journal of Fuzzy SystemsSpringer Journals

Published: Jul 26, 2019

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