A modified fuzzy clustering approach in unsupervised classification for detecting the mixed pixels of satellite images
Abstract
The major problem of remote sensing images is mixed pixels, available in the data which degrades the quality, accuracy of the image classification and object recognition. To overcome the problem of mixed pixel in a real satellite data a modified K-means clustering algorithm and a modified fuzzy C-means clustering algorithm, are discussed. The algorithms are developed by modifying the membership function of the standard K-means clustering algorithm (FKM) and the standard fuzzy C-means algorithm (FCM). The performance of the proposed algorithms is discussed and compared with the traditional fuzzy K-means algorithm and the traditional FCM algorithm. Results on classification and segmentation of satellite images reveal that the suggestive algorithms are robust and effective.
Copyright © 2022 A.R. Sherwani, Q.M. Ali, Irfan Ali. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Cite this article
A. R. Sherwani, Q. M. Ali, I. Ali, A modified fuzzy clustering approach in unsupervised classification for detecting the mixed pixels of satellite images, J. Math. Comput. Sci. 12 (2022), 97. https://doi.org/10.28919/jmcs/7030
BibTeX
@article{Sherwani2022,
author = {Sherwani, A.R. and Ali, Q.M. and Ali, Irfan},
title = {A modified fuzzy clustering approach in unsupervised classification for detecting the mixed pixels of satellite images},
journal = {J. Math. Comput. Sci.},
volume = {12},
year = {2022},
pages = {97},
doi = {10.28919/jmcs/7030}
}