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Reseach Article

K-Means Clustering Algorithm with Color-based Thresholding for Satellite Images

by Gurudatta V Nayak, Anuja A Rao, Nandana Prabhu
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 105 - Number 11
Year of Publication: 2014
Authors: Gurudatta V Nayak, Anuja A Rao, Nandana Prabhu
10.5120/18421-9723

Gurudatta V Nayak, Anuja A Rao, Nandana Prabhu . K-Means Clustering Algorithm with Color-based Thresholding for Satellite Images. International Journal of Computer Applications. 105, 11 ( November 2014), 17-20. DOI=10.5120/18421-9723

@article{ 10.5120/18421-9723,
author = { Gurudatta V Nayak, Anuja A Rao, Nandana Prabhu },
title = { K-Means Clustering Algorithm with Color-based Thresholding for Satellite Images },
journal = { International Journal of Computer Applications },
issue_date = { November 2014 },
volume = { 105 },
number = { 11 },
month = { November },
year = { 2014 },
issn = { 0975-8887 },
pages = { 17-20 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume105/number11/18421-9723/ },
doi = { 10.5120/18421-9723 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:37:27.373534+05:30
%A Gurudatta V Nayak
%A Anuja A Rao
%A Nandana Prabhu
%T K-Means Clustering Algorithm with Color-based Thresholding for Satellite Images
%J International Journal of Computer Applications
%@ 0975-8887
%V 105
%N 11
%P 17-20
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Land cover classification is an essential input to environmental and land use planning . Clustering is a technique used for land cover classification. Clustering is the assignment of objects into groups called clusters so that objects from the same cluster are more similar to each other than objects from different clusters. The proposed work presents an algorithm Using K means clustering algorithm with color based thresholding for classification of a satellite image. It is observed that this method gives better accuracy as compared using only K means clustering algorithm. The image quality metrics used are overall accuracy, user's accuracy, producer accuracy, average accuracy for user and producer.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Image segmentation Remote sensing K-means K-means with color based thresholding Confusion Matrix.