Discovering similarities in Landsat satellite images using the K-means method

Ariza Colpas Paola Patricia; Oviedo Carrascal Ana Isabel; De La Hoz Franco Emiro

doi:10.1016/j.procs.2020.03.017

Discovering similarities in Landsat satellite images using the K-means method

Ariza Colpas Paola Patricia, Oviedo Carrascal Ana Isabel, De La Hoz Franco Emiro

Producción científica: Contribución a una revista › Ponencia publicada en las memorias del evento con ISSN › revisión exhaustiva

3 Citas (Scopus)

Resumen

This article different ways for the treatment and identification of similarities in satellite images. By means of the systematic review of the literature it is possible to know the different existing forms for the treatment of this type of objects and by means of the implementation that is described, the operation of the K-means algorithm is shown to help the segmentation and analysis of characteristics associated to the color. In this type of objects, a descriptive analysis of the results thrown by the method is finally carried out.

Idioma original	Inglés
Páginas (desde-hasta)	129-136
Número de páginas	8
Publicación	Procedia Computer Science
Volumen	170
DOI	https://doi.org/10.1016/j.procs.2020.03.017
Estado	Publicada - 2020
Evento	11th International Conference on Ambient Systems, Networks and Technologies, ANT 2020 / 3rd International Conference on Emerging Data and Industry 4.0, EDI40 2020 / Affiliated Workshops - Warsaw, Polonia Duración: 6 abr. 2020 → 9 abr. 2020

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© 2020 The Authors. Published by Elsevier B.V.All rights reserved.

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@article{6988db80d3b445a0ad17d8f09d4bb594,

title = "Discovering similarities in Landsat satellite images using the K-means method",

abstract = "This article different ways for the treatment and identification of similarities in satellite images. By means of the systematic review of the literature it is possible to know the different existing forms for the treatment of this type of objects and by means of the implementation that is described, the operation of the K-means algorithm is shown to help the segmentation and analysis of characteristics associated to the color. In this type of objects, a descriptive analysis of the results thrown by the method is finally carried out.",

keywords = "Clustering, Multiclustering, Multimedia Multidimensional Georreferenced Objects, Satellite Images",

author = "{Paola Patricia}, {Ariza Colpas} and {Ana Isabel}, {Oviedo Carrascal} and Emiro, {De La Hoz Franco}",

note = "Publisher Copyright: {\textcopyright} 2020 The Authors. Published by Elsevier B.V.All rights reserved.; 11th International Conference on Ambient Systems, Networks and Technologies, ANT 2020 / 3rd International Conference on Emerging Data and Industry 4.0, EDI40 2020 / Affiliated Workshops ; Conference date: 06-04-2020 Through 09-04-2020",

year = "2020",

doi = "10.1016/j.procs.2020.03.017",

language = "Ingl{\'e}s",

volume = "170",

pages = "129--136",

journal = "Procedia Computer Science",

issn = "1877-0509",

publisher = "Elsevier B.V.",

}

TY - JOUR

T1 - Discovering similarities in Landsat satellite images using the K-means method

AU - Paola Patricia, Ariza Colpas

AU - Ana Isabel, Oviedo Carrascal

AU - Emiro, De La Hoz Franco

PY - 2020

Y1 - 2020

N2 - This article different ways for the treatment and identification of similarities in satellite images. By means of the systematic review of the literature it is possible to know the different existing forms for the treatment of this type of objects and by means of the implementation that is described, the operation of the K-means algorithm is shown to help the segmentation and analysis of characteristics associated to the color. In this type of objects, a descriptive analysis of the results thrown by the method is finally carried out.

AB - This article different ways for the treatment and identification of similarities in satellite images. By means of the systematic review of the literature it is possible to know the different existing forms for the treatment of this type of objects and by means of the implementation that is described, the operation of the K-means algorithm is shown to help the segmentation and analysis of characteristics associated to the color. In this type of objects, a descriptive analysis of the results thrown by the method is finally carried out.

KW - Clustering

KW - Multiclustering

KW - Multimedia Multidimensional Georreferenced Objects

KW - Satellite Images

UR - http://www.scopus.com/inward/record.url?scp=85085567918&partnerID=8YFLogxK

U2 - 10.1016/j.procs.2020.03.017

DO - 10.1016/j.procs.2020.03.017

M3 - Ponencia publicada en las memorias del evento con ISSN

AN - SCOPUS:85085567918

SN - 1877-0509

VL - 170

SP - 129

EP - 136

JO - Procedia Computer Science

JF - Procedia Computer Science

T2 - 11th International Conference on Ambient Systems, Networks and Technologies, ANT 2020 / 3rd International Conference on Emerging Data and Industry 4.0, EDI40 2020 / Affiliated Workshops

Y2 - 6 April 2020 through 9 April 2020

ER -

Discovering similarities in Landsat satellite images using the K-means method

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