Discovering similarities in Landsat satellite images using the Kmeans method

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...

Full description

Autores:
Ariza Colpas, Paola Patricia
Oviedo Carrascal, Ana Isabel
De-La-Hoz-Franco, Emiro
Tipo de recurso:
Article of journal
Fecha de publicación:
2020
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/6227
Acceso en línea:
https://hdl.handle.net/11323/6227
https://doi.org/10.1016/j.procs.2020.03.017
https://repositorio.cuc.edu.co/
Palabra clave:
Multiclustering
Multimedia
Multimedia multidimensional georreferenced objects
Satellite images
Rights
openAccess
License
CC0 1.0 Universal
id RCUC2_c092c364d83f1b08cd00a1a2377fb6f6
oai_identifier_str oai:repositorio.cuc.edu.co:11323/6227
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv Discovering similarities in Landsat satellite images using the Kmeans method
title Discovering similarities in Landsat satellite images using the Kmeans method
spellingShingle Discovering similarities in Landsat satellite images using the Kmeans method
Multiclustering
Multimedia
Multimedia multidimensional georreferenced objects
Satellite images
title_short Discovering similarities in Landsat satellite images using the Kmeans method
title_full Discovering similarities in Landsat satellite images using the Kmeans method
title_fullStr Discovering similarities in Landsat satellite images using the Kmeans method
title_full_unstemmed Discovering similarities in Landsat satellite images using the Kmeans method
title_sort Discovering similarities in Landsat satellite images using the Kmeans method
dc.creator.fl_str_mv Ariza Colpas, Paola Patricia
Oviedo Carrascal, Ana Isabel
De-La-Hoz-Franco, Emiro
dc.contributor.author.spa.fl_str_mv Ariza Colpas, Paola Patricia
Oviedo Carrascal, Ana Isabel
De-La-Hoz-Franco, Emiro
dc.subject.spa.fl_str_mv Multiclustering
Multimedia
Multimedia multidimensional georreferenced objects
Satellite images
topic Multiclustering
Multimedia
Multimedia multidimensional georreferenced objects
Satellite images
description 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.
publishDate 2020
dc.date.accessioned.none.fl_str_mv 2020-04-20T21:58:57Z
dc.date.available.none.fl_str_mv 2020-04-20T21:58:57Z
dc.date.issued.none.fl_str_mv 2020
dc.type.spa.fl_str_mv Artículo de revista
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dc.identifier.issn.spa.fl_str_mv 1877-0509
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dc.identifier.doi.spa.fl_str_mv https://doi.org/10.1016/j.procs.2020.03.017
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
identifier_str_mv 1877-0509
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/6227
https://doi.org/10.1016/j.procs.2020.03.017
https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.references.spa.fl_str_mv [1] Ariza-Colpas, P., Morales-Ortega, R., Piñeres-Melo, M. A., Melendez-Pertuz, F., Serrano-Torné, G., Hernandez-Sanchez, G., ... & CollazosMorales, C. (2019, October). Teleagro: Software Architecture of Georeferencing and Detection of Heat of Cattle. In Workshop on Engineering Applications (pp. 159-166). Springer, Cham.
[2] Ariza, P., Pineres, M., Santiago, L., Mercado, N., & De la Hoz, A. (2014, November). Implementation of moprosoft level I and II in software development companies in the colombian caribbean, a commitment to the software product quality region. In 2014 IEEE Central America and Panama Convention (CONCAPAN XXXIV) (pp. 1-5). IEEE.
[3] Calabria-Sarmiento, J. C., Ariza-Colpas, P., Pineres-Melo, M., Ayala-Mantilla, C., Urina-Triana, M., Morales-Ortega, R., ... & EcheverriOcampo, I. (2018). Software applications to health sector: A systematic review of literature.
[4] Echeverri-Ocampo, I., Urina-Triana, M., Patricia Ariza, P., & Mantilla, M. (2018). El trabajo colaborativo entre ingenieros y personal de la salud para el desarrollo de proyectos en salud digital: una visión al futuro para lograr tener éxito.
[5] Jimeno Gonzalez, K. J., Ariza Colpas, P. P., & Piñeres Melo, M. (2017). Gobierno de TI en pymes colombianas.¿ mito o realidad?.
[6] Ariza-Colpas, P., Morales-Ortega, R., Piñeres-Melo, M., De la Hoz-Franco, E., Echeverri-Ocampo, I., & Salas-Navarro, K. (2019, July). Parkinson Disease Analysis Using Supervised and Unsupervised Techniques. In International Conference on Swarm Intelligence (pp. 191-199). Springer, Cham.
[7] Ariza-Colpas, P., Piñeres-Melo, M., Barceló-Martinez, E., De la Hoz-Franco, E., Benitez-Agudelo, J., Gelves-Ospina, M., ... & Leon-Jacobus, A. (2019, July). Enkephalon-technological platform to support the diagnosis of alzheimer’s disease through the analysis of resonance images using data mining techniques. In International Conference on Swarm Intelligence (pp. 211-220). Springer, Cham.
[8] Ariza-Colpas, P. P., Piñeres-Melo, M. A., Nieto-Bernal, W., & Morales-Ortega, R. (2019, July). WSIA: Web Ontological Search Engine Based on Smart Agents Applied to Scientific Articles. In International Conference on Swarm Intelligence (pp. 338-347). Springer, Cham.
[9] Piñeres-Melo, M. A., Ariza-Colpas, P. P., Nieto-Bernal, W., & Morales-Ortega, R. (2019, July). SSwWS: Structural Model of Information Architecture. In International Conference on Swarm Intelligence (pp. 400-410). Springer, Cham.
[10] Ariza-Colpas, P., Oviedo-Carrascal, A. I., & De-la-hoz-Franco, E. (2019, July). Using K-Means Algorithm for Description Analysis of Text in RSS News Format. In International Conference on Data Mining and Big Data (pp. 162-169). Springer, Singapore.
[11] Koundal, D., Gupta, S., & Singh, S. (2016). Automated delineation of thyroid nodules in ultrasound images using spatial neutrosophic clustering and level set. Applied Soft Computing, 40, 86-97
[12] Alias, H. M., Rekha, K. S., & Anitha, R. (2016). Reveal Difference in Synthetic Aperture Radar Images Implementing Fuzzy Clustering Along With Improved MRF Energy Function and Wavelet Denoising Technique. Procedia Technology, 24, 1325-1332
[13] Ariza-Colpas, P., Morales-Ortega, R., Piñeres-Melo, M. A., Melendez-Pertuz, F., Serrano-Torné, G., Hernandez-Sanchez, G., & MartínezOsorio, H. (2019, September). Teleagro: iot applications for the georeferencing and detection of zeal in cattle. In IFIP International Conference on Computer Information Systems and Industrial Management (pp. 232-239). Springer, Cham.
[14] Banerjee, A., & Maji, P. (2016). Rough-probabilistic clustering and hidden Markov random field model for segmentation of HEp-2 cell and brain MR images. Applied Soft Computing
[15] Hou, J., Liu, W., Xu, E., & Cui, H. (2016). Towards parameter-independent data clustering and image segmentation. Pattern Recognition, 60, 25-36
[16] Zhang, H., & Dai, G. (2016). Improvement of distributed clustering algorithm based on min-cluster. Optik-International Journal for Light and Electron Optics, 127(8), 3878-3881.
[17] Reboul, C. F., Bonnet, F., Elmlund, D., & Elmlund, H. (2016). A Stochastic Hill Climbing Approach for Simultaneous 2D Alignment and Clustering of Cryogenic Electron Microscopy Images. Structure, 24(6), 988-996.
[18] Jin, X., & Kim, J. (2016). Video fragment format classification using optimized discriminative subspace clustering. Signal Processing: Image Communication, 40, 26-35.
[19] Viloria, A., & Lezama, O. B. P. (2019). An intelligent approach for the design and development of a personalized system of knowledge representation. Procedia Comput. Sci, 151, 1225-1230.
[20] Pineda Lezama, O. B., & Reniz, J. (2019). Recommendation of collaborative filtering for a technological surveillance model using MultiDimension Tensor Factorization
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spelling Ariza Colpas, Paola PatriciaOviedo Carrascal, Ana IsabelDe-La-Hoz-Franco, Emiro2020-04-20T21:58:57Z2020-04-20T21:58:57Z20201877-0509https://hdl.handle.net/11323/6227https://doi.org/10.1016/j.procs.2020.03.017Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/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.Ariza Colpas, Paola Patricia-will be generated-orcid-0000-0003-4503-5461-600Oviedo Carrascal, Ana Isabel-will be generated-orcid-0000-0002-7105-7819-600De-La-Hoz-Franco, Emiro-will be generated-orcid-0000-0002-4926-7414-600engProcedia Computer ScienceCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2MulticlusteringMultimediaMultimedia multidimensional georreferenced objectsSatellite imagesDiscovering similarities in Landsat satellite images using the Kmeans methodArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion[1] Ariza-Colpas, P., Morales-Ortega, R., Piñeres-Melo, M. A., Melendez-Pertuz, F., Serrano-Torné, G., Hernandez-Sanchez, G., ... & CollazosMorales, C. (2019, October). Teleagro: Software Architecture of Georeferencing and Detection of Heat of Cattle. In Workshop on Engineering Applications (pp. 159-166). Springer, Cham.[2] Ariza, P., Pineres, M., Santiago, L., Mercado, N., & De la Hoz, A. (2014, November). Implementation of moprosoft level I and II in software development companies in the colombian caribbean, a commitment to the software product quality region. In 2014 IEEE Central America and Panama Convention (CONCAPAN XXXIV) (pp. 1-5). IEEE.[3] Calabria-Sarmiento, J. C., Ariza-Colpas, P., Pineres-Melo, M., Ayala-Mantilla, C., Urina-Triana, M., Morales-Ortega, R., ... & EcheverriOcampo, I. (2018). Software applications to health sector: A systematic review of literature.[4] Echeverri-Ocampo, I., Urina-Triana, M., Patricia Ariza, P., & Mantilla, M. (2018). El trabajo colaborativo entre ingenieros y personal de la salud para el desarrollo de proyectos en salud digital: una visión al futuro para lograr tener éxito.[5] Jimeno Gonzalez, K. J., Ariza Colpas, P. P., & Piñeres Melo, M. (2017). Gobierno de TI en pymes colombianas.¿ mito o realidad?.[6] Ariza-Colpas, P., Morales-Ortega, R., Piñeres-Melo, M., De la Hoz-Franco, E., Echeverri-Ocampo, I., & Salas-Navarro, K. (2019, July). Parkinson Disease Analysis Using Supervised and Unsupervised Techniques. In International Conference on Swarm Intelligence (pp. 191-199). Springer, Cham.[7] Ariza-Colpas, P., Piñeres-Melo, M., Barceló-Martinez, E., De la Hoz-Franco, E., Benitez-Agudelo, J., Gelves-Ospina, M., ... & Leon-Jacobus, A. (2019, July). Enkephalon-technological platform to support the diagnosis of alzheimer’s disease through the analysis of resonance images using data mining techniques. In International Conference on Swarm Intelligence (pp. 211-220). Springer, Cham.[8] Ariza-Colpas, P. P., Piñeres-Melo, M. A., Nieto-Bernal, W., & Morales-Ortega, R. (2019, July). WSIA: Web Ontological Search Engine Based on Smart Agents Applied to Scientific Articles. In International Conference on Swarm Intelligence (pp. 338-347). Springer, Cham.[9] Piñeres-Melo, M. A., Ariza-Colpas, P. P., Nieto-Bernal, W., & Morales-Ortega, R. (2019, July). SSwWS: Structural Model of Information Architecture. In International Conference on Swarm Intelligence (pp. 400-410). Springer, Cham.[10] Ariza-Colpas, P., Oviedo-Carrascal, A. I., & De-la-hoz-Franco, E. (2019, July). Using K-Means Algorithm for Description Analysis of Text in RSS News Format. In International Conference on Data Mining and Big Data (pp. 162-169). Springer, Singapore.[11] Koundal, D., Gupta, S., & Singh, S. (2016). Automated delineation of thyroid nodules in ultrasound images using spatial neutrosophic clustering and level set. Applied Soft Computing, 40, 86-97[12] Alias, H. M., Rekha, K. S., & Anitha, R. (2016). Reveal Difference in Synthetic Aperture Radar Images Implementing Fuzzy Clustering Along With Improved MRF Energy Function and Wavelet Denoising Technique. Procedia Technology, 24, 1325-1332[13] Ariza-Colpas, P., Morales-Ortega, R., Piñeres-Melo, M. A., Melendez-Pertuz, F., Serrano-Torné, G., Hernandez-Sanchez, G., & MartínezOsorio, H. (2019, September). Teleagro: iot applications for the georeferencing and detection of zeal in cattle. In IFIP International Conference on Computer Information Systems and Industrial Management (pp. 232-239). Springer, Cham.[14] Banerjee, A., & Maji, P. (2016). Rough-probabilistic clustering and hidden Markov random field model for segmentation of HEp-2 cell and brain MR images. Applied Soft Computing[15] Hou, J., Liu, W., Xu, E., & Cui, H. (2016). Towards parameter-independent data clustering and image segmentation. Pattern Recognition, 60, 25-36[16] Zhang, H., & Dai, G. (2016). Improvement of distributed clustering algorithm based on min-cluster. Optik-International Journal for Light and Electron Optics, 127(8), 3878-3881.[17] Reboul, C. F., Bonnet, F., Elmlund, D., & Elmlund, H. (2016). A Stochastic Hill Climbing Approach for Simultaneous 2D Alignment and Clustering of Cryogenic Electron Microscopy Images. Structure, 24(6), 988-996.[18] Jin, X., & Kim, J. (2016). Video fragment format classification using optimized discriminative subspace clustering. Signal Processing: Image Communication, 40, 26-35.[19] Viloria, A., & Lezama, O. B. P. (2019). An intelligent approach for the design and development of a personalized system of knowledge representation. Procedia Comput. Sci, 151, 1225-1230.[20] Pineda Lezama, O. B., & Reniz, J. (2019). Recommendation of collaborative filtering for a technological surveillance model using MultiDimension Tensor FactorizationPublicationORIGINALDiscovering similarities in Landsat satellite images using the K-means method.pdfDiscovering similarities in Landsat satellite images using the K-means method.pdfapplication/pdf685289https://repositorio.cuc.edu.co/bitstreams/d1170ea9-27f2-4537-8d01-703ccce3667e/downloadfe95b9356478592a1338a464dd5962d7MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8701https://repositorio.cuc.edu.co/bitstreams/3f822310-981d-4012-80c2-ff55f1bffe35/download42fd4ad1e89814f5e4a476b409eb708cMD52LICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorio.cuc.edu.co/bitstreams/3a8dcdca-d201-4296-a899-2bc5c66340be/download8a4605be74aa9ea9d79846c1fba20a33MD53THUMBNAILDiscovering similarities in Landsat satellite images using the K-means method.pdf.jpgDiscovering similarities in Landsat satellite images using the K-means method.pdf.jpgimage/jpeg40294https://repositorio.cuc.edu.co/bitstreams/7456007f-0a7c-44e8-9ecb-d557042c3297/downloadde0ea877f5a8c805ac326f695f6a64ebMD54TEXTDiscovering similarities in Landsat satellite images using the K-means method.pdf.txtDiscovering similarities in Landsat satellite images using the K-means method.pdf.txttext/plain45382https://repositorio.cuc.edu.co/bitstreams/a41095b3-720e-4d3e-9e17-ee43444f69ff/downloadc0f350c8cd266d9ecce0b66668dbd1eeMD5511323/6227oai:repositorio.cuc.edu.co:11323/62272024-09-16 16:40:02.052http://creativecommons.org/publicdomain/zero/1.0/CC0 1.0 Universalopen.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.coTk9URTogUExBQ0UgWU9VUiBPV04gTElDRU5TRSBIRVJFClRoaXMgc2FtcGxlIGxpY2Vuc2UgaXMgcHJvdmlkZWQgZm9yIGluZm9ybWF0aW9uYWwgcHVycG9zZXMgb25seS4KCk5PTi1FWENMVVNJVkUgRElTVFJJQlVUSU9OIExJQ0VOU0UKCkJ5IHNpZ25pbmcgYW5kIHN1Ym1pdHRpbmcgdGhpcyBsaWNlbnNlLCB5b3UgKHRoZSBhdXRob3Iocykgb3IgY29weXJpZ2h0Cm93bmVyKSBncmFudHMgdG8gRFNwYWNlIFVuaXZlcnNpdHkgKERTVSkgdGhlIG5vbi1leGNsdXNpdmUgcmlnaHQgdG8gcmVwcm9kdWNlLAp0cmFuc2xhdGUgKGFzIGRlZmluZWQgYmVsb3cpLCBhbmQvb3IgZGlzdHJpYnV0ZSB5b3VyIHN1Ym1pc3Npb24gKGluY2x1ZGluZwp0aGUgYWJzdHJhY3QpIHdvcmxkd2lkZSBpbiBwcmludCBhbmQgZWxlY3Ryb25pYyBmb3JtYXQgYW5kIGluIGFueSBtZWRpdW0sCmluY2x1ZGluZyBidXQgbm90IGxpbWl0ZWQgdG8gYXVkaW8gb3IgdmlkZW8uCgpZb3UgYWdyZWUgdGhhdCBEU1UgbWF5LCB3aXRob3V0IGNoYW5naW5nIHRoZSBjb250ZW50LCB0cmFuc2xhdGUgdGhlCnN1Ym1pc3Npb24gdG8gYW55IG1lZGl1bSBvciBmb3JtYXQgZm9yIHRoZSBwdXJwb3NlIG9mIHByZXNlcnZhdGlvbi4KCllvdSBhbHNvIGFncmVlIHRoYXQgRFNVIG1heSBrZWVwIG1vcmUgdGhhbiBvbmUgY29weSBvZiB0aGlzIHN1Ym1pc3Npb24gZm9yCnB1cnBvc2VzIG9mIHNlY3VyaXR5LCBiYWNrLXVwIGFuZCBwcmVzZXJ2YXRpb24uCgpZb3UgcmVwcmVzZW50IHRoYXQgdGhlIHN1Ym1pc3Npb24gaXMgeW91ciBvcmlnaW5hbCB3b3JrLCBhbmQgdGhhdCB5b3UgaGF2ZQp0aGUgcmlnaHQgdG8gZ3JhbnQgdGhlIHJpZ2h0cyBjb250YWluZWQgaW4gdGhpcyBsaWNlbnNlLiBZb3UgYWxzbyByZXByZXNlbnQKdGhhdCB5b3VyIHN1Ym1pc3Npb24gZG9lcyBub3QsIHRvIHRoZSBiZXN0IG9mIHlvdXIga25vd2xlZGdlLCBpbmZyaW5nZSB1cG9uCmFueW9uZSdzIGNvcHlyaWdodC4KCklmIHRoZSBzdWJtaXNzaW9uIGNvbnRhaW5zIG1hdGVyaWFsIGZvciB3aGljaCB5b3UgZG8gbm90IGhvbGQgY29weXJpZ2h0LAp5b3UgcmVwcmVzZW50IHRoYXQgeW91IGhhdmUgb2J0YWluZWQgdGhlIHVucmVzdHJpY3RlZCBwZXJtaXNzaW9uIG9mIHRoZQpjb3B5cmlnaHQgb3duZXIgdG8gZ3JhbnQgRFNVIHRoZSByaWdodHMgcmVxdWlyZWQgYnkgdGhpcyBsaWNlbnNlLCBhbmQgdGhhdApzdWNoIHRoaXJkLXBhcnR5IG93bmVkIG1hdGVyaWFsIGlzIGNsZWFybHkgaWRlbnRpZmllZCBhbmQgYWNrbm93bGVkZ2VkCndpdGhpbiB0aGUgdGV4dCBvciBjb250ZW50IG9mIHRoZSBzdWJtaXNzaW9uLgoKSUYgVEhFIFNVQk1JU1NJT04gSVMgQkFTRUQgVVBPTiBXT1JLIFRIQVQgSEFTIEJFRU4gU1BPTlNPUkVEIE9SIFNVUFBPUlRFRApCWSBBTiBBR0VOQ1kgT1IgT1JHQU5JWkFUSU9OIE9USEVSIFRIQU4gRFNVLCBZT1UgUkVQUkVTRU5UIFRIQVQgWU9VIEhBVkUKRlVMRklMTEVEIEFOWSBSSUdIVCBPRiBSRVZJRVcgT1IgT1RIRVIgT0JMSUdBVElPTlMgUkVRVUlSRUQgQlkgU1VDSApDT05UUkFDVCBPUiBBR1JFRU1FTlQuCgpEU1Ugd2lsbCBjbGVhcmx5IGlkZW50aWZ5IHlvdXIgbmFtZShzKSBhcyB0aGUgYXV0aG9yKHMpIG9yIG93bmVyKHMpIG9mIHRoZQpzdWJtaXNzaW9uLCBhbmQgd2lsbCBub3QgbWFrZSBhbnkgYWx0ZXJhdGlvbiwgb3RoZXIgdGhhbiBhcyBhbGxvd2VkIGJ5IHRoaXMKbGljZW5zZSwgdG8geW91ciBzdWJtaXNzaW9uLgo=

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