Unsupervised Change Detection Driven by Floating References: A Pattern Analysis Approach
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1007/s10044-020-00954-w http://hdl.handle.net/11449/205698 |
Resumo: | The Earth’s environment is continually changing due to both human and natural factors. Timely identification of the location and kind of change is of paramount importance in several areas of application. Because of that, remote sensing change detection is a topic of great interest. The development of precise change detection methods is a constant challenge. This study introduces a novel unsupervised change detection method based on data clustering and optimization. The proposal is less dependent on radiometric normalization than classical approaches. We carried experiments with remote sensing images and simulated datasets to compare the proposed method with other unsupervised well-known techniques. At its best, the proposal improves by 50% the accuracy concerning the second best technique. Such improvement is most noticeable with uncalibrated data. Experiments with simulated data reveal that the proposal is better than all other compared methods at any practical significance level. The results show the potential of the proposed method. |
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Repositório Institucional da UNESP |
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2946 |
spelling |
Unsupervised Change Detection Driven by Floating References: A Pattern Analysis ApproachPattern analysisRemote sensingUnsupervised change detectionThe Earth’s environment is continually changing due to both human and natural factors. Timely identification of the location and kind of change is of paramount importance in several areas of application. Because of that, remote sensing change detection is a topic of great interest. The development of precise change detection methods is a constant challenge. This study introduces a novel unsupervised change detection method based on data clustering and optimization. The proposal is less dependent on radiometric normalization than classical approaches. We carried experiments with remote sensing images and simulated datasets to compare the proposed method with other unsupervised well-known techniques. At its best, the proposal improves by 50% the accuracy concerning the second best technique. Such improvement is most noticeable with uncalibrated data. Experiments with simulated data reveal that the proposal is better than all other compared methods at any practical significance level. The results show the potential of the proposed method.Department of Environmental Engineering Institute of Science and Technology—ICT São Paulo State University—UNESPSchool of Mathematics and Statistics Victoria University of WellingtonDepartment of Environmental Engineering Institute of Science and Technology—ICT São Paulo State University—UNESPUniversidade Estadual Paulista (Unesp)Victoria University of WellingtonNegri, Rogério G. [UNESP]Frery, Alejandro C.2021-06-25T10:19:49Z2021-06-25T10:19:49Z2021-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1007/s10044-020-00954-wPattern Analysis and Applications.1433-755X1433-7541http://hdl.handle.net/11449/20569810.1007/s10044-020-00954-w2-s2.0-85099041428Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengPattern Analysis and Applicationsinfo:eu-repo/semantics/openAccess2021-10-22T13:22:22Zoai:repositorio.unesp.br:11449/205698Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T21:03:29.882056Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Unsupervised Change Detection Driven by Floating References: A Pattern Analysis Approach |
title |
Unsupervised Change Detection Driven by Floating References: A Pattern Analysis Approach |
spellingShingle |
Unsupervised Change Detection Driven by Floating References: A Pattern Analysis Approach Negri, Rogério G. [UNESP] Pattern analysis Remote sensing Unsupervised change detection |
title_short |
Unsupervised Change Detection Driven by Floating References: A Pattern Analysis Approach |
title_full |
Unsupervised Change Detection Driven by Floating References: A Pattern Analysis Approach |
title_fullStr |
Unsupervised Change Detection Driven by Floating References: A Pattern Analysis Approach |
title_full_unstemmed |
Unsupervised Change Detection Driven by Floating References: A Pattern Analysis Approach |
title_sort |
Unsupervised Change Detection Driven by Floating References: A Pattern Analysis Approach |
author |
Negri, Rogério G. [UNESP] |
author_facet |
Negri, Rogério G. [UNESP] Frery, Alejandro C. |
author_role |
author |
author2 |
Frery, Alejandro C. |
author2_role |
author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) Victoria University of Wellington |
dc.contributor.author.fl_str_mv |
Negri, Rogério G. [UNESP] Frery, Alejandro C. |
dc.subject.por.fl_str_mv |
Pattern analysis Remote sensing Unsupervised change detection |
topic |
Pattern analysis Remote sensing Unsupervised change detection |
description |
The Earth’s environment is continually changing due to both human and natural factors. Timely identification of the location and kind of change is of paramount importance in several areas of application. Because of that, remote sensing change detection is a topic of great interest. The development of precise change detection methods is a constant challenge. This study introduces a novel unsupervised change detection method based on data clustering and optimization. The proposal is less dependent on radiometric normalization than classical approaches. We carried experiments with remote sensing images and simulated datasets to compare the proposed method with other unsupervised well-known techniques. At its best, the proposal improves by 50% the accuracy concerning the second best technique. Such improvement is most noticeable with uncalibrated data. Experiments with simulated data reveal that the proposal is better than all other compared methods at any practical significance level. The results show the potential of the proposed method. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-06-25T10:19:49Z 2021-06-25T10:19:49Z 2021-01-01 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1007/s10044-020-00954-w Pattern Analysis and Applications. 1433-755X 1433-7541 http://hdl.handle.net/11449/205698 10.1007/s10044-020-00954-w 2-s2.0-85099041428 |
url |
http://dx.doi.org/10.1007/s10044-020-00954-w http://hdl.handle.net/11449/205698 |
identifier_str_mv |
Pattern Analysis and Applications. 1433-755X 1433-7541 10.1007/s10044-020-00954-w 2-s2.0-85099041428 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Pattern Analysis and Applications |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808129279331926016 |