A general and extensible framework for assessing change detection techniques
Autor(a) principal: | |
---|---|
Data de Publicação: | 2023 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1016/j.cageo.2023.105390 http://hdl.handle.net/11449/247498 |
Resumo: | Change detection techniques play an essential role in Remote Sensing applications, such as environmental monitoring, governmental planning, and studies of areas affected by natural disasters. This fact makes the development of more accurate change detection techniques a constant challenge. However, the lack of public benchmarks available to analyze and compare the performance of change detection techniques hampers quantitative comparisons. In light of this reality, this study proposes and formalizes a novel framework for imagery dataset simulation. In contrast with other image simulation methods, images synthesized by the proposed method are explicitly designed to assess and compare change detection methods. The framework is extensible and general allowing, in particular, the use of both supervised and unsupervised change detection methods. As an application, we compare the performance of well-known algorithms to data sets that mimic what the Landsat 5 TM sensor observed over a forest area subjected to deforestation for agricultural purposes. The results support discussing the performance of methods and show the usefulness of the proposed framework. We provide the source codes in a public repository. |
id |
UNSP_0e5dacee9c6e9a8c2487982f3dab249a |
---|---|
oai_identifier_str |
oai:repositorio.unesp.br:11449/247498 |
network_acronym_str |
UNSP |
network_name_str |
Repositório Institucional da UNESP |
repository_id_str |
2946 |
spelling |
A general and extensible framework for assessing change detection techniquesChange detectionClassification assessmentImage simulationChange detection techniques play an essential role in Remote Sensing applications, such as environmental monitoring, governmental planning, and studies of areas affected by natural disasters. This fact makes the development of more accurate change detection techniques a constant challenge. However, the lack of public benchmarks available to analyze and compare the performance of change detection techniques hampers quantitative comparisons. In light of this reality, this study proposes and formalizes a novel framework for imagery dataset simulation. In contrast with other image simulation methods, images synthesized by the proposed method are explicitly designed to assess and compare change detection methods. The framework is extensible and general allowing, in particular, the use of both supervised and unsupervised change detection methods. As an application, we compare the performance of well-known algorithms to data sets that mimic what the Landsat 5 TM sensor observed over a forest area subjected to deforestation for agricultural purposes. The results support discussing the performance of methods and show the usefulness of the proposed framework. We provide the source codes in a public repository.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)São Paulo State University – UNESP Institute of Science and Technology – ICT, São José dos CamposSchool of Mathematics and Statistics Victoria University of Wellington — VUWSão Paulo State University – UNESP Institute of Science and Technology – ICT, São José dos CamposFAPESP: 2018/01033-3FAPESP: 2021/01305-6CNPq: 305220/2022-5Universidade Estadual Paulista (UNESP)Victoria University of Wellington — VUWNegri, Rogério G. [UNESP]Frery, Alejandro C.2023-07-29T13:17:40Z2023-07-29T13:17:40Z2023-09-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1016/j.cageo.2023.105390Computers and Geosciences, v. 178.0098-3004http://hdl.handle.net/11449/24749810.1016/j.cageo.2023.1053902-s2.0-85160754161Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengComputers and Geosciencesinfo:eu-repo/semantics/openAccess2023-07-29T13:17:40Zoai:repositorio.unesp.br:11449/247498Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462023-07-29T13:17:40Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
A general and extensible framework for assessing change detection techniques |
title |
A general and extensible framework for assessing change detection techniques |
spellingShingle |
A general and extensible framework for assessing change detection techniques Negri, Rogério G. [UNESP] Change detection Classification assessment Image simulation |
title_short |
A general and extensible framework for assessing change detection techniques |
title_full |
A general and extensible framework for assessing change detection techniques |
title_fullStr |
A general and extensible framework for assessing change detection techniques |
title_full_unstemmed |
A general and extensible framework for assessing change detection techniques |
title_sort |
A general and extensible framework for assessing change detection techniques |
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 — VUW |
dc.contributor.author.fl_str_mv |
Negri, Rogério G. [UNESP] Frery, Alejandro C. |
dc.subject.por.fl_str_mv |
Change detection Classification assessment Image simulation |
topic |
Change detection Classification assessment Image simulation |
description |
Change detection techniques play an essential role in Remote Sensing applications, such as environmental monitoring, governmental planning, and studies of areas affected by natural disasters. This fact makes the development of more accurate change detection techniques a constant challenge. However, the lack of public benchmarks available to analyze and compare the performance of change detection techniques hampers quantitative comparisons. In light of this reality, this study proposes and formalizes a novel framework for imagery dataset simulation. In contrast with other image simulation methods, images synthesized by the proposed method are explicitly designed to assess and compare change detection methods. The framework is extensible and general allowing, in particular, the use of both supervised and unsupervised change detection methods. As an application, we compare the performance of well-known algorithms to data sets that mimic what the Landsat 5 TM sensor observed over a forest area subjected to deforestation for agricultural purposes. The results support discussing the performance of methods and show the usefulness of the proposed framework. We provide the source codes in a public repository. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-07-29T13:17:40Z 2023-07-29T13:17:40Z 2023-09-01 |
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/10.1016/j.cageo.2023.105390 Computers and Geosciences, v. 178. 0098-3004 http://hdl.handle.net/11449/247498 10.1016/j.cageo.2023.105390 2-s2.0-85160754161 |
url |
http://dx.doi.org/10.1016/j.cageo.2023.105390 http://hdl.handle.net/11449/247498 |
identifier_str_mv |
Computers and Geosciences, v. 178. 0098-3004 10.1016/j.cageo.2023.105390 2-s2.0-85160754161 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Computers and Geosciences |
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_ |
1803046848416448512 |