A framework of filtering rules over ground truth samples to achieve higher accuracy in land cover maps

Remote Sensing (RS) digital classification techniques require sufficient, accurate and ubiquitously distributed ground truth (GT) samples. GT is usually considered "true" per se; however, human errors, or differences in criteria when defining classes, among other reasons, often undermine t...

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Autores: Padial-Iglesias, Mario|||0000-0002-8173-5353, Serra Ruiz, Pere|||0000-0003-1023-5586, Ninyerola i Casals, Miquel|||0000-0002-1101-0453, Pons, Xavier|||0000-0002-6924-1641
Formato: artículo
Fecha de publicación:2021
País:España
Recursos:Universitat Autònoma de Barcelona
Repositorio:Dipòsit Digital de Documents de la UAB
Idioma:inglés
OAI Identifier:oai:ddd.uab.cat:251098
Acesso em linha:https://ddd.uab.cat/record/251098
https://dx.doi.org/urn:doi:10.3390/rs13142662
Access Level:acceso abierto
Palavra-chave:Land-cover change mapping
Landsat
Digital image classification
Ground truth samples
Filtering rules
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spelling A framework of filtering rules over ground truth samples to achieve higher accuracy in land cover mapsPadial-Iglesias, Mario|||0000-0002-8173-5353Serra Ruiz, Pere|||0000-0003-1023-5586Ninyerola i Casals, Miquel|||0000-0002-1101-0453Pons, Xavier|||0000-0002-6924-1641Land-cover change mappingLandsatDigital image classificationGround truth samplesFiltering rulesRemote Sensing (RS) digital classification techniques require sufficient, accurate and ubiquitously distributed ground truth (GT) samples. GT is usually considered "true" per se; however, human errors, or differences in criteria when defining classes, among other reasons, often undermine this veracity. Trusting the GT is so crucial that protocols should be defined for making additional quality checks before passing to the classification stage. Fortunately, the nature of RS imagery allows setting a framework of quality controls to improve the confidence in the GT areas by proposing a set of filtering rules based on data from the images themselves. In our experiment, two pre-existing reference datasets (rDS) were used to obtain GT candidate pixels, over which inconsistencies were identified. This served as a basis for inferring five key filtering rules based on NDVI data, a product available from almost all RS instruments. We evaluated the performance of the rules in four temporal study cases (under backdating and updating scenarios) and two study areas. In each case, a set of GT samples was extracted from the rDS and the set was used both unfiltered (original) and filtered according to the rules. Our proposal shows that the filtered GT samples made it possible to solve usual problems in wilderness and agricultural categories. Indeed, the confusion matrices revealed, on average, an increase in the overall accuracy of 10.9, a decrease in the omission error of 16.8, and a decrease in the commission error of 14.0, all values in percent points. Filtering rules corrected inconsistencies in the GT samples extracted from the rDS by considering inter-annual and intra-annual differences, scale issues, multiple behaviours over time and labelling misassignments. Therefore, although some intrinsic limitations have been detected (as in mixed forests), the protocol allows a much better Land Cover mapping thanks to using more robust GT samples, something particularly important in a multitemporal context in which accounting for phenology is essential. 22021-01-0120212021-01-01Articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://ddd.uab.cat/record/251098https://dx.doi.org/urn:doi:10.3390/rs13142662reponame:Dipòsit Digital de Documents de la UABinstname:Universitat Autònoma de BarcelonaInglésengAgencia Estatal de Investigación https://doi.org/10.13039/501100011033 BES-2016-078262Agencia Estatal de Investigación https://doi.org/10.13039/501100011033 RTI2018-099397-B-C21open accesshttp://purl.org/coar/access_right/c_abf2Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:ddd.uab.cat:2510982026-06-06T12:50:31Z
dc.title.none.fl_str_mv A framework of filtering rules over ground truth samples to achieve higher accuracy in land cover maps
title A framework of filtering rules over ground truth samples to achieve higher accuracy in land cover maps
spellingShingle A framework of filtering rules over ground truth samples to achieve higher accuracy in land cover maps
Padial-Iglesias, Mario|||0000-0002-8173-5353
Land-cover change mapping
Landsat
Digital image classification
Ground truth samples
Filtering rules
title_short A framework of filtering rules over ground truth samples to achieve higher accuracy in land cover maps
title_full A framework of filtering rules over ground truth samples to achieve higher accuracy in land cover maps
title_fullStr A framework of filtering rules over ground truth samples to achieve higher accuracy in land cover maps
title_full_unstemmed A framework of filtering rules over ground truth samples to achieve higher accuracy in land cover maps
title_sort A framework of filtering rules over ground truth samples to achieve higher accuracy in land cover maps
dc.creator.none.fl_str_mv Padial-Iglesias, Mario|||0000-0002-8173-5353
Serra Ruiz, Pere|||0000-0003-1023-5586
Ninyerola i Casals, Miquel|||0000-0002-1101-0453
Pons, Xavier|||0000-0002-6924-1641
author Padial-Iglesias, Mario|||0000-0002-8173-5353
author_facet Padial-Iglesias, Mario|||0000-0002-8173-5353
Serra Ruiz, Pere|||0000-0003-1023-5586
Ninyerola i Casals, Miquel|||0000-0002-1101-0453
Pons, Xavier|||0000-0002-6924-1641
author_role author
author2 Serra Ruiz, Pere|||0000-0003-1023-5586
Ninyerola i Casals, Miquel|||0000-0002-1101-0453
Pons, Xavier|||0000-0002-6924-1641
author2_role author
author
author
dc.subject.none.fl_str_mv Land-cover change mapping
Landsat
Digital image classification
Ground truth samples
Filtering rules
topic Land-cover change mapping
Landsat
Digital image classification
Ground truth samples
Filtering rules
description Remote Sensing (RS) digital classification techniques require sufficient, accurate and ubiquitously distributed ground truth (GT) samples. GT is usually considered "true" per se; however, human errors, or differences in criteria when defining classes, among other reasons, often undermine this veracity. Trusting the GT is so crucial that protocols should be defined for making additional quality checks before passing to the classification stage. Fortunately, the nature of RS imagery allows setting a framework of quality controls to improve the confidence in the GT areas by proposing a set of filtering rules based on data from the images themselves. In our experiment, two pre-existing reference datasets (rDS) were used to obtain GT candidate pixels, over which inconsistencies were identified. This served as a basis for inferring five key filtering rules based on NDVI data, a product available from almost all RS instruments. We evaluated the performance of the rules in four temporal study cases (under backdating and updating scenarios) and two study areas. In each case, a set of GT samples was extracted from the rDS and the set was used both unfiltered (original) and filtered according to the rules. Our proposal shows that the filtered GT samples made it possible to solve usual problems in wilderness and agricultural categories. Indeed, the confusion matrices revealed, on average, an increase in the overall accuracy of 10.9, a decrease in the omission error of 16.8, and a decrease in the commission error of 14.0, all values in percent points. Filtering rules corrected inconsistencies in the GT samples extracted from the rDS by considering inter-annual and intra-annual differences, scale issues, multiple behaviours over time and labelling misassignments. Therefore, although some intrinsic limitations have been detected (as in mixed forests), the protocol allows a much better Land Cover mapping thanks to using more robust GT samples, something particularly important in a multitemporal context in which accounting for phenology is essential.
publishDate 2021
dc.date.none.fl_str_mv 2
2021-01-01
2021
2021-01-01
dc.type.none.fl_str_mv Article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://ddd.uab.cat/record/251098
https://dx.doi.org/urn:doi:10.3390/rs13142662
url https://ddd.uab.cat/record/251098
https://dx.doi.org/urn:doi:10.3390/rs13142662
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Agencia Estatal de Investigación https://doi.org/10.13039/501100011033 BES-2016-078262
Agencia Estatal de Investigación https://doi.org/10.13039/501100011033 RTI2018-099397-B-C21
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
https://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
https://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Dipòsit Digital de Documents de la UAB
instname:Universitat Autònoma de Barcelona
instname_str Universitat Autònoma de Barcelona
reponame_str Dipòsit Digital de Documents de la UAB
collection Dipòsit Digital de Documents de la UAB
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