Improving the empirical line method applied to hyperspectral inland water images by combining reference targets and in situ water measurements
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1080/2150704X.2019.1692383 http://hdl.handle.net/11449/199730 |
Resumo: | Empirical line methods are frequently used to correct images from remote sensing. This method is performed in two steps: the first stage finds the calibration equation representing the data interval and the second step transforms the image data into the quantity established from the equation. Several works have been successfully applied empirical lines over land areas, but it is still a great challenge to correct images from waterbodies. Remote sensing of aquatic environments captures only a small amount of energy because the water absorbs much of it. The response signal of the water is smaller than the signal from other land surface targets. This work presents a new approach to calibrate empirical lines combining reference panels with a water point. For this purpose, we evaluated several combinations of targets using both linear and exponential fit. The best matching was provided with an exponential fit using a single grey reference panel combined with a water point resulting in a coefficient of determination about 0.87, a root-mean-squared error of 0.002 sr−1 and a mean absolute percentage error of 18%. This approach presented suitable results to derive reflectance data from the raw digital numbers from images. |
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Repositório Institucional da UNESP |
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2946 |
spelling |
Improving the empirical line method applied to hyperspectral inland water images by combining reference targets and in situ water measurementsEmpirical line methods are frequently used to correct images from remote sensing. This method is performed in two steps: the first stage finds the calibration equation representing the data interval and the second step transforms the image data into the quantity established from the equation. Several works have been successfully applied empirical lines over land areas, but it is still a great challenge to correct images from waterbodies. Remote sensing of aquatic environments captures only a small amount of energy because the water absorbs much of it. The response signal of the water is smaller than the signal from other land surface targets. This work presents a new approach to calibrate empirical lines combining reference panels with a water point. For this purpose, we evaluated several combinations of targets using both linear and exponential fit. The best matching was provided with an exponential fit using a single grey reference panel combined with a water point resulting in a coefficient of determination about 0.87, a root-mean-squared error of 0.002 sr−1 and a mean absolute percentage error of 18%. This approach presented suitable results to derive reflectance data from the raw digital numbers from images.São Paulo State University (UNESP) Faculty of Sciences and Technology (FCT) Graduate Program of Cartographic Sciences (PPGCC) Campus Presidente Prudente–BrazilSão Paulo State University (UNESP) Faculty of Sciences and Technology (FCT) Graduate Program of Cartographic Sciences (PPGCC) Campus Presidente Prudente–BrazilUniversidade Estadual Paulista (Unesp)do Carmo, Alisson Fernando Coelho [UNESP]Bernardo, Nariane Marselhe Ribeiro [UNESP]Imai, Nilton Nobuhiro [UNESP]Shimabukuro, Milton Hirokazu [UNESP]2020-12-12T01:47:47Z2020-12-12T01:47:47Z2020-02-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article186-194http://dx.doi.org/10.1080/2150704X.2019.1692383Remote Sensing Letters, v. 11, n. 2, p. 186-194, 2020.2150-70582150-704Xhttp://hdl.handle.net/11449/19973010.1080/2150704X.2019.16923832-s2.0-85075729612118419553681480629857711025053300000-0003-0516-0567Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengRemote Sensing Lettersinfo:eu-repo/semantics/openAccess2024-06-18T15:01:26Zoai:repositorio.unesp.br:11449/199730Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-06-18T15:01:26Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Improving the empirical line method applied to hyperspectral inland water images by combining reference targets and in situ water measurements |
title |
Improving the empirical line method applied to hyperspectral inland water images by combining reference targets and in situ water measurements |
spellingShingle |
Improving the empirical line method applied to hyperspectral inland water images by combining reference targets and in situ water measurements do Carmo, Alisson Fernando Coelho [UNESP] |
title_short |
Improving the empirical line method applied to hyperspectral inland water images by combining reference targets and in situ water measurements |
title_full |
Improving the empirical line method applied to hyperspectral inland water images by combining reference targets and in situ water measurements |
title_fullStr |
Improving the empirical line method applied to hyperspectral inland water images by combining reference targets and in situ water measurements |
title_full_unstemmed |
Improving the empirical line method applied to hyperspectral inland water images by combining reference targets and in situ water measurements |
title_sort |
Improving the empirical line method applied to hyperspectral inland water images by combining reference targets and in situ water measurements |
author |
do Carmo, Alisson Fernando Coelho [UNESP] |
author_facet |
do Carmo, Alisson Fernando Coelho [UNESP] Bernardo, Nariane Marselhe Ribeiro [UNESP] Imai, Nilton Nobuhiro [UNESP] Shimabukuro, Milton Hirokazu [UNESP] |
author_role |
author |
author2 |
Bernardo, Nariane Marselhe Ribeiro [UNESP] Imai, Nilton Nobuhiro [UNESP] Shimabukuro, Milton Hirokazu [UNESP] |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
do Carmo, Alisson Fernando Coelho [UNESP] Bernardo, Nariane Marselhe Ribeiro [UNESP] Imai, Nilton Nobuhiro [UNESP] Shimabukuro, Milton Hirokazu [UNESP] |
description |
Empirical line methods are frequently used to correct images from remote sensing. This method is performed in two steps: the first stage finds the calibration equation representing the data interval and the second step transforms the image data into the quantity established from the equation. Several works have been successfully applied empirical lines over land areas, but it is still a great challenge to correct images from waterbodies. Remote sensing of aquatic environments captures only a small amount of energy because the water absorbs much of it. The response signal of the water is smaller than the signal from other land surface targets. This work presents a new approach to calibrate empirical lines combining reference panels with a water point. For this purpose, we evaluated several combinations of targets using both linear and exponential fit. The best matching was provided with an exponential fit using a single grey reference panel combined with a water point resulting in a coefficient of determination about 0.87, a root-mean-squared error of 0.002 sr−1 and a mean absolute percentage error of 18%. This approach presented suitable results to derive reflectance data from the raw digital numbers from images. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-12-12T01:47:47Z 2020-12-12T01:47:47Z 2020-02-01 |
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/10.1080/2150704X.2019.1692383 Remote Sensing Letters, v. 11, n. 2, p. 186-194, 2020. 2150-7058 2150-704X http://hdl.handle.net/11449/199730 10.1080/2150704X.2019.1692383 2-s2.0-85075729612 1184195536814806 2985771102505330 0000-0003-0516-0567 |
url |
http://dx.doi.org/10.1080/2150704X.2019.1692383 http://hdl.handle.net/11449/199730 |
identifier_str_mv |
Remote Sensing Letters, v. 11, n. 2, p. 186-194, 2020. 2150-7058 2150-704X 10.1080/2150704X.2019.1692383 2-s2.0-85075729612 1184195536814806 2985771102505330 0000-0003-0516-0567 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Remote Sensing Letters |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
186-194 |
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_ |
1803649634557493248 |