Digital Image Classification: a Comparison of Classic Methods for Land Cover and Land Use Mapping
| Autor(a) principal: | |
|---|---|
| Data de Publicação: | 2022 |
| Outros Autores: | , , , |
| Tipo de documento: | Artigo |
| Idioma: | eng |
| Título da fonte: | Anuário do Instituto de Geociências (Online) |
| DOI: | 10.11137/1982-3908_2022_45_47481 |
| Texto Completo: | https://revistas.ufrj.br/index.php/aigeo/article/view/47481 |
Resumo: | In the classification of images for land cover and land use mapping, several methods can be applied, however, they can present different results in relation to field truth. Therefore, the objective of this work was to test techniques for classifying high spatial digital images obtained from the Google Earth Pro® platform. The images refer to a section of the Federal University of Goias, campus Samambaia Goiania - GO, Brazil. Classification tests were performed on the images obtained, using two classifiers per region and two classifiers per pixel, both available free of charge, in the Spring software of the National Institute for Space Research (INPE / Brazil). For the analysis of the quality of the classifications, the results were compared to a survey by direct method, in this case the topographic one, seeking to identify which classifier came closest to the field truth. The classification that presented the best performance and class separability was the Bhattacharya, with Global Accuracy of 0.85. Bhattacharya was also the classifier that came closest in terms of measured areas, by the topographic survey, with the areas of the “zinc roofing” use class, analyzed and calculated. |
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Digital Image Classification: a Comparison of Classic Methods for Land Cover and Land Use Mappingremote Sensing; direct method; indirect methodsIn the classification of images for land cover and land use mapping, several methods can be applied, however, they can present different results in relation to field truth. Therefore, the objective of this work was to test techniques for classifying high spatial digital images obtained from the Google Earth Pro® platform. The images refer to a section of the Federal University of Goias, campus Samambaia Goiania - GO, Brazil. Classification tests were performed on the images obtained, using two classifiers per region and two classifiers per pixel, both available free of charge, in the Spring software of the National Institute for Space Research (INPE / Brazil). For the analysis of the quality of the classifications, the results were compared to a survey by direct method, in this case the topographic one, seeking to identify which classifier came closest to the field truth. The classification that presented the best performance and class separability was the Bhattacharya, with Global Accuracy of 0.85. Bhattacharya was also the classifier that came closest in terms of measured areas, by the topographic survey, with the areas of the “zinc roofing” use class, analyzed and calculated.Universidade Federal do Rio de JaneiroSantos, Alex Mota dosCarmo, Nadyelle Curcino doNunes, Fabrizia GioppoAguiar, Larissa Andrade deSilva, Carlos Fabricio Assunção da2022-08-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://revistas.ufrj.br/index.php/aigeo/article/view/4748110.11137/1982-3908_2022_45_47481Anuário do Instituto de Geociências; Vol 45 (2022)Anuário do Instituto de Geociências; Vol 45 (2022)1982-39080101-9759reponame:Anuário do Instituto de Geociências (Online)instname:Universidade Federal do Rio de Janeiro (UFRJ)instacron:UFRJenghttps://revistas.ufrj.br/index.php/aigeo/article/view/47481/pdf/*ref*/Abdelaty, E.F.S. 2016, ‘Land use change detection and prediction using high-spatial resolution Google earth imagery and GIS techniques: a study on ElBeheira governorate’, Fourth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2016), vol. 9688, no.1, pp. 968803-1. DOI:10.1117/12.223989./*ref*/Almeida, A.S.D., Werneck, G.L. & Resendes, A.P.D.C. 2014, ‘Classificação orientada a objeto de imagens de sensoriamento remoto em estudos epidemiológicos sobre leishmaniose visceral em área urbana’, Cadernos de Saúde Pública, vol. 30, no.1, pp. 1639-53, DOI:10.1590/0102-311X00059414./*ref*/Amaral, M.V.F., Souza, A.L., Soares, V.P., Soares, C.P., Leite, H.G., Martins, S.V., Fernandes, E.I. Filho & Lana, J.M. 2009, ‘Avaliação e comparação de métodos de classificação de imagens de satélites para o mapeamento de estádios de sucessão florestal’, Revista Árvore, vol. 33, no. 3, pp. 575-82, DOI:10.1590/S0100-67622009000300019./*ref*/Duhl, T.R., Guenther, A. & Helmig, D. 2012, ‘Estimating urban vegetation cover fraction using Google Earth® images’, Journal of Land Use Science, vol. 7, no. 3, pp. 311-29, DOI:10.1080/1747423X.2011.587207./*ref*/Durán, G., Pereira, W. Filho & Kuplich, T. M. 2018, ‘Identificação espectral de materiais urbanos com a técnica mapeador de ângulo espectral (SAM) e o sensor de alta resolução espacial Geoeye-1’, Boletim Geográfico do Rio Grande do Sul, vol. 31, n.1, pp. 9-34./*ref*/Garcia, A.S., Vilela, V.M.F.N., Rizzoa, R., Westb, P., Gerberb, J.S., Engstromb, P.M. & Ballester, M.V.R. 2019, ‘Assessing land use/cover dynamics and exploring drivers in the Amazon's arc of deforestation through a hierarchical, multi-scale and multi-temporal classification approach’, Remote Sensing Applications: Society and Environment, vol. 15, no. 1, pp. 1-14, DOI:10.1016/j.rsase.2019.05.002./*ref*/Ghaffarian, S. & Ghaffarian, S. 2014, ‘Automatic building detection based on supervised classification using high resolution Google Earth images’, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XL-3. ISPRS Technical Commission III Symposium, Zurich, Switzerland, 2014, DOI:10.5194/isprsarchives-XL-3-101-2014./*ref*/Hu, Q., Wu, W., Xia, T., Yu, O., Yang, P., Li, Z. & Song, Q. 2013, ‘Exploring the use of Google Earth imagery and object-based methods in land use/cover mapping’, Remote Sensing, vol. 5, no. 11, pp. 6026-42, DOI:10.3390/rs5116026./*ref*/Huang, M., Chen, N., Du, W., Chen, Z. & Gong, J. 2018, ‘DMBLC: an indirect urban impervious Surface Area Extraction Approach by Detecting and Masking Background Land Cover on Google Earth Image’, Remote Sensing, vol. 10, no. 5, pp. 1-17, DOI:10.3390/rs10050766./*ref*/IBGE, Instituto Brasileiro de Geografia e Estatística 2013. Manual Técnico de Uso da Terra, viewed 20 September 2021, <https://biblioteca.ibge.gov.br/visualizacao/livros/liv81615.pdf >. INPE, Instituto Nacional de Pesquisas Espaciais 2001, SPRING: Tutorial de Geoprocessamento, viewed 20 September 2021, <http://www.dpi.inpe.br/spring/portugues/tutorial/index.html>./*ref*/Irons, J.R. & Petersen, G.W. 1981, ‘Texture transforms of remote sensing data’, Remote Sensing of Environment, vol. 11, no. 1, pp. 359-70. DOI:10.1016/0034-4257(81)90033-X./*ref*/Jacobson, A., Dhanota, J., Godfrey, J., Jacobson, H., Rossman, Z., Stanish, A., Walker, H. & Riggio, J. 2015, ‘A novel approach to mapping land conversion using Google Earth with an application to East Africa’, Environmental Modelling & Software, vol. 72, no. 1, pp. 1-9, DOI:10.1016/j.envsoft.2015.06.011./*ref*/Kussul, N., Lavreniuk, M., Skakun, S. & Shelestov, A. 2017, ‘Deep learning classification of land cover and crop types using remote sensing data’, IEEE Geoscience and Remote Sensing Letters, vol. 14, no. 5, pp. 778-82. DOI:10.1109/LGRS.2017.2681128./*ref*/Landis, J. & Koch, G.G. 2017, ‘The measurements of agreement for categorical data’, Biometrics, vol. 33, no. 3, pp. 159-79, DOI:10.2307/2529310./*ref*/Lee, C. & Choi, E. 2000, ‘Bayes error evaluation of the Gaussian ML classifier’. IEEE Transactions on Geoscience and Remote Sensing, vol. 38, no. 3, pp. 1471-75, DOI:10.1109/36.843045./*ref*/Li, M., Zang, S., Zhang, B., Li, S. & Wu, C. 2017, ‘A review of remote sensing image classification techniques: the role of spatio-contextual information’, European Journal of Remote Sensing, vol. 47, no. 1, pp. 389-411, DOI:10.5721/EuJRS20144723./*ref*/Llano, X.C. 2019, ‘AcATaMa - QGIS plugin for Accuracy Assessment of Thematic Maps’, version 3.16.9, AcATaMa, <https://plugins.qgis.org/plugins/AcATaMa/>./*ref*/Lu, D., Batistella, M., Li, G., Moran, E., Hetrick, S., Freitas, C.C., Dutra, L.V. & Sant’Anna, S.J.S. 2012, ‘Land use/cover classification in the Brazilian Amazon using satellite images’, Pesquisa Agropecuária Brasileira., vol. 47, no. 9, pp. 1185-1208, DOI:10.1590/S0100-204X2012000900004./*ref*/Lu, D., Hetrick, S. & Moran, E.F. 2010, ‘Land cover classification in a complex urban-rural landscape with quickbird imagery’, Photogrammetric Engineering & Remote Sensing, vol. 76, no. 10, pp. 1159-68, DOI:10.14358/PERS.76.10.1159./*ref*/Malarvizh, K., Kumar, S.V. & Porchelvan, P. 2016, ‘Use of high resolution Google Earth satellite imagery in landuse map preparation for urban related applications’, Procedia Technology, vol. 24, no.1, pp. 1835-42, DOI:10.1016/j.protcy.2016.05.231./*ref*/Mello, A.Y.I., Alves, D.S., Linhares, C.A. & Lima, F.B. 2012, ‘Avaliação de técnicas de classificação digital de imagens Landsat em diferentes padrões de cobertura da terra em Rondônia’, Revista Árvore, vol. 36, no. 3, pp. 537-47, DOI:10.1590/S0100-67622012000300016/*ref*/Oliveira, M.Z., Veronez, M.R., Turani, M. & Reinhardt, A.O. 2009, ‘Imagens do Google Earth para fins de planejamento ambiental: uma análise de exatidão para o município de São Leopoldo/RS’, Anais XIV Simpósio Brasileiro de Sensoriamento Remoto, Natal, Brasil, 2009, pp. 1835-42, <http://marte.sid.inpe.br/col/dpi.inpe.br/sbsr@80/2008/11.10.17.37/doc/1835-1842.pdf>/*ref*/Oyekola, M.A. & Adewuyi, G.K. 2018, ‘Unsupervised Classification in Land Cover Types Using Remote Sensing and GIS Techniques’, International Journal of Science and Engineering Investigations, vol. 7, no. 72, pp. 11-18, <https://www.researchgate.net/publication/326623967_Unsupervised_Classification_in_Land_Cover_Types_Using_Remote_Sensing_and_GIS_Techniques>./*ref*/Queiroz, T.B., Sousa, R.D.S., Baldin, T., Batista, F.D.J., Marchesan, J., Pedrali, L.D. & Pereira, R.S. 2017, ‘Avaliação do desempenho da classificação do uso e cobertura da terra a partir de imagens Landsat 8 e Rapideye na região central do Rio Grande do Sul’, Geociências, vol. 36, no. 3, pp. 569-78./*ref*/Samaniego, L., Bárdossy, A. & Schulz, K. 2008, ‘Supervised classification of remotely sensed imagery using a modified k-NN technique’, IEEE Transactions on Geoscience and Remote Sensing, vol. 46, no. 7, pp. 2112-25, DOI:10.1109/TGRS.2008.916629./*ref*/Santos, L.A.C. & Lima, P.E.M. 2018, ‘Comparação entre diferentes algoritmos de classificação supervisionada no mapeamento temático de uma bacia hidrográfica’, Revista TreeDimensional, ProFloresta, vol. 3, no. 5, pp. 27-41, <http://www.treedimensional.org/revista/2018a/comparacao.pdf>./*ref*/Schneider, A. 2012, ‘Monitoring land cover change in urban and peri-urban areas using dense time stacks of Landsat satellite data and a data mining approuch’, Remote Sensing of Environment, vol. 124, n. 1, pp. 689-704, DOI:10.1016/j.rse.2012.06.006./*ref*/Strahler, A.H. 1980, ‘The use of prior probabilities in maximum likelihood classification of remotely sensed data’, Remote Sensing of Environment, vol. 10, no. 2, pp. 135-63, DOI:10.1016/0034-4257(80)90011-5./*ref*/Tommaselli, A.M.G., Silva, J.F.C., Hasegawa, J.K., Galo, M. & Dal Poz, A.P. 1999, ‘Fotogrametria: aplicações a curta distância’ in M. Menguete Jr. & N. Alves (orgs), FCT 40 anos, Perfil Científico Educacional, UNESP/FCT, São Paulo, pp. 147-59, <https://www.researchgate.net/publication/267035028_FOTOGRAMETRIA_aplicacoes_a_curta_distancia>./*ref*/Wibowo, A., Salleh, K.O., Frans, F.T.R.S. & Semedi, J.M. 2016, ‘Spatial temporal land use change detection using Google Earth data’, In IOP Conference Series: Earth and Environmental Science, vol. 47, no. 1, 012031. DOI:10.1088/1755-1315/47/1/012031/*ref*/Zanetti, J., Braga, F.L.S & Duarte, D.C.O. 2017, ‘Comparação dos métodos de classificação supervisionada de imagem máxima verossimilhança, distância euclidiana, paralelepípedo e redes neurais em imagens Vant, utilizando o método de Exatidão Global, índice Kappa e o Tau’, Anais do IV Simpósio Brasileiro de Geomática – SBG, Presidente Prudente, 2017, pp. 244-50./*ref*/Zhenkui, M.A. & Redmond, R.L. 1995, ‘Tau coefficients for accuracy assessment of classification of remote sensing data’, Photogrammetric Engineering and Remote Sensing, vol. 61, no. 4, pp. 63-152, <https://www.asprs.org/wp-content/uploads/pers/1995journal/apr/1995_apr_435-439.pdf>Copyright (c) 2022 Anuário do Instituto de Geociênciashttp://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccess2022-12-28T20:46:28Zoai:www.revistas.ufrj.br:article/47481Revistahttps://revistas.ufrj.br/index.php/aigeo/indexPUBhttps://revistas.ufrj.br/index.php/aigeo/oaianuario@igeo.ufrj.br||1982-39080101-9759opendoar:2022-12-28T20:46:28Anuário do Instituto de Geociências (Online) - Universidade Federal do Rio de Janeiro (UFRJ)false |
| dc.title.none.fl_str_mv |
Digital Image Classification: a Comparison of Classic Methods for Land Cover and Land Use Mapping |
| title |
Digital Image Classification: a Comparison of Classic Methods for Land Cover and Land Use Mapping |
| spellingShingle |
Digital Image Classification: a Comparison of Classic Methods for Land Cover and Land Use Mapping Digital Image Classification: a Comparison of Classic Methods for Land Cover and Land Use Mapping Santos, Alex Mota dos remote Sensing; direct method; indirect methods Santos, Alex Mota dos remote Sensing; direct method; indirect methods |
| title_short |
Digital Image Classification: a Comparison of Classic Methods for Land Cover and Land Use Mapping |
| title_full |
Digital Image Classification: a Comparison of Classic Methods for Land Cover and Land Use Mapping |
| title_fullStr |
Digital Image Classification: a Comparison of Classic Methods for Land Cover and Land Use Mapping Digital Image Classification: a Comparison of Classic Methods for Land Cover and Land Use Mapping |
| title_full_unstemmed |
Digital Image Classification: a Comparison of Classic Methods for Land Cover and Land Use Mapping Digital Image Classification: a Comparison of Classic Methods for Land Cover and Land Use Mapping |
| title_sort |
Digital Image Classification: a Comparison of Classic Methods for Land Cover and Land Use Mapping |
| author |
Santos, Alex Mota dos |
| author_facet |
Santos, Alex Mota dos Santos, Alex Mota dos Carmo, Nadyelle Curcino do Nunes, Fabrizia Gioppo Aguiar, Larissa Andrade de Silva, Carlos Fabricio Assunção da Carmo, Nadyelle Curcino do Nunes, Fabrizia Gioppo Aguiar, Larissa Andrade de Silva, Carlos Fabricio Assunção da |
| author_role |
author |
| author2 |
Carmo, Nadyelle Curcino do Nunes, Fabrizia Gioppo Aguiar, Larissa Andrade de Silva, Carlos Fabricio Assunção da |
| author2_role |
author author author author |
| dc.contributor.none.fl_str_mv |
|
| dc.contributor.author.fl_str_mv |
Santos, Alex Mota dos Carmo, Nadyelle Curcino do Nunes, Fabrizia Gioppo Aguiar, Larissa Andrade de Silva, Carlos Fabricio Assunção da |
| dc.subject.por.fl_str_mv |
remote Sensing; direct method; indirect methods |
| topic |
remote Sensing; direct method; indirect methods |
| description |
In the classification of images for land cover and land use mapping, several methods can be applied, however, they can present different results in relation to field truth. Therefore, the objective of this work was to test techniques for classifying high spatial digital images obtained from the Google Earth Pro® platform. The images refer to a section of the Federal University of Goias, campus Samambaia Goiania - GO, Brazil. Classification tests were performed on the images obtained, using two classifiers per region and two classifiers per pixel, both available free of charge, in the Spring software of the National Institute for Space Research (INPE / Brazil). For the analysis of the quality of the classifications, the results were compared to a survey by direct method, in this case the topographic one, seeking to identify which classifier came closest to the field truth. The classification that presented the best performance and class separability was the Bhattacharya, with Global Accuracy of 0.85. Bhattacharya was also the classifier that came closest in terms of measured areas, by the topographic survey, with the areas of the “zinc roofing” use class, analyzed and calculated. |
| publishDate |
2022 |
| dc.date.none.fl_str_mv |
2022-08-12 |
| dc.type.none.fl_str_mv |
|
| dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
| format |
article |
| status_str |
publishedVersion |
| dc.identifier.uri.fl_str_mv |
https://revistas.ufrj.br/index.php/aigeo/article/view/47481 10.11137/1982-3908_2022_45_47481 |
| url |
https://revistas.ufrj.br/index.php/aigeo/article/view/47481 |
| identifier_str_mv |
10.11137/1982-3908_2022_45_47481 |
| dc.language.iso.fl_str_mv |
eng |
| language |
eng |
| dc.relation.none.fl_str_mv |
https://revistas.ufrj.br/index.php/aigeo/article/view/47481/pdf /*ref*/Abdelaty, E.F.S. 2016, ‘Land use change detection and prediction using high-spatial resolution Google earth imagery and GIS techniques: a study on ElBeheira governorate’, Fourth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2016), vol. 9688, no.1, pp. 968803-1. DOI:10.1117/12.223989. /*ref*/Almeida, A.S.D., Werneck, G.L. & Resendes, A.P.D.C. 2014, ‘Classificação orientada a objeto de imagens de sensoriamento remoto em estudos epidemiológicos sobre leishmaniose visceral em área urbana’, Cadernos de Saúde Pública, vol. 30, no.1, pp. 1639-53, DOI:10.1590/0102-311X00059414. /*ref*/Amaral, M.V.F., Souza, A.L., Soares, V.P., Soares, C.P., Leite, H.G., Martins, S.V., Fernandes, E.I. Filho & Lana, J.M. 2009, ‘Avaliação e comparação de métodos de classificação de imagens de satélites para o mapeamento de estádios de sucessão florestal’, Revista Árvore, vol. 33, no. 3, pp. 575-82, DOI:10.1590/S0100-67622009000300019. /*ref*/Duhl, T.R., Guenther, A. & Helmig, D. 2012, ‘Estimating urban vegetation cover fraction using Google Earth® images’, Journal of Land Use Science, vol. 7, no. 3, pp. 311-29, DOI:10.1080/1747423X.2011.587207. /*ref*/Durán, G., Pereira, W. Filho & Kuplich, T. M. 2018, ‘Identificação espectral de materiais urbanos com a técnica mapeador de ângulo espectral (SAM) e o sensor de alta resolução espacial Geoeye-1’, Boletim Geográfico do Rio Grande do Sul, vol. 31, n.1, pp. 9-34. /*ref*/Garcia, A.S., Vilela, V.M.F.N., Rizzoa, R., Westb, P., Gerberb, J.S., Engstromb, P.M. & Ballester, M.V.R. 2019, ‘Assessing land use/cover dynamics and exploring drivers in the Amazon's arc of deforestation through a hierarchical, multi-scale and multi-temporal classification approach’, Remote Sensing Applications: Society and Environment, vol. 15, no. 1, pp. 1-14, DOI:10.1016/j.rsase.2019.05.002. /*ref*/Ghaffarian, S. & Ghaffarian, S. 2014, ‘Automatic building detection based on supervised classification using high resolution Google Earth images’, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XL-3. ISPRS Technical Commission III Symposium, Zurich, Switzerland, 2014, DOI:10.5194/isprsarchives-XL-3-101-2014. /*ref*/Hu, Q., Wu, W., Xia, T., Yu, O., Yang, P., Li, Z. & Song, Q. 2013, ‘Exploring the use of Google Earth imagery and object-based methods in land use/cover mapping’, Remote Sensing, vol. 5, no. 11, pp. 6026-42, DOI:10.3390/rs5116026. /*ref*/Huang, M., Chen, N., Du, W., Chen, Z. & Gong, J. 2018, ‘DMBLC: an indirect urban impervious Surface Area Extraction Approach by Detecting and Masking Background Land Cover on Google Earth Image’, Remote Sensing, vol. 10, no. 5, pp. 1-17, DOI:10.3390/rs10050766. /*ref*/IBGE, Instituto Brasileiro de Geografia e Estatística 2013. Manual Técnico de Uso da Terra, viewed 20 September 2021, <https://biblioteca.ibge.gov.br/visualizacao/livros/liv81615.pdf >. INPE, Instituto Nacional de Pesquisas Espaciais 2001, SPRING: Tutorial de Geoprocessamento, viewed 20 September 2021, <http://www.dpi.inpe.br/spring/portugues/tutorial/index.html>. /*ref*/Irons, J.R. & Petersen, G.W. 1981, ‘Texture transforms of remote sensing data’, Remote Sensing of Environment, vol. 11, no. 1, pp. 359-70. DOI:10.1016/0034-4257(81)90033-X. /*ref*/Jacobson, A., Dhanota, J., Godfrey, J., Jacobson, H., Rossman, Z., Stanish, A., Walker, H. & Riggio, J. 2015, ‘A novel approach to mapping land conversion using Google Earth with an application to East Africa’, Environmental Modelling & Software, vol. 72, no. 1, pp. 1-9, DOI:10.1016/j.envsoft.2015.06.011. /*ref*/Kussul, N., Lavreniuk, M., Skakun, S. & Shelestov, A. 2017, ‘Deep learning classification of land cover and crop types using remote sensing data’, IEEE Geoscience and Remote Sensing Letters, vol. 14, no. 5, pp. 778-82. DOI:10.1109/LGRS.2017.2681128. /*ref*/Landis, J. & Koch, G.G. 2017, ‘The measurements of agreement for categorical data’, Biometrics, vol. 33, no. 3, pp. 159-79, DOI:10.2307/2529310. /*ref*/Lee, C. & Choi, E. 2000, ‘Bayes error evaluation of the Gaussian ML classifier’. 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