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dc.contributor.authorBem, Pablo Pozzobon de-
dc.contributor.authorCarvalho Júnior, Osmar Abílio de-
dc.contributor.authorCarvalho, Osmar Luiz Ferreira de-
dc.contributor.authorGomes, Roberto Arnaldo Trancoso-
dc.contributor.authorGuimarães, Renato Fontes-
dc.date.accessioned2022-05-16T11:05:28Z-
dc.date.available2022-05-16T11:05:28Z-
dc.date.issued2020-08-11-
dc.identifier.citationBEM, Pablo Pozzobon de et al. Performance analysis of deep convolutional autoencoders with different patch sizes for change detection from burnt areas. Remote Sensing, v. 12, n. 16, 2576, 2020. DOI: https://doi.org/10.3390/rs12162576. Disponível em: https://www.mdpi.com/2072-4292/12/16/2576. Acesso em: 16 maio 2022.pt_BR
dc.identifier.urihttps://repositorio.unb.br/handle/10482/43715-
dc.language.isoInglêspt_BR
dc.publisherMDPIpt_BR
dc.rightsAcesso Abertopt_BR
dc.titlePerformance analysis of deep convolutional autoencoders with different patch sizes for change detection from burnt areaspt_BR
dc.typeArtigopt_BR
dc.subject.keywordAprendizagem profundapt_BR
dc.subject.keywordRedes neurais (Computação)pt_BR
dc.subject.keywordClassificaçãopt_BR
dc.subject.keywordFogopt_BR
dc.subject.keywordImagem multitemporalpt_BR
dc.rights.license© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).pt_BR
dc.description.abstract1Fire is one of the primary sources of damages to natural environments globally. Estimates show that approximately 4 million km2 of land burns yearly. Studies have shown that such estimates often underestimate the real extent of burnt land, which highlights the need to find better, state-of-the-art methods to detect and classify these areas. This study aimed to analyze the use of deep convolutional Autoencoders in the classification of burnt areas, considering di erent sample patch sizes. A simple Autoencoder and the U-Net and ResUnet architectures were evaluated. We collected Landsat 8 OLI+ data from three scenes in four consecutive dates to detect the changes specifically in the form of burnt land. The data were sampled according to four di erent sampling strategies to evaluate possible performance changes related to sampling window sizes. The training stage used two scenes, while the validation stage used the remaining scene. The ground truth change mask was created using the Normalized Burn Ratio (NBR) spectral index through a thresholding approach. The classifications were evaluated according to the F1 index, Kappa index, and mean Intersection over Union (mIoU) value. Results have shown that the U-Net and ResUnet architectures offered the best classifications with average F1, Kappa, and mIoU values of approximately 0.96, representing excellent classification results. We have also verified that a sampling window size of 256 by 256 pixels offered the best results.pt_BR
dc.identifier.orcidhttps://orcid.org/ 0000-0003-3868-8704pt_BR
dc.identifier.orcidhttps://orcid.org/ 0000-0002-0346-1684pt_BR
dc.identifier.orcidhttps://orcid.org/ 0000-0002-5619-8525pt_BR
dc.identifier.orcidhttps://orcid.org/ 0000-0003-4724-4064pt_BR
dc.identifier.orcidhttps://orcid.org/ 0000-0002-9555-043Xpt_BR
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