Skip navigation
Use este identificador para citar ou linkar para este item: http://repositorio.unb.br/handle/10482/43601
Arquivos associados a este item:
Arquivo Descrição TamanhoFormato 
ARTIGO_ChangeDetectionDeforestation.pdf5,27 MBAdobe PDFVisualizar/Abrir
Registro completo de metadados
Campo DCValorIdioma
dc.contributor.authorBem, Pablo Pozzobon de-
dc.contributor.authorCarvalho Júnior, Osmar Abílio de-
dc.contributor.authorGuimarães, Renato Fontes-
dc.contributor.authorGomes, Roberto Arnaldo Trancoso-
dc.date.accessioned2022-05-03T13:23:39Z-
dc.date.available2022-05-03T13:23:39Z-
dc.date.issued2020-03-11-
dc.identifier.citationBEM, Pablo Pozzobon de et al. Change detection of deforestation in the Brazilian Amazon using landsat data and convolutional neural networks. Remote Sensing, v. 12, n. 6, 901, 2020. DOI: https://doi.org/10.3390/rs12060901. Disponível em: https://www.mdpi.com/2072-4292/12/6/901. Acesso em: 03 maio 2022.pt_BR
dc.identifier.urihttps://repositorio.unb.br/handle/10482/43601-
dc.language.isoInglêspt_BR
dc.publisherMDPIpt_BR
dc.rightsAcesso Abertopt_BR
dc.titleChange detection of deforestation in the Brazilian Amazon using landsat data and convolutional neural networkspt_BR
dc.typeArtigopt_BR
dc.subject.keywordAprendizagem profundapt_BR
dc.subject.keywordRedes neurais (Computação)pt_BR
dc.subject.keywordClassificaçãopt_BR
dc.subject.keywordDetecção de mudançapt_BR
dc.subject.keywordDesmatamentopt_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.identifier.doihttps://doi.org/10.3390/rs12060901pt_BR
dc.description.abstract1Mapping deforestation is an essential step in the process of managing tropical rainforests. It lets us understand and monitor both legal and illegal deforestation and its implications, which include the effect deforestation may have on climate change through greenhouse gas emissions. Given that there is ample room for improvements when it comes to mapping deforestation using satellite imagery, in this study, we aimed to test and evaluate the use of algorithms belonging to the growing field of deep learning (DL), particularly convolutional neural networks (CNNs), to this end. Although studies have been using DL algorithms for a variety of remote sensing tasks for the past few years, they are still relatively unexplored for deforestation mapping. We attempted to map the deforestation between images approximately one year apart, specifically between 2017 and 2018 and between 2018 and 2019. Three CNN architectures that are available in the literature—SharpMask, U-Net, and ResUnet—were used to classify the change between years and were then compared to two classic machine learning (ML) algorithms—random forest (RF) and multilayer perceptron (MLP)—as points of reference. After validation, we found that the DL models were better in most performance metrics including the Kappa index, F1 score, and mean intersection over union (mIoU) measure, while the ResUnet model achieved the best overall results with a value of 0.94 in all three measures in both time sequences. Visually, the DL models also provided classifications with better defined deforestation patches and did not need any sort of post-processing to remove noise, unlike the ML models, which needed some noise removal to improve 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/ 0000-0002-9555-043Xpt_BR
Aparece nas coleções:Artigos publicados em periódicos e afins

Mostrar registro simples do item Visualizar estatísticas



Os itens no repositório estão protegidos por copyright, com todos os direitos reservados, salvo quando é indicado o contrário.