{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T14:03:48Z","timestamp":1774533828041,"version":"3.50.1"},"reference-count":53,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2022,7,8]],"date-time":"2022-07-08T00:00:00Z","timestamp":1657238400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico (CNPq)"},{"name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior (CAPES)"},{"name":"Funda\u00e7\u00e3o Carlos Chagas Filho de Amparo \u00e0 Pesquisa do Estado do Rio de Janeiro (FAPERJ)"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Detecting early deforestation is a fundamental process in reducing forest degradation and carbon emissions. With this procedure, it is possible to monitor and control illegal activities associated with deforestation. Most regular monitoring projects have been recently proposed, but most of them rely on optical imagery. In addition, these data are seriously restricted by cloud coverage, especially in tropical environments. In this regard, Synthetic Aperture Radar (SAR) is an attractive alternative that can fill this observational gap. This work evaluated and compared a conventional method based on time series and a Fully Convolutional Network (FCN) with bi-temporal SAR images. These approaches were assessed in two regions of the Brazilian Amazon to detect deforestation between 2019 and 2020. Different pre-processing techniques, including filtering and stabilization stages, were applied to the C-band Sentinel-1 images. Furthermore, this study proposes to provide the network with the distance map to past-deforestation as additional information to the pair of images being compared. In our experiments, this proposal brought up to 4% improvement in average precision. The experimental results further indicated a clear superiority of the DL approach over a time series-based deforestation detection method used as a baseline in all experiments. Finally, the study proved the benefits of pre-processing techniques when using detection methods based on time series. On the contrary, the analysis revealed that the neural network could eliminate noise from the input images, making filtering innocuous and, therefore, unnecessary. On the other hand, the stabilization of the input images brought non-negligible accuracy gains to the DL approach.<\/jats:p>","DOI":"10.3390\/rs14143290","type":"journal-article","created":{"date-parts":[[2022,7,11]],"date-time":"2022-07-11T00:06:21Z","timestamp":1657497981000},"page":"3290","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Improving Deforestation Detection on Tropical Rainforests Using Sentinel-1 Data and Convolutional Neural Networks"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4106-0291","authenticated-orcid":false,"given":"Mabel","family":"Ortega Adarme","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro 22451-900, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2573-3783","authenticated-orcid":false,"given":"Juan","family":"Doblas Prieto","sequence":"additional","affiliation":[{"name":"National Institute for Space Research (INPE), S\u00e3o Jose dos Campos, S\u00e3o Paulo 12227-010, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8344-5096","authenticated-orcid":false,"given":"Raul","family":"Queiroz Feitosa","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro 22451-900, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1032-6966","authenticated-orcid":false,"given":"Cl\u00e1udio Aparecido","family":"De Almeida","sequence":"additional","affiliation":[{"name":"National Institute for Space Research (INPE), S\u00e3o Jose dos Campos, S\u00e3o Paulo 12227-010, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"10759","DOI":"10.1073\/pnas.1605516113","article-title":"Land-use and climate change risks in the Amazon and the need of a novel sustainable development paradigm","volume":"113","author":"Nobre","year":"2016","journal-title":"Proc. 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