{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T07:00:40Z","timestamp":1789110040214,"version":"build-2803163510"},"reference-count":67,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2021,12,5]],"date-time":"2021-12-05T00:00:00Z","timestamp":1638662400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>It is estimated that, in the Brazilian Amazon, forest degradation contributes three times more than deforestation for the loss of gross above-ground biomass. Degradation, in particular those caused by selective logging, result in features whose detection is a challenge to remote sensing, due to its size, space configuration, and geographical distribution. From the available remote sensing technologies, SAR data allow monitoring even during adverse atmospheric conditions. The aim of this study was to test different pre-trained models of Convolutional Neural Networks (CNNs) for change detection associated with forest degradation in bitemporal products obtained from a pair of SAR COSMO-SkyMed images acquired before and after logging in the Jamari National Forest. This area contains areas of legal and illegal logging, and to test the influence of the speckle effect on the result of this classification by applying the classification methodology on previously filtered and unfiltered images, comparing the results. A method of cluster detections was also presented, based on density-based spatial clustering of applications with noise (DBSCAN), which would make it possible, for example, to guide inspection actions and allow the calculation of the intensity of exploitation (IEX). Although the differences between the tested models were in the order of less than 5%, the tests on the RGB composition (where R = coefficient of variation; G = minimum values; and B = gradient) presented a slightly better performance compared to the others in terms of the number of correct classifications for selective logging, in particular using the model Painters (accuracy = 92%) even in the generalization tests, which presented an overall accuracy of 87%, and in the test on RGB from the unfiltered image pair (accuracy of 90%). These results indicate that multitemporal X-band SAR data have the potential for monitoring selective logging in tropical forests, especially in combination with CNN techniques.<\/jats:p>","DOI":"10.3390\/rs13234944","type":"journal-article","created":{"date-parts":[[2021,12,6]],"date-time":"2021-12-06T03:10:38Z","timestamp":1638760238000},"page":"4944","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Change Detection of Selective Logging in the Brazilian Amazon Using X-Band SAR Data and Pre-Trained Convolutional Neural Networks"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0952-7055","authenticated-orcid":false,"given":"Tahisa Neitzel","family":"Kuck","sequence":"first","affiliation":[{"name":"Command, Control, Communications, Computers, Intelligence, Surveillance and Reconnaissance Division, Institute for Advanced Studies (IEAv), S\u00e3o Jos\u00e9 dos Campos 12228-001, Brazil"},{"name":"Geoscience Institute, Universidade de Bras\u00edlia (UnB), Bras\u00edlia 70910-900, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0556-3470","authenticated-orcid":false,"given":"Paulo Fernando Ferreira","family":"Silva Filho","sequence":"additional","affiliation":[{"name":"Command, Control, Communications, Computers, Intelligence, Surveillance and Reconnaissance Division, Institute for Advanced Studies (IEAv), S\u00e3o Jos\u00e9 dos Campos 12228-001, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5760-556X","authenticated-orcid":false,"given":"Edson Eyji","family":"Sano","sequence":"additional","affiliation":[{"name":"Geoscience Institute, Universidade de Bras\u00edlia (UnB), Bras\u00edlia 70910-900, Brazil"},{"name":"Embrapa Cerrados, Planaltina 73310-970, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0247-8449","authenticated-orcid":false,"given":"Polyanna da Concei\u00e7\u00e3o","family":"Bispo","sequence":"additional","affiliation":[{"name":"Department of Geography, School of Environment, Education and Development, University of Manchester, Manchester M13 9PL, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5226-0435","authenticated-orcid":false,"given":"Elcio Hideiti","family":"Shiguemori","sequence":"additional","affiliation":[{"name":"Command, Control, Communications, Computers, Intelligence, Surveillance and Reconnaissance Division, Institute for Advanced Studies (IEAv), S\u00e3o Jos\u00e9 dos Campos 12228-001, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7151-8697","authenticated-orcid":false,"given":"Ricardo","family":"Dalagnol","sequence":"additional","affiliation":[{"name":"Department of Geography, School of Environment, Education and Development, University of Manchester, Manchester M13 9PL, UK"},{"name":"Earth Observation and Geoinformatics Division, National Institute for Space Research-INPE, S\u00e3o Jos\u00e9 dos Campos 12227-010, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,5]]},"reference":[{"key":"ref_1","unstructured":"SEEG-Brasil (2021, September 20). 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