@Article{KuckSiSaBiShSi:2021:ChDeSe,
               author = "Kuck, Tahisa Neitzel and Silva Filho, Paulo Fernando Ferreira and 
                         Sano, Edson Eyji and Bispo, Popyanna da Concei{\c{c}}{\~a}o and 
                         Shiguemori, Elcio Hideiti and Silva, Ricardo Dal'Agnol da",
          affiliation = "{Instituto de Estudos Avan{\c{c}}ados (IEAv)} and {Instituto de 
                         Estudos Avan{\c{c}}ados (IEAv)} and {Universidade de 
                         Bras{\'{\i}}lia (UnB)} and {University of Manchester} and 
                         {Instituto de Estudos Avan{\c{c}}ados (IEAv)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)}",
                title = "Change detection of selective logging in the brazilian amazon 
                         using x-band sar data and pre-trained convolutional neural 
                         networks",
              journal = "Remote Sensing",
                 year = "2021",
               volume = "13",
               number = "23",
                pages = "e4944",
                month = "Dec.",
             keywords = "Convolutional neural networks, Selective logging, Synthetic 
                         aperture radar.",
             abstract = "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.",
                  doi = "10.3390/rs13234944",
                  url = "http://dx.doi.org/10.3390/rs13234944",
                 issn = "2072-4292",
             language = "en",
           targetfile = "remotesensing-13-04944-v2.pdf",
        urlaccessdate = "01 maio 2024"
}