@Article{SotoCFHONAH:2020:DoAdCy,
               author = "Soto, P. J. and Costa, G. A. O. P. and Feitosa, R. Q. and Happ, P. 
                         N. and Ortega, M. X. and Noa, J. and Almeida, Cl{\'a}udio 
                         Aparecido de and Heipke, C.",
          affiliation = "{Pontif{\'{\i}}cia Universidade Cat{\'o}lica do Rio de Janeiro 
                         (PUC-Rio)} and {Universidade do Estado do Rio de Janeiro (UERJ)} 
                         and {Pontif{\'{\i}}cia Universidade Cat{\'o}lica do Rio de 
                         Janeiro (PUC-Rio)} and {Pontif{\'{\i}}cia Universidade 
                         Cat{\'o}lica do Rio de Janeiro (PUC-Rio)} and 
                         {Pontif{\'{\i}}cia Universidade Cat{\'o}lica do Rio de Janeiro 
                         (PUC-Rio)} and {Pontif{\'{\i}}cia Universidade Cat{\'o}lica do 
                         Rio de Janeiro (PUC-Rio)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Leibniz Universitat Hannover}",
                title = "Domain adaptation with cyclegan for change detection in the amazon 
                         forest",
              journal = "International Archives of the Photogrammetry, Remote Sensing and 
                         Spatial Information Sciences",
                 year = "2020",
               volume = "43",
               number = "B3",
                pages = "1635--1643",
                month = "Aug.",
                 note = "{Pr{\^e}mio CAPES Elsevier 2023 - ODS 15: Vida terrestre}",
             keywords = "Remote Sensing, Change Detection, Domain Adaptation, 
                         Cycle-Consistent Generative Adversarial Networks.",
             abstract = "Deep learning classification models require large amounts of 
                         labeled training data to perform properly, but the production of 
                         reference data for most Earth observation applications is a labor 
                         intensive, costly process. In that sense, transfer learning is an 
                         option to mitigate the demand for labeled data. In many remote 
                         sensing applications, however, the accuracy of a deep 
                         learning-based classification model trained with a specific 
                         dataset drops significantly when it is tested on a different 
                         dataset, even after fine-tuning. In general, this behavior can be 
                         credited to the domain shift phenomenon. In remote sensing 
                         applications, domain shift can be associated with changes in the 
                         environmental conditions during the acquisition of new data, 
                         variations of objects appearances, geographical variability and 
                         different sensor properties, among other aspects. In recent years, 
                         deep learning-based domain adaptation techniques have been used to 
                         alleviate the domain shift problem. Recent improvements in domain 
                         adaptation technology rely on techniques based on Generative 
                         Adversarial Networks (GANs), such as the Cycle-Consistent 
                         Generative Adversarial Network (CycleGAN), which adapts images 
                         across different domains by learning nonlinear mapping functions 
                         between the domains. In this work, we exploit the CycleGAN 
                         approach for domain adaptation in a particular change detection 
                         application, namely, deforestation detection in the Amazon forest. 
                         Experimental results indicate th.",
                  doi = "10.5194/isprs-archives-XLIII-B3-2020-1635-2020",
                  url = "http://dx.doi.org/10.5194/isprs-archives-XLIII-B3-2020-1635-2020",
                 issn = "1682-1750",
             language = "en",
           targetfile = "soto_domain.pdf",
        urlaccessdate = "27 abr. 2024"
}