@Article{SotoVegaCoFeOrAlHeRo:2021:UnDoAd,
               author = "Soto Vega, Pedro Juan and Costa, Gilson Alexandre Ostwald Pedro da 
                         and Feitosa, Raul Queiroz and Ortega Adarme, Mabel Ximena and 
                         Almeida, Cl{\'a}udio Aparecido de and Heipke, Christian and 
                         Rottensteiner, Franz",
          affiliation = "{Universidade Federal do Rio de Janeiro (UFRJ)} and {Universidade 
                         do Estado do Rio de Janeiro (UERJ)} and {Universidade Federal do 
                         Rio de Janeiro (UFRJ)} and {Universidade Federal do Rio de Janeiro 
                         (UFRJ)} and {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Leibniz Universitat Hannover (LUH)} and {Leibniz Universitat 
                         Hannover (LUH)}",
                title = "An unsupervised domain adaptation approach for change detection 
                         and its application to deforestation mapping in tropical biomes",
              journal = "ISPRS Journal of Photogrammetry and Remote Sensing",
                 year = "2021",
               volume = "181",
                pages = "113--128",
                month = "Nov.",
             keywords = "Change detection, CycleGAN, Deep learning, Deforestation 
                         detection, Domain adaptation, Remote sensing.",
             abstract = "Changes in environmental conditions, geographical variability and 
                         different sensor properties typically make it almost impossible to 
                         employ previously trained classifiers for new data without a 
                         significant drop in classification accuracy. Domain adaptation 
                         (DA) techniques been proven useful to alleviate that problem. In 
                         particular, appearance adaptation techniques may be used to adapt 
                         images from a specific dataset in such a way that the generated 
                         images have a style that is similar to the images from another 
                         dataset. Such techniques are, however, prone to creating artifacts 
                         that hinder proper classification of the adapted images. In this 
                         work we propose an unsupervised DA approach for change detection 
                         tasks, which is based on a particular appearance adaptation 
                         method: the Cycle-Consistent Generative Adversarial Network 
                         (CycleGAN). Specifically, we extend that method by introducing 
                         additional constraints in the training phase of the model 
                         components, which make it preserve the semantic structure and 
                         class transitions in the adapted images. We evaluate the proposed 
                         approach on a deforestation detection application, considering 
                         different sites in the Amazon rain-forest and in the Brazilian 
                         Cerrado (savanna) using Landsat-8 images. In the experiments, each 
                         site corresponds to a domain, and the accuracy of a classifier 
                         trained with images and references from one (source) domain is 
                         measured in the classification of another (target) domain. The 
                         results show that the proposed approach is successful in producing 
                         artifact-free adapted images, which can be satisfactory classified 
                         by the pre-trained source classifiers. On average, the accuracies 
                         achieved in the classification of the adapted images outperformed 
                         the baselines (when no adaptation was made) by 7.1% in terms of 
                         mean average precision, and 9.1% in terms of F1-Score. To the best 
                         of our knowledge, the proposed method is the first unsupervised 
                         domain adaptation approach devised for change detection.",
                  doi = "10.1016/j.isprsjprs.2021.08.026",
                  url = "http://dx.doi.org/10.1016/j.isprsjprs.2021.08.026",
                 issn = "0924-2716",
             language = "en",
           targetfile = "vega_unsupervised.pdf",
        urlaccessdate = "12 maio 2024"
}