@Article{MartinezAdTuCoAlFe:2022:CoClRe,
               author = "Martinez, J. A. C. and Adarme, M. X. O. and Turnes, J. N. and 
                         Costa, Gilson A. O. P. and Almeida, Claudio Aparecido de and 
                         Feitosa, Raul Q.",
          affiliation = "{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 {University of Waterloo} and 
                         {Universidade do Estado do Rio de Janeiro (UERJ)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Pontif{\'{\i}}cia 
                         Universidade Cat{\'o}lica do Rio de Janeiro (PUC-Rio)}",
                title = "A comparison of cloud removal methods for deforestation monitoring 
                         in Amazon rainforest",
              journal = "International Archives of the Photogrammetry, Remote Sensing and 
                         Spatial Information Sciences",
                 year = "2022",
               volume = "43",
               number = "B3",
                pages = "665--671",
                month = "June",
             keywords = "Cloud Removal, Deep learning, Deforestation, Optical imagery, 
                         SAR-optical Data fusion.",
             abstract = "Deforestation in tropical rainforests is a major source of carbon 
                         dioxide emissions, an important driver of climate change. For 
                         decades, the Brazilian government has maintained monitoring 
                         programs for deforestation detection in the Brazilian Legal Amazon 
                         area based on remotely sensed optical images in a protocol that 
                         involves considerable efforts of visual interpretation. However, 
                         the Amazon region is covered with clouds for most of the year, and 
                         deforestation assessment can rely only on images acquired in the 
                         dry season when cloud-free images are more likely to capture. One 
                         possibility to lessen that restriction and enable deforestation 
                         detection throughout the year is to synthesize cloud-free optical 
                         images from corresponding SAR images, which are only marginally 
                         influenced by atmospheric conditions. This work compares a set of 
                         such image synthesis methods, considering deforestation detection 
                         in the Amazon forest as the target application. Specifically, we 
                         evaluate three deep learning methods for cloud removal in 
                         Sentinel-2 images: a conditional Generative Adversarial Network 
                         (cGAN) based on the pix2pixi architecture; an extension of that 
                         method, which uses atrous convolutions (Atrous cGANi) to enhance 
                         fine image details; and a non-generative method (DSen2-CRi) based 
                         on residual networks. In the evaluation, we assess both the 
                         quality of the generated images and the accuracy obtained when 
                         performing deforestation detection from those images. We further 
                         compare those methods with an image aggregation tool available in 
                         Google Earth Engine (GEE Tooli), which creates cloud-free mosaics 
                         from sequences of images acquired at nearby dates. In this study, 
                         we considered two sites in the Brazilian Amazon, characterized by 
                         distinct vegetation and deforestation patterns. In terms of the 
                         quality metrics and classification accuracy, the Atrous cGANi was 
                         the best performing deep learning method. The GEE Tooli 
                         outperformed all those methods when dealing with images from the 
                         dry season but turned out to be the poorest performing method in 
                         the wet season.",
                  doi = "10.5194/isprs-archives-XLIII-B3-2022-665-2022",
                  url = "http://dx.doi.org/10.5194/isprs-archives-XLIII-B3-2022-665-2022",
                 issn = "1682-1750",
             language = "en",
           targetfile = "isprs-archives-XLIII-B3-2022-665-2022.pdf",
        urlaccessdate = "25 jun. 2024"
}