@Article{FerrariFerrAlmeFeit:2023:FuSeSe,
               author = "Ferrari, Felipe and Ferreira, Matheus Pinheiro and Almeida, 
                         Cl{\'a}udio Aparecido de and Feitosa, Raul Queiroz",
          affiliation = "{Pontif{\'{\i}}cia Universidade Cat{\'o}lica do Rio de Janeiro 
                         (PUC-Rio)} and {Instituto Militar de Engenharia (IME)} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Fusing Sentinel-1 and Sentinel-2 images for deforestation 
                         detection under diverse cloud conditions",
              journal = "IEEE Geoscience and Remote Sensing Letters",
                 year = "2023",
               volume = "20",
                pages = "e2501005",
             keywords = "Adaptive optics, Biomedical optical imaging, Clouds, Land Surface, 
                         Optical Data, Optical imaging, Optical sensors, Radar polarimetry, 
                         SAR Data, Synthetic aperture radar, Vegetation.",
             abstract = "Most current early warning systems for deforestation rely on 
                         cloud-free optical images, which are difficult to obtain in 
                         tropical regions. The fusion of optical and SAR images is an 
                         attractive alternative in these cases. Although less 
                         discriminative in cloudless regions, SAR data are nearly unaltered 
                         by clouds, allowing better discrimination in cloudy areas than the 
                         optical counterpart. This letter proposes solutions that seek the 
                         best combination between the two modalities for each pixel as a 
                         function of the surrounding cloud cover to maximize classification 
                         accuracy. We compared early, joint, and late fusion variants of 
                         Fully Convolutional Networks (FCN) to detect deforestation in the 
                         Amazon rainforest from Sentinel 1 and Sentinel 2 data. Experiments 
                         conducted to compare the architecture variants showed that 
                         optical-SAR fusion might outperform the single-modal variants for 
                         deforestation detection on pixels affected by any cloud cover 
                         level. In particular, the joint fusion approach outperformed the 
                         single modal counterparts under all cloud cover scenarios.",
                  doi = "10.1109/LGRS.2023.3242430",
                  url = "http://dx.doi.org/10.1109/LGRS.2023.3242430",
                 issn = "1545-598X",
             language = "en",
           targetfile = "
                         
                         Fusing_Sentinel-1_and_Sentinel-2_Images_for_Deforestation_Detection_in_the_Brazilian_Amazon_Under_Diverse_Cloud_Conditions.pdf",
        urlaccessdate = "28 abr. 2024"
}