@Article{MarujoFonsKörtBend:2020:MuSeAp,
               author = "Marujo, Rennan de Freitas Bezerra and Fonseca, Leila Maria Garcia 
                         and K{\"o}rting, Thales Sehn and Bendini, Hugo do Nascimento",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)}",
                title = "A multi-scale segmentation approach to filling gaps in Landsat 
                         ETM+ SLC-off images through pixel weighting",
              journal = "International Archives of the Photogrammetry and Remote Sensing",
                 year = "2020",
               volume = "42",
               number = "3",
                pages = "79--84",
                month = "Feb.",
                 note = "Joint Meeting of the 21st William T. Pecora Memorial Remote 
                         Sensing Symposium, Pecora 2019 and the 38th International 
                         Symposium on Remote Sensing of Environment, ISRSE 2019; Baltimore; 
                         United States; 6 October 2019 through 11 October 2019.",
             keywords = "Landsat, SLC-off, Segmentation, Gap-filling, Image processing.",
             abstract = "Monitoring changes on Earths surface is a difficult task commonly 
                         performed using multi-spectral remote sensing images. The absence 
                         of surface information in optical images due to the presence of 
                         cloud, low temporal resolution and sensors defects interfere in 
                         analyses. In this context, we present an approach for filling gaps 
                         in imagery mainly caused by small clouds and sensor defects. Our 
                         method consists of an adaptation from an existing method that uses 
                         spatial context of close-in-time images through the use of the 
                         most frequent value obtained using multiscale segmentation. Our 
                         method uses the pixel proportion contained in each segment to fill 
                         missing values. We applied the gap-filling methodology on three 
                         dates containing simulated images from Landsat7 using Landsat8 
                         images. We validated the method by introducing and filling 
                         artificial gaps, and comparing the original data with model 
                         predictions. The developed approach surpassed Maxwell et al. 
                         (2007) gap-filling method for all bands, presenting a minimal R 2 
                         of 0.78. Our method proved to enhance the Maxwell et al. (2007) 
                         gap-filling method, while also asymptotically maintaining the 
                         algorithm cost. It also allowed image texture to be conserved on 
                         reconstructed images. This characteristic enables narrow features, 
                         e.g., as roads, riparian areas, and small streams capable of being 
                         detected on the filled images. Based on that, further object-based 
                         approaches can be used on images filled using this methodology, 
                         demonstrating its capacity to estimate Earths surface data.",
                  doi = "10.5194/isprs-archives-XLII-3-W11-79-2020",
                  url = "http://dx.doi.org/10.5194/isprs-archives-XLII-3-W11-79-2020",
                 issn = "0256-1840",
             language = "en",
           targetfile = "marujo_multi.pdf",
        urlaccessdate = "28 abr. 2024"
}