@Article{NevesKöFoSoGiHe:2021:HiMaBr,
               author = "Neves, Alana K. and K{\"o}rting, Thales Sehn and Fonseca, Leila 
                         Maria Garcia and Soares, Anderson Reis and Girolamo Neto, Cesare 
                         Di and Heipke, Christian",
          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)} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)} and {Leibniz Universit{\"a}t Hannover}",
                title = "Hierarchical mapping of Brazilian Savanna (Cerrado) physiognomies 
                         based on deep learning",
              journal = "Journal of Applied Remote Sensing",
                 year = "2021",
               volume = "15",
               number = "04",
                pages = "e044504",
             keywords = "Savanna, Cerrado, physiognomy, semantic segmentation, spectral 
                         channels, protected area.",
             abstract = "The Brazilian Savanna, also known as Cerrado, is considered a 
                         global hotspot for biodiversity conservation. The detailed mapping 
                         of vegetation types, called physiognomies, is still a challenge 
                         due to their high spectral similarity and spatial variability. 
                         There are three major ecosystem groups (forest, savanna, and 
                         grassland), which can be hierarchically subdivided into 25 
                         detailed physiognomies, according to a well-known classification 
                         system. We used an adapted U-net architecture to process a 
                         WorldView-2 image with 2-m spatial resolution to hierarchically 
                         classify the physiognomies of a Cerrado protected area based on 
                         deep learning techniques. Several spectral channels were tested as 
                         input datasets to classify the three major ecosystem groups (first 
                         level of classification). The dataset composed of RGB bands plus 
                         2-band enhanced vegetation index (EVI2) achieved the best 
                         performance and was used to perform the hierarchical 
                         classification. In the first level of classification, the overall 
                         accuracy was 92.8%. On the other hand, for the savanna and 
                         grassland detailed physiognomies (second level of classification), 
                         86.1% and 85.0% were reached, respectively. As the first work that 
                         intended to classify Cerrado physiognomies in this level of detail 
                         using deep learning, our accuracy rates outperformed others that 
                         applied traditional machine learning algorithms for this task. © 
                         The Authors. Published by SPIE under a Creative Commons 
                         Attribution 4.0 International License. Distribution or 
                         reproduction of this work in whole or in part requires full 
                         attribution of the original publication, including its DOI. [DOI: 
                         10.1117/1.JRS.15.044504].",
                  doi = "10.1117/1.JRS.15.044504",
                  url = "http://dx.doi.org/10.1117/1.JRS.15.044504",
                 issn = "1931-3195",
                label = "lattes: 5123287769635741 3 NevesK{\"o}FoSoGiHe:2021:HiMaBr",
             language = "pt",
           targetfile = "neves_hierarchical.pdf",
                  url = "https://caps.luminad.com:8443/stockage/stock/SPIE/LDL-SPIE-JARS-210442/JARS-210442_online.pdf",
        urlaccessdate = "05 maio 2024"
}