@AudiovisualMaterial{PachecoMaSiSoShEs:2021:ImClMe,
             abstract = "This paper aims to test different methods for image classification 
                         focusing on small-scale agriculture in the region of Mocajuba and 
                         Camet{\'a}, municipalities in the Northeast of Par{\'a} state, 
                         Brazil. It is an important land use class, always ignored by 
                         Land-Use and Land-Cover monitoring systems because of its small 
                         size and variable spectral signature. We used an image from the 
                         PlanetScope Surface Reflectance Mosaics (Analysis Ready) with 
                         spatial resolution of 4.77 meters and 4 spectral bands (red, 
                         green, blue and infra-red). After proceeding with a 
                         multiresolution segmentation to identify image objects, two 
                         object-oriented classification algorithms were tested: Adapted 
                         Nearest-neighbor and C5.0 Decision trees algorithms. We selected 
                         122 random points using the images available on Google Earth Pro 
                         as reference to assess the accuracy of classifications. 
                         Afterwards, confusion matrices were generated. Both methods showed 
                         similar overall accuracy and kappa value. However, C5.0 Decision 
                         trees reached a higher producers accuracy to small-scale 
                         agriculture (75%) in comparison to Adapted Nearest-neighbor (65%). 
                         The average size of the small-scale agriculture segments estimated 
                         was less than 1 ha in both maps, showing the need to carry out 
                         studies on scales of greater detail, preferably with images of 
                         high spatial resolution to represent these systems properly. In 
                         this study, C5.0 Decision trees had the best result, representing 
                         the most suitable method for mapping small-scale agriculture in 
                         Brazilian Amazon.",
          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 {Instituto Nacional de Pesquisas Espaciais (INPE)}",
               author = "Pacheco, Fl{\'a}via Domingos and Matias, Ma{\'{\i}}ra Ramalho 
                         and Silva, Gabriel M{\'a}ximo da and Souza, Anielli Rosane de and 
                         Shimabukuro, Yosio Edemir and Escada, Maria Isabel Sobral",
                 city = "Nice, France",
       conferencename = "International Conference on Advanced Geographic Information 
                         Systems, Applications, and Services, 13 (GEOProcessing)",
                 date = "19-22 july",
                 isbn = "978-1-61208-871-6",
                 issn = "2308-393X",
             keywords = "digital image processing, segmentation, land use, land cover, 
                         smallholders, planetscope.",
             language = "en",
            publisher = "IARIA",
     publisheraddress = "S{\~a}o Jos{\'e} dos Campos",
           targetfile = "30034_GEOProcessing2021.pdf",
                title = "Image Classification Methods Assessment for Identification of 
                         Small-Scale Agriculture in Brazilian Amazon",
                 year = "2021",
        urlaccessdate = "03 maio 2024"
}