@Article{GirolamoNetoSaSaSiRoAl:2020:ObBaIm,
               author = "Girolamo Neto, Cesare Di and Sato, Luciane Yumie and Sanches, Ieda 
                         Del'Arco and Silva, Isabel Cristina de Oliveira and Rocha, Joana 
                         Carolina Silva and Almeida, Cl{\'a}udio Aparecido de",
          affiliation = "GIZ, Deutsche Gesellschaft f{\"u}r Internationale Zusammenarbeit 
                         and GIZ, Deutsche Gesellschaft f{\"u}r Internationale 
                         Zusammenarbeit and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)} and GIZ, Deutsche Gesellschaft f{\"u}r Internationale 
                         Zusammenarbeit and GIZ, Deutsche Gesellschaft f{\"u}r 
                         Internationale Zusammenarbeit and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)}",
                title = "Object based image analysis and texture features for pasture 
                         classification in brazilian savannah",
              journal = "ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial 
                         Information Sciences",
                 year = "2020",
               volume = "5",
               number = "3",
                pages = "453--460",
                month = "Aug.",
                 note = "2020 24th ISPRS Congress on Technical Commission III; Nice, 
                         Virtual; France; 31 August 2020 through 2 September 2020;",
             keywords = "Sentinel-2, Random Forest, Superpixel, Spectral Unmixing, 
                         Grasslands, Cerrado.",
             abstract = "The classification of different types of pasture using remote 
                         sensing imagery is still a challenge. Assessing high quality 
                         geospatial information of pasture management system and 
                         productivity are key factors for establishing local public 
                         policies related to food security. In this context, we aim to 
                         investigate how texture features, allied with Object Based Image 
                         Analysis, can contribute to the automatic classification of 
                         herbaceous pastures and shrubby pastures in a region of Brazilian 
                         Savannah. We used Sentinel-2 images from dry and rainy seasons to 
                         extract several vegetation indexes, spectral unmixing components 
                         and texture features. The SLIC algorithm was used for perform 
                         image segmentation and the Random Forest for image classification. 
                         The use of texture features on pasture classification resulted in 
                         an accuracy of 87.03%. Our key finding is that features like 
                         entropy and contrast were able to detect areas with a greater 
                         concentration of shrubby-arboreal elements, which are often 
                         present on shrubby pastures and may be the first signal of a 
                         degradation process.",
                  doi = "10.5194/isprs-Annals-V-3-2020-453-2020",
                  url = "http://dx.doi.org/10.5194/isprs-Annals-V-3-2020-453-2020",
                 issn = "0924-2716",
             language = "en",
           targetfile = "Girolamo_objec.pdf",
        urlaccessdate = "27 abr. 2024"
}