@InProceedings{LacerdaShiDamAnjHab:2020:ImSePa,
               author = "Lacerda, M. G. and Shiguemori, Elcio Hideiti and Dami{\~a}o, A. 
                         J. and Anjos, C. S. and Habermann, M.",
          affiliation = "{Instituto de Estudos Avan{\c{c}}ados (IEAv)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto de Estudos 
                         Avan{\c{c}}ados (IEAv)} and {Instituto Federal de Ci{\^e}ncia e 
                         Tecnologia do Sul de Minas (IFSULDEMINAS)} and {Instituto de 
                         Estudos Avan{\c{c}}ados (IEAv)}",
                title = "Impact of segmentation parameters on the classification of VHR 
                         images acquired by RPAS",
            booktitle = "Proceedings...",
                 year = "2020",
                pages = "28--33",
         organization = "IEEE Latin American GRSS; ISPRS Remote Sensing Conference",
            publisher = "IEEE",
             keywords = "Image Classification, Segmentation Parameters, RPA, Very High 
                         Resolution Images.",
             abstract = "RPAs (Remotely Piloted Aircrafts) have been used in many Remote 
                         Sensing applications, featuring high-quality imaging sensors. In 
                         some situations, the images are interpreted in an automated 
                         fashion using object-oriented classification. In this case, the 
                         first step is segmentation. However, the setting of segmentation 
                         parameters such as scale, shape, and compactness may yield too 
                         many different segmentations, thus it is necessary to understand 
                         the influence of those parameters on the final output. This paper 
                         compares 24 segmentation parameter sets by taking into account 
                         classification scores. The results indicate that the segmentation 
                         parameters exert influence on both classification accuracy and 
                         processing time.",
  conference-location = "Santiago, Chile",
      conference-year = "21-26 Mar.",
                  doi = "10.1109/LAGIRS48042.2020.9165637",
                  url = "http://dx.doi.org/10.1109/LAGIRS48042.2020.9165637",
                 isbn = "978-172814350-7",
             language = "en",
           targetfile = "lacerda_impact.pdf",
        urlaccessdate = "28 abr. 2024"
}