@InProceedings{RomaniAmaGonZulSou:2013:ApTéCl,
               author = "Romani, Luciana Alvim Santos and Amaral, Bruno Ferraz do and 
                         Gon{\c{c}}alves, Renata Ribeiro do Valle and Zullo Junior, 
                         Jurandir and Sousa, Elaine Parros Machado de",
                title = "Aplica{\c{c}}{\~a}o de t{\'e}cnicas de 
                         classifica{\c{c}}{\~a}o semissupervisionada para an{\'a}lise de 
                         s{\'e}ries multitemporais de imagens de sat{\'e}lite",
            booktitle = "Anais...",
                 year = "2013",
               editor = "Epiphanio, Jos{\'e} Carlos Neves and Galv{\~a}o, L{\^e}nio 
                         Soares",
                pages = "1750--1757",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 16. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "In the last years remote sensing has become an important tool to 
                         support the agricultural crop monitoring in the whole world. In 
                         the most of studies involving agriculture specialists have applied 
                         medium and high spatial resolution satellites, preferentially. 
                         However, an important question is: can low spatial resolution 
                         satellites also be used in this monitoring task? In this context, 
                         the focus of this work is how to take advantage of the high 
                         temporal resolution which is characteristic of this kind of 
                         satellite to identify agricultural fields such as sugarcane. 
                         Accordingly, this paper proposes to apply two semi-supervisioned 
                         classification methods to classify multitemporal satellite images. 
                         Thus, we adapted the LNP (Linear Neighborhood Propagation) and 
                         HC-LGT (Hierarchical Clustering and Local Graph Transduction) 
                         methods to be employed in classification of time series extracted 
                         from NDVI-NOAA images. The training dataset was generated with 
                         time series of six (agricultural crops, sugarcane, urban areas, 
                         forest, pasture and perennial crops) different classes defined by 
                         agrometeorologists. The study area encloses state of S{\~a}o 
                         Paulo in Brazil where is cultivated large sugarcane fields. 
                         Results with both classification methods showed that time series 
                         from low spatial resolution satellites can be satisfactorily used 
                         to identify regions of agricultural fields as well as forests, 
                         pasture and urban areas. Additionally, the classification 
                         generated by both methods can also be used to identify the 
                         vegetation cover of a specific region as an initial step before 
                         applying more complex strategies.",
  conference-location = "Foz do Igua{\c{c}}u",
      conference-year = "13-18 abr. 2013",
                 isbn = "{978-85-17-00066-9 (Internet)} and {978-85-17-00065-2 (DVD)}",
                label = "1237",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "3ERPFQRTRW34M/3E7GK25",
                  url = "http://urlib.net/ibi/3ERPFQRTRW34M/3E7GK25",
           targetfile = "p1237.pdf",
                 type = "An{\'a}lise e Aplica{\c{c}}{\~a}o de Imagens Multitemporais",
        urlaccessdate = "17 jun. 2024"
}