@InProceedings{ColtriCoSoRoZuTrTr:2011:ClÁrCa,
               author = "Coltri, Priscila Pereira and Cordeiro, Robson Leonardo Ferreira 
                         and Souza, Tamires Tessarolli de and Romani, Luciana Alvim Santos 
                         and Zullo J{\'u}nior, Jurandir and Traina J{\'u}nior, Caetano 
                         and Traina, Agma Juci Machado",
          affiliation = "{Cepagri / Feagri – Unicamp} and {Universidade de S{\~a}o Paulo – 
                         USP/ICMC} and {Universidade de S{\~a}o Paulo – USP/ICMC} and 
                         {Embrapa Inform{\'a}tica Agropecu{\'a}ria} and {Cepagri / Feagri 
                         – Unicamp} and {Universidade de S{\~a}o Paulo – USP/ICMC} and 
                         {Universidade de S{\~a}o Paulo – USP/ICMC}",
                title = "Classifica{\c{c}}{\~a}o de {\'a}reas de caf{\'e} em Minas 
                         Gerais por meio do novo algoritmo QMAS em imagem espectral 
                         Geoeye-1",
            booktitle = "Anais...",
                 year = "2011",
               editor = "Epiphanio, Jos{\'e} Carlos Neves and Galv{\~a}o, L{\^e}nio 
                         Soares",
                pages = "539--546",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 15. (SBSR).",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             keywords = "coffee crops, spectral pattern, QMAS classification algorithm, 
                         cafeicultura, padr{\~a}o espectral, algoritmo de 
                         classifica{\c{c}}{\~a}o QMAS.",
             abstract = "Although there exist different image processing techniques that 
                         can be used to discover knowledge from satellite images of coffee 
                         crops, there are still many issues to be addressed. One of them is 
                         that automatic image classification techniques usually have 
                         problems to recognize patterns from images of coffee crops, due to 
                         their spatial variability and planting characteristics. In this 
                         context, we present a comparison of two different methods for the 
                         task of classifying a Geoye-1 image of coffee fields from the 
                         South of the state of Minas Gerais, in Brazil. The compared 
                         methods are: QMAS, a new algorithm for image classification, and 
                         MAXVER, a traditional method commonly used by agronomists to 
                         classify satellite images. The overall statistical results were 
                         reasonable for the traditional MAXVER method. Nevertheless, it has 
                         presented 30% in average of misclassification between the classes: 
                         Coffee and Forest. The majority of the areas in which the 
                         misclassification occurred refer to the middle of the coffee 
                         field, which complicates the process of post-classification. On 
                         the other hand, the QMAS algorithm presented better results, being 
                         more efficient especially for the coffee classification, since it 
                         did not present classificatory confusion in the middle of the 
                         coffee area. Between all the coffee fields classified by QMAS, 
                         only one was wrongly recognized as forest. In addition, the QMAS 
                         method was able to classify a forest fragment in the middle of the 
                         coffee plantation. Thus, we conclude that the QMAS algorithm is a 
                         viable alternative for the classification of remote sensing images 
                         from coffee producing regions.",
  conference-location = "Curitiba",
      conference-year = "30 abr. - 5 maio 2011",
                 isbn = "{978-85-17-00056-0 (Internet)} and {978-85-17-00057-7 (DVD)}",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "3ERPFQRTRW/3A22M2S",
                  url = "http://urlib.net/ibi/3ERPFQRTRW/3A22M2S",
           targetfile = "p0993.pdf",
                 type = "Agricultura",
        urlaccessdate = "18 jun. 2024"
}