@InProceedings{SotheAlmeLiesSchi:2017:AnCoAb,
               author = "Sothe, Camile and Almeida, Cl{\'a}udia Maria de and Liesenberg, 
                         Veraldo and Schimalski, Marcos Benedito",
          affiliation = "{} and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "An{\'a}lise comparativa de abordagens para 
                         classifica{\c{c}}{\~a}o do est{\'a}dio sucessional da 
                         vegeta{\c{c}}{\~a}o de um fragmento florestal da Mata 
                         Atl{\^a}ntica",
            booktitle = "Anais...",
                 year = "2017",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "1306--1313",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 18. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "The remote classification of the different vegetation successional 
                         stages still represents a challenging task in face of the similar 
                         spectral response of such classes. This paper is committed to 
                         evaluate the performance of both Landsat 8 and RapidEye images in 
                         the classification of successional forest stages within a patch of 
                         Mixed Ombrophilous Forest located inside the S{\~a}o Joaquim 
                         National Park, Santa Catarina State, south of Brazil. Three 
                         variables dataset extracted from each image were analyzed, namely; 
                         (1) one solely consisting of the spectral bands themselves; (2) a 
                         second one comprising GLCM-based texture measures derived from the 
                         spectral bands; and (3) a third one containing these two datasets 
                         and additionally two vegetation indices obtained from the 
                         Landsat-8 image and three vegetation indices from the RapidEye 
                         image. Each dataset was subject to three classifiers: random 
                         forest (RF), support vector machine (SVM), and maximum likelihood 
                         estimation (MLE or MAXVER). Results show that Kappa coefficients 
                         ranged from 0.66 to 0.88, and both userīs and producerīs 
                         accuracies were over 50%. The best result was attained with the 
                         Landsat 8 image using the third dataset and the RF classifier. 
                         Texture measures such as mean, contrast and dissimilarity were 
                         decisive for the successful classification of both images.",
  conference-location = "Santos",
      conference-year = "28-31 maio 2017",
                 isbn = "978-85-17-00088-1",
                label = "59501",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3PS4GFH",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3PS4GFH",
           targetfile = "59501.pdf",
                 type = "Floresta e outros tipos de vegeta{\c{c}}{\~a}o",
        urlaccessdate = "27 abr. 2024"
}