@InProceedings{SoaresAbrSouPerMed:2011:AvViCl,
               author = "Soares, Jonnas Gon{\c{c}}alves and Abreu, Marcos Vinicius Sanches 
                         and Souza, Wi{\'e}ner Anselmo de Medeiros and Pereira, Isaias da 
                         Silva and Medeiros, Nilcilene das Gra{\c{c}}as",
          affiliation = "{Universidade Federal de Vi{\c{c}}osa – UFV} and {Universidade 
                         Federal de Vi{\c{c}}osa – UFV} and {Universidade Federal de 
                         Vi{\c{c}}osa – UFV} and {Universidade Federal de Vi{\c{c}}osa – 
                         UFV} and {Universidade Federal de Vi{\c{c}}osa – UFV}",
                title = "Avalia{\c{c}}{\~a}o da viabilidade de classifica{\c{c}}{\~a}o 
                         de imagens fusionadas pelo uso de m{\'e}todo de an{\'a}lise 
                         estat{\'{\i}}stica",
            booktitle = "Anais...",
                 year = "2011",
               editor = "Epiphanio, Jos{\'e} Carlos Neves and Galv{\~a}o, L{\^e}nio 
                         Soares",
                pages = "2636",
         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 = "Pan sharpening, classification, statistical, fus{\~a}o, 
                         classifica{\c{c}}{\~a}o, estat{\'{\i}}stica.",
             abstract = "Pan sharpening allows obtaining high spectral and spatial 
                         resolution images. The use of pan sharpened images is growing in 
                         many remote sensing applications. One of these applications is 
                         image classification, that use samples of spectral response 
                         contained in images. In pan sharpened images, the gray levels are 
                         generated from the images by mathematical process, and may not 
                         match the original spectral responses. Were statistically compared 
                         classified images of the city of Teresina, Piau{\'{\i}}, 
                         generated by the CBERS 2-B CCD sensor, and the second created 
                         combining the CCD image with the HRC image of the same satellite 
                         by the substitution method RGB-IHS. Both were classified by 
                         Maximum Likelihood method. Classification quality was evaluated by 
                         Kappa coefficient. The images were compared. The correlation 
                         matrix between the CCD and pan sharpened images bands was 
                         obtained. Among these images, were compared the means and 
                         variances of the pixels that belong to the classes in each band 
                         counterparts. The test used to compare was the z test, with 95% 
                         confidence level. Classifications were satisfactory by Kappa 
                         coefficient (Kappa of 0.80 for the CCD image and 0.74 for pan 
                         sharpened). But statistical analysis shows differences between 
                         them. With the exception of band 1, there was low correlation 
                         between bands, and there was incompatibility between all classes. 
                         This indicates the need for caution in classifying pan sharpened 
                         images, because pan-sharpening may modify the spectral response.",
  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/3A59C78",
                  url = "http://urlib.net/ibi/3ERPFQRTRW/3A59C78",
           targetfile = "p1208.pdf",
                 type = "Processamento de Imagens",
        urlaccessdate = "15 jun. 2024"
}