@InProceedings{MarujoFonKorSanBen:2017:CBAuDe,
               author = "Marujo, Rennan de Freitas Bezerra and Fonseca, Leila Maria Garcia 
                         and Korting, Thales Sehn and Santos, Rafael Duarte Coelho dos and 
                         Bendini, Hugo do Nascimento",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)}",
                title = "CBERS-4/MUX automatic detection of clouds and cloud shadows using 
                         decision trees",
            booktitle = "Anais...",
                 year = "2017",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "6328--6335",
         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 = "Cloud contamination can compromise surface observation on 
                         satellite images and impossibility land cover and land use 
                         mapping, due to their high re\flectance. Similarly, cloud 
                         shadows 
                         candarkentheimageorbeconfusedwithwater,makingithardertodifferentiatetargets. 
                         Thispaperaims at evaluating an automatic cloud and cloud shadow 
                         detection method using decision tree classi\fier for 
                         CBERS-4 (China Brazil Earth Resources Satellite) MUX 
                         (Multispectral Camera) camera. In relation to the features used in 
                         the classi\fication process, 3 methods were tested to 
                         classify 10 images of CBERS-4 MUXcamera. The\first 
                         oneusedspectral informationandspectralindices, 
                         suchasNDVI,WIandHOT; the second one added shape attributes in the 
                         feature set, and the third one added texture attributes. The 
                         classi\fication process considered 3 classes: cloud, cloud 
                         shadow and cloud-free, which were validated using visually 
                         interpreted images. The results presented an overall accuracy of 
                         about 92.98%. The accuracy for the cloud detection was 0.91, while 
                         for the cloud shadow the classi\fication accuracy was 0.67. 
                         These results point out that for sensors that has only visible and 
                         near infrared spectral bands, like CBERS-4/MUX, the NDVI, WI and 
                         HOT spectral indices are relevant for cloud detection. On the 
                         other hand, for cloud shadow detection it is necessary to explore 
                         other features capable to discriminate it from dark objects in the 
                         images.",
  conference-location = "Santos",
      conference-year = "28-31 maio 2017",
                 isbn = "978-85-17-00088-1",
                label = "59995",
             language = "en",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3PSMCMH",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3PSMCMH",
           targetfile = "59995.pdf",
                 type = "Classifica{\c{c}}{\~a}o e minera{\c{c}}{\~a}o de dados",
        urlaccessdate = "24 abr. 2024"
}