@Article{BottinoCeba:2015:DaClCl,
               author = "Bottino, Marcus Jorge and Ceballos, Juan Carlos",
          affiliation = "CEMADEN and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Daytime cloud classification over South American region using 
                         multispectral GOES-8 imagery",
              journal = "International Journal of Remote Sensing",
                 year = "2015",
               volume = "36",
               number = "1",
                pages = "1--19",
             keywords = "Factor analysis, Geostationary satellites, Luminance, 
                         Microcomputers, Multivariant analysis, Reflection, Satellite 
                         imagery, Temperature, Textures Brightness temperatures, 
                         Classification scheme, Cloud classification, Euclidean distance, 
                         Geostationary operational environmental satellites, Multi-spectral 
                         imagery, Principal Components, Temperature differences.",
             abstract = "Information about a minimal set of characteristic variables and 
                         types of scene was searched for Geostationary Operational 
                         Environmental Satellite (GOES)-8 Imager multispectral imagery over 
                         an extended area of South America, September 2002, 1600 UTC (near 
                         local noon over most parts of the area). Thirteen variables were 
                         considered for each pixel: five of them described reflectance and 
                         brightness temperature in four channels, three variables assessed 
                         temperature difference related to channel 4; finally, five 
                         variables assessed local homogeneity (texture) in each channel. 
                         Thirty-two clusters were determined by a classification scheme 
                         (dynamic cluster) based on minimal Euclidean distance. Factor 
                         analysis in principal components (PCs) applied to cluster 
                         centroids shows that only five variables might be taken as 
                         non-redundant, namely reflectance in channel 1 and brightness 
                         temperature in channel 4 as well as their textures, together with 
                         difference between channels 5 and 4. Although factor analysis 
                         suggests defining about seven clusters (which in turn are 
                         consistent with image nephanalysis), PC analysis makes it evident 
                         that an objective minimal number of scenes is actually loosely 
                         defined but provides some useful criteria for definition of proper 
                         centroids, depending on the users convenience.",
                  doi = "10.1080/01431161.2014.978953",
                  url = "http://dx.doi.org/10.1080/01431161.2014.978953",
                 issn = "0143-1161",
                label = "self-archiving-INPE-MCTI-GOV-BR",
             language = "en",
        urlaccessdate = "27 abr. 2024"
}