@Article{MantelliNetoWangPereSobi:2020:HiCoSi,
               author = "Mantelli Neto, Sylvio Luiz and von Wangenheim, Aldo and Pereira, 
                         Enio Bueno and Sobieranski, Antonio Carlos",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Universidade Federal de Santa Catarina (UFSC)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Universidade Federal 
                         de Santa Catarina (UFSC)}",
                title = "Hierarchical color similarity metrics for step-wise application on 
                         sky monitoring surface cameras",
              journal = "Journal of Geophysical Research: Atmospheres",
                 year = "2020",
               volume = "1",
                month = "jun.",
             abstract = "Digital cameras on the surface are frequently used for monitoring 
                         atmospheric conditions. Several methods were developed to use the 
                         images for synoptic observations, cloud assessments, short term 
                         forecasting and so on. However, there are some restrictions not 
                         considered by these methods, especially when a linear camera is 
                         used to observe logarithmic ranges of atmospheric luminance. 
                         Cameras accommodate the scene to a linear scale causing 
                         distortions on pattern distributions by pixel value saturation 
                         (PVS) and drifts from its original hues. This brings on some 
                         simplifying practices commonly found in the literature to overcome 
                         these problems. But those practices result in loss of data, 
                         misinterpretation of valid pixels and restriction on the use of 
                         computer vision algorithms. The present work begins by 
                         illustrating these problems performing supervised learning for two 
                         reasons: all observation systems seek out automation of human 
                         synoptic observation in order to provide a sound mathematical 
                         modeling of the observed patterns. A new modeling paradigm is 
                         proposed to map the sky patterns to represent the existent 
                         physical atmospheric phenomena not considered by the literature. 
                         We validate the proposed method, and compared the results using 
                         1630 images against two well-established methods. A hypothesis 
                         test showed that results are compatible with currently used binary 
                         approach with advantages. Differences were due to PVS and other 
                         restrictions not considered by the methods existent on literature. 
                         Finally, the present work concludes that the new paradigm presents 
                         more meaningful results of sky patterns interpretation, allows 
                         extended daylight observation periods and uses a higher 
                         dimensional space.",
                  doi = "10.1002/essoar.10503135.1",
                  url = "http://dx.doi.org/10.1002/essoar.10503135.1",
                 issn = "2169-8996 and 2169-897X",
             language = "en",
           targetfile = "essoar.10503135.1.pdf",
        urlaccessdate = "28 abr. 2024"
}