Estimating the Obstructed Portion of the Night Sky Using Digital Image Processing
Felipe Alexandre da Silva
Grupo de Pesquisa em Matemática Científica e Computacional (GPMCC), Instituto Federal de Educação, Ciência e Tecnologia de São Paulo (IFSP), São José dos Campos, SP, Brazil.
Vinicius Daniel dos Reis de Castro
Grupo de Pesquisa em Matemática Científica e Computacional (GPMCC), Instituto Federal de Educação, Ciência e Tecnologia de São Paulo (IFSP), São José dos Campos, SP, Brazil.
Marcos William da Silva Oliveira
Grupo de Pesquisa em Matemática Científica e Computacional (GPMCC), Instituto Federal de Educação, Ciência e Tecnologia de São Paulo (IFSP), São José dos Campos, SP, Brazil.
Lucas Antonio Caritá *
Grupo de Pesquisa em Matemática Científica e Computacional (GPMCC), Instituto Federal de Educação, Ciência e Tecnologia de São Paulo (IFSP), São José dos Campos, SP, Brazil.
Irapuan Rodrigues
Universidade do Vale do Paraíba (UNIVAP), São José dos Campos, SP, Brazil.
Francisco Carlos Rocha Fernandes
Faculdade de Tecnologia de Bauru (FATEC), Bauru, SP, Brazil.
*Author to whom correspondence should be addressed.
Abstract
Determining the portion obscured by clouds in the night sky recorded in an image from a meteor monitoring station can be useful to help in the discussion and analysis of studies in this area. Thereby, this work presents a proposal to quantitatively measure the proportion of the night sky covered by clouds from images of meteor records. The images used are from EXOSS database, particularly from the UVP1 and UVP2 stations. Two approaches were elaborated: the first one computes the proportion of pixels in clouds over the total number of pixels, the second approach calculates another percentage value by using image processing techniques and applying an equalization method. An application was created to perform the calculations in an automated way. The program displays an enhanced image and the two calculated percentages and can be used for specialists as a tool to guide their choice of coverage values. The results showed that the second approach proves to be better for “many” and “few” clouds; furthermore, the first approach proves to be better in low cloud density images.
Keywords: Image processing, meteor monitoring, meteor shower, EXOSS