Morphological Classification of Galaxies with Different Grayscale Images Using Deep Learning

• João Pedro Holanda Universidade Federal do Piauí
• Melissa Sales Universidade Federal do Piauí
• Marcus Ferraz Universidade Federal do Piauí
• Roney Santos Universidade Federal do Piauí

DOI: https://doi.org/10.5753/eniac.2023.234521

Resumo

In recent years, several images of galaxies have been collected by telescopes, so that they could be morphologically analyzed using artificial intelligence devices. Thus, this work aims to analyze new image processing approaches, using grayscale conversion algorithms, in order to explore their influence on CNN (Convolutional Neural Networks). Images from the Galaxies10 DECals dataset of two different types of galaxies were used, which were grayscaled and analyzed separately on the CNN. Aspects such as the influence of redshift and the average pixel value have been studied, since grayscale conversions depend on the influence of each channel. It was concluded that, besides altering the assertiveness of the CNN, the applied grayscales also facilitate recognition by the CNN in specific cases.

Palavras-chave: Image Processing, Grayscale, Galaxies, Convolutional Neural Networks

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