Improved classification of star and galaxy from telescope by using a spatio-spectral feature ResNet model
Küçük Resim Yok
Tarih
2026
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier Sci Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Given the vast number of galaxies and stellar constellations, automatic identification and morphological classification of stars and galaxies have become increasingly important for astronomical research. Astronomers rely on automated methods for distinguishing star and galaxy images in astronomical observations. The Convolutional Neural Networks (CNNs), which are powerful machine learning tools for the multi-class, closed-set image classification problems, have been effectively applied to the classification of astronomical images. However, accurate classification of astronomical images remains a challenging task because of a number of natural and technical difficulties, such as atmospheric seeing, instrumental noise, brightness variation and the low-image resolution. This study investigates use of spatio-spectral features in order to enhance the performance of ResNet-based classification models for distinguishing stars and galaxies in telescopic images. The spatial features in the pixel domain are combined with spectral features from the frequency domain to obtain a three channel spatio-spectral image representation. We demonstrate that combining spatio-spectral features improves the performance robustness of ResNet neural network classification model. Advantages of these features in the image classification problem come from properties that phase spectrum is nearly invariant to brightness variations, whereas the amplitude spectrum is relatively invariant to source position shifting in the image. To illustrate the effectiveness of spatio-spectral features in star-galaxy classification, the authors conducted experiments on low-resolution, noisy images that were captured by the 1.3-m telescope at the Devasthal Observatory. The results show that incorporating spatio-spectral features into ResNet-50 models can improve the classification accuracy by up to 12 % on this dataset. (c) 2025 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
Açıklama
Anahtar Kelimeler
Star-galaxy classification, Deep learning, Convolutional neural networks, Frequency domain, Fourier transform
Kaynak
Advances in Space Research
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
77
Sayı
4
