Classification of Planetary Nebulae through Deep Transfer Learning

Dayang N.F., Awang Iskandar and Zijlstra, Albert A. and McDonald, Iain and Rosni, Abdullah and Fuller, Gary A. and Ahmad H., Fauzi and Johari, Abdullah (2020) Classification of Planetary Nebulae through Deep Transfer Learning. Galaxies, 8 (4). p. 88. ISSN 2075-4434

[img] PDF
Classification of Planetary Nebulae through DeepTransfer Learning_pdf.pdf

Download (157kB)
Official URL:


This study investigate the effectiveness of using Deep Learning (DL) for the classification of planetary nebulae (PNe). It focusses on distinguishing PNe from other types of objects, as well as their morphological classification. We adopted the deep transfer learning approach using three ImageNet pre-trained algorithms. This study was conducted using images from the Hong Kong/Australian Astronomical Observatory/Strasbourg Observatory H-alpha Planetary Nebula research platform database (HASH DB) and the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS). We found that the algorithm has high success in distinguishing True PNe from other types of objects even without any parameter tuning. The Matthews correlation coefficient is 0.9. Our analysis shows that DenseNet201 is the most effective DL algorithm. For the morphological classification, we found for three classes, Bipolar, Elliptical and Round, half of objects are correctly classified. Further improvement may require more data and/or training. We discuss the trade-offs and potential avenues for future work and conclude that deep transfer learning can be utilized to classify wide-field astronomical images.

Item Type: Article
Additional Information: Information, Communication and Creative Technology
Uncontrolled Keywords: deep learning; transfer learning; planetary nebulae; morphology; classification; HASH DB; Pan-STARRS,unimas, university, universiti, Borneo, Malaysia, Sarawak, Kuching, Samarahan, ipta, education, research, Universiti Malaysia Sarawak
Subjects: Q Science > Q Science (General)
Q Science > QB Astronomy
Q Science > QC Physics
Divisions: Academic Faculties, Institutes and Centres > Faculty of Computer Science and Information Technology
Depositing User: Awg Iskandar
Date Deposited: 17 Dec 2020 04:51
Last Modified: 29 Sep 2022 02:02

Actions (For repository members only: login required)

View Item View Item