Journal Article2023Cited by 5

Morphological classification of Radio Galaxies using Semi-Supervised Group Equivariant CNNs

Mir Sazzat Hossain, Sugandha Roy, K. M. B. Asad, Arshad Momen, Amin Ahsan Ali, M Ashraful Amin, A. K. M. Mahbubur Rahman

Procedia Computer Science

Elsevier BV, Vol. 222, pp. 601-612, ISBN: 1877-0509

DOI: 10.1016/j.procs.2023.08.198Open AccessURL

Abstract

Out of the estimated few trillion galaxies, only around a million have been detected through radio frequencies, and only a tiny fraction, approximately a thousand, have been manually classified. We have addressed this disparity between labeled and unlabeled images of radio galaxies by employing a semi-supervised learning approach to classify them into the known Fanaroff-Riley Type I (FRI) and Type II (FRII) categories. A Group Equivariant Convolutional Neural Network (G-CNN) was used as an encoder of the state-of-the-art self-supervised methods SimCLR (A Simple Framework for Contrastive Learning of Visual Representations) and BYOL (Bootstrap Your Own Latent). The G-CNN preserves the equivariance for the Euclidean Group E(2), enabling it to effectively learn the representation of globally oriented feature maps. After representation learning, we trained a fully-connected classifier and fine-tuned the trained encoder with labeled data. Our findings demonstrate that our semi-supervised approach outperforms existing state-of-the-art methods across several metrics, including cluster quality, convergence rate, accuracy, precision, recall, and the F1-score. Moreover, statistical significance testing via a t-test revealed that our method surpasses the performance of a fully supervised G-CNN. This study emphasizes the importance of semi-supervised learning in radio galaxy classification, where labeled data are still scarce, but the prospects for discovery are immense.

Keywords

  • BYOL
  • SimCLR
  • semi-supervised learning
  • radio galaxies
  • Fanaroff-Riley classification
  • self-supervised representation learning
  • galaxy morphology classification
  • limited labeled data

CCDS Authors

Mir Sazzat Hossain
Mir Sazzat Hossain

Mir Sazzat Hossain

  • Research Assistant, CCDS

  • Bachelors

    Independent University, Bangladesh (IUB)

Published at

WACV, IJCNN, ICIP, Smart Agricultural Technology

Sugandha Roy
Sugandha Roy

Sugandha Roy

  • Alumni, CCDS

  • Master's Student

    Washington University in St. Louis, USA

  • BSc

    University of Dhaka, Bangladesh

Prof. Amin Ahsan Ali, PhD
Amin Ahsan Ali, PhD

Prof. Amin Ahsan Ali, PhD

  • Director, AI/ML Wing
  • Supervisor, ComBio Wing

  • Professor

    Department of Computer Science and Engineering

    Independent University, Bangladesh (IUB)

Published at

ACL, CHI, UbiComp, ECCV, IJCAI, ECAI, WACV, IPSN, PAKDD, LREC, IJCNN, ICPR, ICIP, BEA, BLP, ICMLA, Scientific Reports, Scientific Data, Pattern Recognition, Information Sciences, Ad Hoc Networks, Drug and Alcohol Dependence, PLOS ONE

Prof. M Ashraful Amin, PhD
M Ashraful Amin, PhD

Prof. M Ashraful Amin, PhD

  • Founder Director, CCDS
  • Director, AI/ML, HCI & DS Wings

Published at

ACL, CHI, UbiComp, ECAI, WACV, PAKDD, IJCNN, ICPR, ICIP, HCII, CLEF, BLP, Scientific Reports, Pattern Recognition, JMIR, IEEE Access, Smart Agricultural Technology, Frontiers in Computational Neuroscience, Diabetes Research and Clinical Practice, PLOS ONE

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Cited By

  1. 1.Steven Ndung’u, Trienko Grobler, Stefan J. Wijnholds, Dimka Karastoyanova, George Azzopardi. (2023). Advances on the morphological classification of radio galaxies: A review. New Astronomy Reviews, 97, 101685[10.1016/j.newar.2023.101685]
  2. 2.S. Riggi, T. Cecconello, S. Palazzo, A.M. Hopkins, N. Gupta, C. Bordiu, A. Ingallinera, C. Buemi, F. Bufano, F. Cavallaro, M.D. Filipović, P. Leto, S. Loru, A.C. Ruggeri, C. Trigilio, G. Umana, F. Vitello. (2024). Self-supervised contrastive learning of radio data for source detection, classification and peculiar object discovery. Publications of the Astronomical Society of Australia, 41[10.1017/pasa.2024.84]
  3. 3.Steven Ndung’u, Trienko Grobler, Stefan J Wijnholds, Dimka Karastoyanova, George Azzopardi. (2024). Classification of radio galaxies with trainable COSFIRE filters. Monthly Notices of the Royal Astronomical Society, 530(1), 783–794[10.1093/mnras/stae821]
  4. 4.Nicolas Baron Perez, Marcus Brüggen, Gregor Kasieczka, Luisa Lucie-Smith. (2025). Classification of radio sources through self-supervised learning. Astronomy and Astrophysics, 699, A302[10.1051/0004-6361/202554735]
  5. 5.M. S. Hossain, M. S. H. Shahal, K. M. B. Asad, P. Saikia, A. Khan, F. Akter, A. A. Ali, M. A. Amin, D. P. Guha, M. Hasan, M. O. B. Jihad, A. Momen, S. Sen, A. K. M. M. Rahman. (2026). RGC: A radio AGN classifier based on deep learning. Astronomy and Astrophysics, 714, A5[10.1051/0004-6361/202557828]
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