Journal Article2023Cited by 3

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

Published at

WACV, IJCNN, ICIP, Smart Agricultural Technology

Research Interests

Machine Learning, Computer Vision

Sugandha Roy
Sugandha Roy

Sugandha Roy

PhD Student

.

Research Interests

Machine Learning

Amin Ahsan Ali, PhD
Amin Ahsan Ali, PhD

Amin Ahsan Ali, PhD

  • Director, AI/ML Wing

Professor

Department of Computer Science and Engineering

Independent University, Bangladesh

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

Research Interests

Artificial Intelligence, Machine Learning

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

Prof. M Ashraful Amin, PhD

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

Professor

+1 more affiliation

Department of Information Sciences and Technology

George Mason University, USA

Published at

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

Research Interests

Machine Learning, Cognitive & Vision Science, Cybernetics, Surveillance & Security, ICT in Education, Health, & Agriculture, Human-Computer Interaction, Internet of Things, Robotics

References

  1. 1.B. L. Fanaroff, J. M. Riley. (1974). The Morphology of Extragalactic Radio Sources of High and Low Luminosity. Monthly Notices of the Royal Astronomical Society, 167(1), 31P–36P[10.1093/mnras/167.1.31p]
  2. 2.A. K. Aniyan, K. Thorat. (2017). Classifying Radio Galaxies with the Convolutional Neural Network. The Astrophysical Journal Supplement Series, 230(2), 20[10.3847/1538-4365/aa7333]
  3. 3.Julie Banfield, O. Ivy Wong, Kyle Willett, R. P. Norris, L. Rudnick, Stanislav S. Shabala, Brooke Simmons, C Snyder, Avery Garon, N. Seymour, E. Middelberg, H. Andernach, Chris Lintott, Kristin A. Jacob, A. D. Kapińska, Minnie Mao, Karen L. Masters, M. J. Jarvis, Kevin Schawinski, Edward Paget, R. J. Simpson, H.-R. Klöckner, S. P. Bamford, T. Burchell, Kate Chow, Garret Cotter, L. Fortson, Ian Heywood, T. W. Jones, Sugata Kaviraj, Á. R. López-Sánchez, W. Peter Maksym, Kai Polsterer, K. Borden, Robert Hollow, L. Whyte. (2015). Radio Galaxy Zoo: host galaxies and radio morphologies derived from visual inspection. Monthly Notices of the Royal Astronomical Society, 453(3), 2327–2341[10.1093/mnras/stv1688]
  4. 4.Chen Wu, O. Ivy Wong, L. Rudnick, Stanislav S. Shabala, Matthew J. Alger, Julie Banfield, Cheng Soon Ong, S. V. White, Avery Garon, R. P. Norris, H. Andernach, Jean Tate, Vesna Lukic, Hongming Tang, Kevin Schawinski, Foivos I. Diakogiannis. (2018). Radio Galaxy Zoo:<scp>Claran</scp>– a deep learning classifier for radio morphologies. Monthly Notices of the Royal Astronomical Society, 482(1), 1211–1230[10.1093/mnras/sty2646]
  5. 5.Halime Miraghaei, P. N. Best. (2017). The nuclear properties and extended morphologies of powerful radio galaxies: the roles of host galaxy and environment. Monthly Notices of the Royal Astronomical Society, stx007[10.1093/mnras/stx007]
  6. 6.D. D. Proctor. (2011). MORPHOLOGICAL ANNOTATIONS FOR GROUPS IN THE FIRST DATABASE. The Astrophysical Journal Supplement Series, 194(2), 31[10.1088/0067-0049/194/2/31]
  7. 7.Zhixian Ma, Haiguang Xu, Jie Zhu, Dan Hu, Weitian Li, Chenxi Shan, Zhenghao Zhu, Liyi Gu, Jinjin Li, Chengze Liu, Xiangping Wu. (2019). A Machine Learning Based Morphological Classification of 14,245 Radio AGNs Selected from the Best–Heckman Sample. The Astrophysical Journal Supplement Series, 240(2), 34[10.3847/1538-4365/aaf9a2]
  8. 8.Timothy J. Galvin, Minh Huynh, R. P. Norris, X. Rosalind Wang, Ellen Hopkins, Kai Lars Polsterer, Nicholas Ralph, Andrew O’Brien, G. Heald. (2020). Cataloguing the radio-sky with unsupervised machine learning: a new approach for the SKA era. Monthly Notices of the Royal Astronomical Society, 497(3), 2730–2758[10.1093/mnras/staa1890]
  9. 9.Wathela Alhassan, A. R. Taylor, M. Vaccari. (2018). The FIRST Classifier: compact and extended radio galaxy classification using deep Convolutional Neural Networks. Monthly Notices of the Royal Astronomical Society, 480(2), 2085–2093[10.1093/mnras/sty2038]
  10. 10.Anna M. M. Scaife, Fiona Porter. (2021). Fanaroff–Riley classification of radio galaxies using group-equivariant convolutional neural networks. Monthly Notices of the Royal Astronomical Society, 503(2), 2369–2379[10.1093/mnras/stab530]
  11. 11.Inigo Val Slijepcevic, Anna M. M. Scaife, Mike Walmsley, Micah Bowles, O. Ivy Wong, Stanislav S. Shabala, Hongming Tang. (2022). Radio Galaxy Zoo: Using semi-supervised learning to leverage large unlabelled data-sets for radio galaxy classification under data-set shift. arXiv (Cornell University)[10.1093/mnras/stac1135]
  12. 12.Tapan K. Sasmal, Soumen Bera, Sabyasachi Pal, Soumen Mondal. (2022). A New Catalog of Head–Tail Radio Galaxies from the VLA FIRST Survey. The Astrophysical Journal Supplement Series, 259(2), 31[10.3847/1538-4365/ac4473]
  13. 13.Zhixian Ma, Jie Zhu, Yongkai Zhu, Haiguang Xu. (2019). Classification of Radio Galaxy Images with Semi-supervised Learning. Communications in computer and information science, 191–200[10.1007/978-981-32-9563-6_20]
  14. 14.Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre H. Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Ávila Pires, Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, Bilal Piot, Koray Kavukcuoglu, Rémi Munos, Michal Vaľko. (2020). Bootstrap your own latent: A new approach to self-supervised Learning. arXiv (Cornell University)[10.48550/arxiv.2006.07733]
  15. 15.Fiona Porter. (2020). MiraBest Batched Dataset. Zenodo (CERN European Organization for Nuclear Research)[10.5281/zenodo.4288837]

Cited By

  1. 1.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]
  2. 2.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]
  3. 3.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]
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