Semantic Segmentation of Radio-Astronomical Images
Authors: 
Carmelo Pino, Renato Sortino, Eva Sciacca, Simone Riggi, Concetto SpampinatoAuthors Info & Claims
Carmelo Pino, Renato Sortino, Eva Sciacca, Simone Riggi, Concetto SpampinatoAuthors Info & ClaimsPages 393 - 403
Abstract
In the context of next-generation radio-astronomical visual surveys, automated object detection and segmentation are necessary tasks to support astrophysics research from observations. Indeed, identifying manually astronomical sources (e.g., galaxies) from the daunting amount of acquired images is largely unfeasible, greatly limiting the huge potential of big data in the field. As a consequence, the astrophysics research has directed its attention, with increasing interest given the recent success in AI, to learning-based computer vision methods.
Several automated visual source extractors have been proposed, but they mainly pose the source identification as an object detection. While this may reduce the time needed for visual inspection, it presents an evident shortcoming in case of objects consisting of multiple, spatial distant, parts (e.g., the same galaxy appearing as a set of isolated objects). This specific limitation can be overcome through semantic segmentation. Consequently, in this paper we evaluate the performance of multiple semantic segmentation models for pixelwise dense prediction in astrophysical images with the objective to identify and segment galaxies, sidelobe, and compact sources. Performance analysis is carried out on a dataset consisting of over 9,000 images and shows how state-of-the-art segmentation models yield accurate results, thus providing a baseline for future works. We also employ the output segmentation maps for object detection and results are better than those obtained with Mask-RCNN based detectors that are largely used in the field.
References
[1]
Burke, C.J., et al.: Deblending and classifying astronomical sources with mask R-CNN deep learning. Monthly Not. R. Astron. Soc. 490(3), 3952–3965 (2019)
[2]
Cunningham, F., et al.: Ensembl 2019. Nucleic Acids Res. 47(D1), D745–D751 (2019)
[3]
DeBoer, D.R., et al.: Australian SKA pathfinder: a high-dynamic range wide-field of view survey telescope. Proc. IEEE 97(8), 1507–1521 (2009)
[4]
Farias, H., et al.: Mask galaxy: morphological segmentation of galaxies. Astron. Comput. 33, 100420 (2020)
[5]
Friedlander, A.E.A.: Latent Dirichlet allocation for image segmentation and source finding in radio astronomy images. In: ACM International Conference Proceeding Series (2012)
[6]
González, R.E., Muñoz, R.P., Hernández, C.A.: Galaxy detection and identification using deep learning and data augmentation. Astron. Comput. 25, 103–109 (2018)
[7]
He, K., et al.: Mask R-CNN. In: ICCV (2017)
[8]
Hou, Y.C., et al.: Identification and extraction of solar radio spikes based on deep learning. Solar Phys. 295(10), 1–11 (2020)
[9]
Huang, G., et al.: Densely connected convolutional networks. In: CVPR (2017).
[10]
Jégou, S., et al.: The one hundred layers tiramisu: fully convolutional densenets for semantic segmentation. In: CVPRW (2017)
[11]
Jia, P., et al.: Detection and classification of astronomical targets with deep neural networks in wide field small aperture telescopes. arXiv 159(5) (2020)
[12]
Kayalibay, B., et al.: CNN-based segmentation of medical imaging data. arXiv preprint arXiv:1701.03056 (2017)
[13]
Lin, T.Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014).
[14]
Lukic, V., Gasperin, F.D., Brüggen, M.: ConvoSource: radio-astronomical source-finding with convolutional neural networks (2019)
[15]
Murabito, F., et al.: Deep recurrent-convolutional model for automated segmentation of craniomaxillofacial CT scans. In: 2020 25th International Conference on Pattern Recognition (ICPR) (2021)
[16]
Norris, R.P., et al.: EMU: evolutionary map of the universe. Publ. Astron. Soc. Aust. 28(3), 215–248 (2011)
[17]
Ren, S., et al.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39, 1137–1149 (2015)
[18]
Riggi, S., et al.: Caesar source finder: recent developments and testing. Publ. Astron. Soc. Aust. 36 (2019)
[19]
Riggi, S., et al.: Automated detection of extended sources in radio maps: progress from the SCORPIO survey. Monthly Not. R. Astron. Soc. 460(2), 1486–1499 (2016)
[20]
Robotham, A., et al.: ProFound: source extraction and application to modern survey data. Monthly Not. R. Astron. Soc. 476(3), 3137–3159 (2018)
[21]
Ronneberger, O., et al.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015).
[22]
Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. (IJCV) 115(3), 211–252 (2015)
[23]
Shimwell, T., et al.: The LOFAR two-metre sky survey-II. First data release. Astron. Astrophys. 622, A1 (2019)
[24]
Vafaei-Sadr, A., et al.: DeepSource: point source detection using deep learning. Monthly Not. R. Astron. Soc. 484(2), 2793–2806 (2019)
[25]
Wu, C., et al.: Radio galaxy Zoo:CLARAN - a deep learning classifier for radio morphologies. Monthly Not. R. Astron. Soc. 482(1), 1211–1230 (2018)
[26]
Xie, Z., Ji, C., Wang, H.: Single and multiwavelength detection of coronal dimming and coronal wave using faster R-CNN. Adv. Astron. 2019 (2019)
[27]
Zhou, Z., et al.: Unet++: a nested U-Net architecture for medical image segmentation. In: Stoyanov, D., et al. (eds.) DLMIA 2018, ML-CDS 2018. LNCS, vol. 11045, pp. 3–11. Springer, Cham (2018).
[28]
Zhu, Q., et al.: Deeply-supervised CNN for prostate segmentation. In: 2017 International Joint Conference on Neural Networks (IJCNN). IEEE (2017)
Recommendations
Detection and Segmentation of Concealed Objects in Terahertz Images
Terahertz imaging makes it possible to acquire images of objects concealed underneath clothing by measuring the radiometric temperatures of different objects on a human subject. The goal of this work is to automatically detect and segment concealed ...
Segmentation of CT Brain Images Using Unsupervised Clusterings
In this paper, we present non-identical unsupervised clustering techniques for the segmentation of CT brain images. Prior to segmentation, we enhance the visualization of the original image. Generally, for the presence of abnormal regions in the brain ...
Comments
Information & Contributors
Information
Published In
Oct 2021
453 pages
ISBN:978-3-030-89690-4
DOI:10.1007/978-3-030-89691-1
© Springer Nature Switzerland AG 2021.
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 05 October 2021
Author Tags
Qualifiers
- Article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 16 Oct 2024
Other Metrics
Citations
View Options
View options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in