# Advances on the classification of radio image cubes

arXiv: https://arxiv.org/abs/2305.03435 · HTML: https://mlsanity.com/paper/2305.03435/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 103 references that are in the map.
- First posted on arXiv 5 May 2023.

## Builds on (most cited first, 15 of 15)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence](https://mlsanity.com/paper/2001.07685/) (2020)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [Group Equivariant Convolutional Networks](https://mlsanity.com/paper/1602.07576/) (2016)
- [A Survey on Deep Hashing Methods](https://mlsanity.com/paper/2003.03369/) (2020)
- [Attention-gating for improved radio galaxy classification](https://mlsanity.com/paper/2012.01248/) (2020)
- [Radio Galaxy Zoo: Using semi-supervised learning to leverage large unlabelled data-sets for radio galaxy classification under data-set shift](https://mlsanity.com/paper/2204.08816/) (2022)
- [Deep reinforcement learning for smart calibration of radio telescopes](https://mlsanity.com/paper/2102.03200/) (2021)
- [DeepSource: Point Source Detection using Deep Learning](https://mlsanity.com/paper/1807.02701/) (2018)
- [Radio Galaxy Zoo: Unsupervised Clustering of Convolutionally Auto-encoded Radio-astronomical Images](https://mlsanity.com/paper/1906.02864/) (2019)
- [Data-Efficient Classification of Radio Galaxies](https://mlsanity.com/paper/2011.13311/) (2020)
- [Morphological classification of compact and extended radio galaxies using convolutional neural networks and data augmentation techniques](https://mlsanity.com/paper/2107.00385/) (2021)
- [DECORAS: detection and characterization of radio-astronomical sources using deep learning](https://mlsanity.com/paper/2109.09077/) (2021)

## Abstract (arXiv)

Modern radio telescopes will daily generate data sets on the scale of exabytes for systems like the Square Kilometre Array (SKA). Massive data sets are a source of unknown and rare astrophysical phenomena that lead to discoveries. Nonetheless, this is only plausible with the exploitation of intensive machine intelligence to complement human-aided and traditional statistical techniques. Recently, there has been a surge in scientific publications focusing on the use of artificial intelligence in radio astronomy, addressing challenges such as source extraction, morphological classification, and anomaly detection. This study presents a succinct, but comprehensive review of the application of machine intelligence techniques on radio images with emphasis on the morphological classification of radio galaxies. It aims to present a detailed synthesis of the relevant papers summarizing the literature based on data complexity, data pre-processing, and methodological novelty in radio astronomy. The rapid advancement and application of computer intelligence in radio astronomy has resulted in a revolution and a new paradigm shift in the automation of daunting data processes. However, the optimal exploitation of artificial intelligence in radio astronomy, calls for continued collaborative efforts in the creation of annotated data sets. Additionally, in order to quickly locate radio galaxies with similar or dissimilar physical characteristics, it is necessary to index the identified radio sources. Nonetheless, this issue has not been adequately addressed in the literature, making it an open area for further study.