# Galaxy Classification Using Transfer Learning and Ensemble of CNNs With Multiple Colour Spaces

arXiv: https://arxiv.org/abs/2305.00002 · HTML: https://mlsanity.com/paper/2305.00002/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 82 references that are in the map.
- First posted on arXiv 26 Mar 2023.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [CNN Features off-the-shelf: an Astounding Baseline for Recognition](https://mlsanity.com/paper/1403.6382/) (2014)
- [Ensemble deep learning: A review](https://mlsanity.com/paper/2104.02395/) (2021)
- [Rotation-invariant convolutional neural networks for galaxy morphology prediction](https://mlsanity.com/paper/1503.07077/) (2015)
- [The Relative Performance of Ensemble Methods with Deep Convolutional Neural Networks for Image Classification](https://mlsanity.com/paper/1704.01664/) (2017)
- [ColorNet: Investigating the importance of color spaces for image classification](https://mlsanity.com/paper/1902.00267/) (2019)
- [Using transfer learning to detect galaxy mergers](https://mlsanity.com/paper/1805.10289/) (2018)
- [Big Universe, Big Data: Machine Learning and Image Analysis for Astronomy](https://mlsanity.com/paper/1704.04650/) (2017)
- [Vehicle Color Recognition using Convolutional Neural Network](https://mlsanity.com/paper/1510.07391/) (2015)
- [DeepShadows: Separating Low Surface Brightness Galaxies from Artifacts using Deep Learning](https://mlsanity.com/paper/2011.12437/) (2020)
- [A CNN and LSTM-Based Approach to Classifying Transient Radio Frequency Interference](https://mlsanity.com/paper/1803.02684/) (2018)
- [StegColNet: Steganalysis based on an ensemble colorspace approach](https://mlsanity.com/paper/2002.02413/) (2020)

## Built on by (most cited first, 1 of 1)

- [Neural Network Prediction of Strong Lensing Systems with Domain Adaptation and Uncertainty Quantification](https://mlsanity.com/paper/2411.03334/) (2024)

## Abstract (arXiv)

Big data has become the norm in astronomy, making it an ideal domain for computer science research. Astronomers typically classify galaxies based on their morphologies, a practice that dates back to Hubble (1936). With small datasets, classification could be performed by individuals or small teams, but the exponential growth of data from modern telescopes necessitates automated classification methods. In December 2013, Winton Capital, Galaxy Zoo, and the Kaggle team created the Galaxy Challenge, which tasked participants with developing models to classify galaxies. The Kaggle Galaxy Zoo dataset has since been widely used by researchers. This study investigates the impact of colour space transformation on classification accuracy and explores the effect of CNN architecture on this relationship. Multiple colour spaces (RGB, XYZ, LAB, etc.) and CNN architectures (VGG, ResNet, DenseNet, Xception, etc.) are considered, utilizing pre-trained models and weights. However, as most pre-trained models are designed for natural RGB images, we examine their performance with transformed, non-natural astronomical images. We test our hypothesis by evaluating individual networks with RGB and transformed colour spaces and examining various ensemble configurations. A minimal hyperparameter search ensures optimal results. Our findings indicate that using transformed colour spaces in individual networks yields higher validation accuracy, and ensembles of networks and colour spaces further improve accuracy. This research aims to validate the utility of colour space transformation for astronomical image classification and serve as a benchmark for future studies.