# Rotation Invariant Descriptors for Galaxy Morphological Classification

## TL;DR

This paper evaluates various rotation-invariant descriptors for galaxy morphology classification, demonstrating a robust approach that achieves high accuracy in distinguishing elliptical and spiral galaxies using machine learning.

## Contribution

The study compares multiple rotation-invariant descriptors and introduces a framework that achieves near-perfect classification accuracy for galaxy types.

## Key findings

- High classification accuracy with AUC of 99.54%
- Robustness of descriptors against noise and transformations
- Effective binary classification of galaxy morphologies

## Abstract

The detection of objects that are multi-oriented is a difficult pattern recognition problem. In this paper, we propose to evaluate the performance of different families of descriptors for the classification of galaxy morphologies. We investigate the performance of the Hu moments, Flusser moments, Zernike moments, Fourier-Mellin moments, and ring projection techniques based on 1D moment and the Fourier transform. We consider two main datasets for the performance evaluation. The first dataset is an artificial dataset based on representative templates from 11 types of galaxies, which are evaluated with different transformations (noise, smoothing), alone or combined. The evaluation is based on image retrieval performance to estimate the robustness of the rotation invariant descriptors with this type of images. The second dataset is composed of real images extracted from the Galaxy Zoo 2 project. The binary classification of elliptical and spiral galaxies is achieved with pre-processing steps including morphological filtering and a Laplacian pyramid. For the binary classification, we compare the different set of features with Support Vector Machines (SVM), Extreme Learning Machine, and different types of linear discriminant analysis techniques. The results support the conclusion that the proposed framework for the binary classification of elliptical and spiral galaxies provides an area under the ROC curve reaching 99.54%, proving the robustness of the approach for helping astronomers to study galaxies.