Zernike moments description of solar and astronomical features: Python code

TL;DR

This paper introduces a Python package utilizing Zernike moments for automatic, rotation, scale, and translation invariant description of solar and astronomical images, aiding feature identification and tracking.

Contribution

It presents a novel Python implementation of Zernike moments tailored for astronomical image analysis, demonstrating their effectiveness in describing and classifying solar features.

Findings

Zernike moments effectively describe solar and astronomical features.

The package provides rotation, scale, and translation invariance.

Application to machine learning enhances feature recognition.

Abstract

Due to the massive increase in astronomical images (such as James Webb and Solar Dynamic Observatory), automatic image description is essential for solar and astronomical. Zernike moments (ZMs) are unique due to the orthogonality and completeness of Zernike polynomials (ZPs); hence valuable to convert a two-dimensional image to one-dimensional series of complex numbers. The magnitude of ZMs is rotation invariant, and by applying image normalization, scale and translation invariants can be made, which are helpful properties for describing solar and astronomical images. In this package, we describe the characteristics of ZMs via several examples of solar (large and small scale) features and astronomical images. ZMs can describe the structure and morphology of objects in an image to apply machine learning to identify and track the features in several disciplines.