The VOISE Algorithm: a Versatile Tool for Automatic Segmentation of Astronomical Images

TL;DR

The paper introduces VOISE, an automatic segmentation algorithm based on Voronoi tessellations, for analyzing and quantifying auroral emissions in astronomical images, providing a more objective alternative to manual analysis.

Contribution

It presents VOISE, a novel dynamic Voronoi-based segmentation algorithm, tailored for automated analysis of astronomical images, specifically for studying planetary auroras.

Findings

VOISE effectively segments auroral images with high accuracy.

The algorithm quantifies auroral features such as intensity and spatial extent.

It offers a more automated and objective analysis method than manual inspection.

Abstract

The auroras on Jupiter and Saturn can be studied with a high sensitivity and resolution by the Hubble Space Telescope (HST) ultraviolet (UV) and far-ultraviolet (FUV) Space Telescope spectrograph (STIS) and Advanced Camera for Surveys (ACS) instruments. We present results of automatic detection and segmentation of Jupiter's auroral emissions as observed by HST ACS instrument with VOronoi Image SEgmentation (VOISE). VOISE is a dynamic algorithm for partitioning the underlying pixel grid of an image into regions according to a prescribed homogeneity criterion. The algorithm consists of an iterative procedure that dynamically constructs a tessellation of the image plane based on a Voronoi Diagram, until the intensity of the underlying image within each region is classified as homogeneous. The computed tessellations allow the extraction of quantitative information about the auroral features such as mean intensity, latitudinal and longitudinal extents and length scales. These outputs thus represent a more automated and objective method of characterising auroral emissions than manual inspection.