Quantifying the Consistency and Characterizing the Confidence of Coronal Holes Detected by Active Contours without Edges (ACWE)

TL;DR

This paper introduces a robust ensemble method using Active Contours Without Edges to detect coronal holes in EUV images, providing confidence maps that improve consistency and reliability for space weather prediction.

Contribution

It develops a novel ensemble approach based on region homogeneity for coronal hole detection, enhancing robustness against resolution and temporal variations.

Findings

Median IOU > 0.75 at various resolutions

SSIM > 0.93 even at low resolutions

Consistent segmentation over short timescales

Abstract

Coronal Holes (CHs) are regions of open magnetic field lines, resulting in high speed solar wind. Accurate detection of CHs is vital for space weather prediction. This paper presents an intramethod ensemble for coronal hole detection based on the Active Contours Without Edges (ACWE) segmentation algorithm. The purpose of this ensemble is to develop a confidence map that defines, for all on disk regions of a Solar extreme ultraviolet (EUV) image, the likelihood that each region belongs to a CH based on that region's proximity to, and homogeneity with, the core of identified CH regions. By relying on region homogeneity, and not intensity (which can vary due to various factors including line of sight changes and stray light from nearby bright regions), to define the final confidence of any given region, this ensemble is able to provide robust, consistent delineations of the CH regions. Using the metrics of global consistency error (GCE), local consistency error (LCE), intersection over union (IOU), and the structural similarity index measure (SSIM), the method is shown to be robust to different spatial resolutions maintaining a median IOU $>0.75$ and minimum SSIM $>0.93$ even when the segmentation process was performed on an EUV image decimated from $4096\times4096$ pixels down to $512\times512$ pixels. Furthermore, using the same metrics, the method is shown to be robust across short timescales, producing segmentation with a mean IOU of 0.826 from EUV images taken at a 1 hour cadence, and showing a smooth decay in similarity across all metrics as a function of time, indicating self-consistent segmentations even when corrections for exposure time have not been applied to the data. Finally, the accuracy of the segmentations and confidence maps are validated by considering the skewness (i.e., unipolarity) of the underlying magnetic field.