Panoptic Segmentation of Galactic Structures in LSB Images

TL;DR

This paper introduces a deep learning-based panoptic segmentation method for identifying galactic structures and contaminants in low surface brightness images, improving detection accuracy through a unified model and human-in-the-loop training.

Contribution

It presents a novel unified multi-class segmentation approach combining Mask R-CNN with a contaminant network and adaptive preprocessing, specifically tailored for LSB galactic images.

Findings

Enhanced detection of galactic structures and contaminants.

The combined model outperforms previous methods.

Human-in-the-loop training improves ground truth labeling.

Abstract

We explore the use of deep learning to localise galactic structures in low surface brightness (LSB) images. LSB imaging reveals many interesting structures, though these are frequently confused with galactic dust contamination, due to a strong local visual similarity. We propose a novel unified approach to multi-class segmentation of galactic structures and of extended amorphous image contaminants. Our panoptic segmentation model combines Mask R-CNN with a contaminant specialised network and utilises an adaptive preprocessing layer to better capture the subtle features of LSB images. Further, a human-in-the-loop training scheme is employed to augment ground truth labels. These different approaches are evaluated in turn, and together greatly improve the detection of both galactic structures and contaminants in LSB images.