Optimising and comparing source-extraction tools using objective segmentation quality criteria

Abstract

Context. With the growth of the scale, depth, and resolution of astronomical imaging surveys, there is increased need for highly accurate automated detection and extraction of astronomical sources from images. This also means there is a need for objective quality criteria, and automated methods to optimise parameter settings for these software tools. Aims. We present a comparison of several tools developed to perform this task: namely SExtractor, ProFound, NoiseChisel, and MTObjects. In particular, we focus on evaluating performance in situations that present challenges for detection. For example, faint and diffuse galaxies; extended structures, such as streams; and objects close to bright sources. Furthermore, we develop an automated method to optimise the parameters for the above tools. Methods. We present four different objective segmentation quality measures, based on precision, recall, and a new measure for the correctly identified area of sources. Bayesian optimisation is used to find optimal parameter settings for each of the four tools when applied to simulated data, for which a ground truth is known. After training, the tools are tested on similar simulated data in order to provide a performance baseline. We then qualitatively assess tool performance on real astronomical images from two different surveys. Results. We determine that when area is disregarded, all four tools are capable of broadly similar levels of detection completeness, while only NoiseChisel and MTObjects are capable of locating the faint outskirts of objects. MTObjects achieves the highest scores on all tests for all four quality measures, whilst SExtractor obtains the highest speeds. No tool has sufficient speed and accuracy to be well suited to large-scale automated segmentation in its current form.

Discovery and analysis of low-surface-brightness galaxies in the environment of NGC 1052

“…For instance, traditional computational utilities for automated source detection and analysis are usually not efficient for low-surface-brightness objects. This implies the need for specialized software to perform detection and photometry that maximizes the potential provided by the data (see a review by Haigh et al 2021). Another aspect is related to automatic morphological classification and the identification of artifacts that parametrically mimic LSBGs, such as reflections, faint clumped sources, and inaccurate deblending identifications.…”

A tutorial on applications of power watershed optimization to image processing

“…Sec 8 contains a brief description of the utility of PW framework in explaining the links between shortest path-based filters and spanning tree-based filters. Sec 9 contains experiments on simulated astronomical sky images [43,31]. It is illustrated that the PW counterparts yield similar results as that of the classic methods at a lower computational cost.…”

“…In this section, experiments are performed on simulated astronomical sky images. It is a common practice to use simulations [47,52,40,31] as it is difficult to obtain ground truth segmentation for real astronomical images. The sky images are generated using the R code developed by authors in [43].…”

“…F-ratio and AUC curves are used evaluating segmentation and filtering results. As the tutorial article does not attempt to achieve state-of-the-art results, sophisticated measures such as pairs-of-pixels method [42] or tailor-made evaluation measures for astronomical sky images such as described in [31] are not used. The second aim of the article is to show that the PW versions scale well when compared to the classic implementations.…”

Distributed Connected Component Filtering and Analysis in 2D and 3D Tera-Scale Data Sets

“…They provide a suitable approach for region-based analysis, including various filtering strategies and multi-scale tools [2]. They have been applied in astronomy [3], [4], [5], remote-sensing [6], [7], or medical analysis [8], [9]. With the development of image acquisition techniques, data sets have become larger and more complex to process.…”