Generation of science-ready data from processed data products is one of the
major challenges in next-generation radio continuum surveys with the Square
Kilometre Array (SKA) and its precursors, due to the expected data volume and
the need to achieve a high degree of automated processing. Source extraction,
characterization, and classification are the major stages involved in this
process. In this work we focus on the classification of compact radio sources
in the Galactic plane using both radio and infrared images as inputs. To this
aim, we produced a curated dataset of ~20,000 images of compact sources of
different astronomical classes, obtained from past radio and infrared surveys,
and novel radio data from pilot surveys carried out with the Australian SKA
Pathfinder (ASKAP). Radio spectral index information was also obtained for a
subset of the data. We then trained two different classifiers on the produced
dataset. The first model uses gradient-boosted decision trees and is trained on
a set of pre-computed features derived from the data, which include
radio-infrared colour indices and the radio spectral index. The second model is
trained directly on multi-channel images, employing convolutional neural
networks. Using a completely supervised procedure, we obtained a high
classification accuracy (F1-score>90%) for separating Galactic objects from the
extragalactic background. Individual class discrimination performances, ranging
from 60% to 75%, increased by 10% when adding far-infrared and spectral index
information, with extragalactic objects, PNe and HII regions identified with
higher accuracies. The implemented tools and trained models were publicly
released, and made available to the radioastronomical community for future
application on new radio data.

使用射电和红外图像对银河平面上的紧凑射电源进行分类，训练了两个不同的分类器，通过完全监督的方法获得高分类准确率，两个模型的性能随着添加远红外和谱指数信息而提高。