  We have developed a method that maps large astronomical images onto a
two-dimensional map and clusters them. A combination of various
state-of-the-art machine learning (ML) algorithms is used to develop a fully
unsupervised image quality assessment and clustering system. Our pipeline
consists of a data pre-processing step where individual image objects are
identified in a large astronomical image and converted to smaller pixel images.
This data is then fed to a deep convolutional autoencoder jointly trained with
a self-organizing map (SOM). This part can be used as a recommendation system.
The resulting output is eventually mapped onto a two-dimensional grid using a
second, deep, SOM. We use data taken from ground-based telescopes and, as a
case study, compare the system's ability and performance with the results
obtained by supervised methods presented by Teimoorinia et al. (2020). The
availability of target labels in this data allowed a comprehensive performance
comparison between our unsupervised and supervised methods. In addition to
image-quality assessments performed in this project, our method can have
various other applications. For example, it can help experts label images in a
considerably shorter time with minimum human intervention. It can also be used
as a content-based recommendation system capable of filtering images based on
the desired content.
