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 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 auto-encoder 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. The availability of target labels in this data allowed for 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.

我们开发了一种将大型天文图像映射到二维地图上并将它们聚类的方法。各种最先进的机器学习算法的组合用于开发完全无监督的图像质量评估和聚类系统。我们的管道由一个数据预处理步骤组成，其中在大型天文图像中识别单个图像对象并将其转换为较小的像素图像。然后将此数据馈送到与自组织图 (SOM) 联合训练的深度卷积自动编码器。这部分可以用作推荐系统。结果输出最终使用第二个深度 SOM 映射到二维网格上。我们使用从地面望远镜获取的数据，作为案例研究，将系统的能力和性能与 Teimoorinia 等人提出的监督方法获得的结果进行比较。此数据中目标标签的可用性允许在我们的无监督和有监督方法之间进行全面的性能比较。除了在该项目中执行的图像质量评估之外，我们的方法还可以有各种其他应用。例如，它可以帮助专家在极短的时间内标记图像，而人工干预最少。它还可以用作基于内容的推荐系统，能够根据所需内容过滤图像。我们的方法可以有各种其他应用。例如，它可以帮助专家在极短的时间内标记图像，而人工干预最少。它还可以用作基于内容的推荐系统，能够根据所需内容过滤图像。我们的方法可以有各种其他应用。例如，它可以帮助专家在极短的时间内标记图像，而人工干预最少。它还可以用作基于内容的推荐系统，能够根据所需内容过滤图像。