Autonomous digital sky surveys such as Pan-STARRS have the ability to image a very large number of galactic and extragalactic objects, and the large and complex nature of the image data reinforces the use of automation. Here we describe the design and implementation of a data analysis process for automatic broad morphology annotation of galaxies, and applied it to the data of Pan-STARRS DR1. The process is based on filters followed by a two-step convolutional neural network (CNN) classification. Training samples are generated by using an augmented and balanced set of manually classified galaxies. Results are evaluated for accuracy by comparison to the annotation of Pan-STARRS included in a previous broad morphology catalog of Sloan Digital Sky Survey galaxies. Our analysis shows that a CNN combined with several filters is an effective approach for annotating the galaxies and removing unclean images. The catalog contains morphology labels for 1,662,190 galaxies with ∼95% accuracy. The accuracy can be further improved by selecting labels above certain confidence thresholds. The catalog is publicly available.
While deep convolutional neural networks (DCNNs) have demonstrated superiority in their ability to classify image data, one of the primary downsides of DCNNs is that their training normally requires large sets of labeled "ground truth" images. For that reason, DCNNs do not provide an effective solution in many real-world problems in which large sets of labeled images are not available. Here we propose to use the quick learning if SVMs to provide a solution for learning from small image datasets in a non-parametric manner. Experimental results show that while "conventional" DCNN architectures such as ResNet-50 outperform SVMnet when the size of the training set is large, SVMnet provides a much higher accuracy when the number of "ground truth" training samples is small.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.