  This study investigate the effectiveness of using Deep Learning (DL) for the
classification of planetary nebulae (PNe). It focusses on distinguishing PNe
from other types of objects, as well as their morphological classification. We
adopted the deep transfer learning approach using three ImageNet pre-trained
algorithms. This study was conducted using images from the Hong Kong/Australian
Astronomical Observatory/Strasbourg Observatory H-alpha Planetary Nebula
research platform database (HASH DB) and the Panoramic Survey Telescope and
Rapid Response System (Pan-STARRS). We found that the algorithm has high
success in distinguishing True PNe from other types of objects even without any
parameter tuning. The Matthews correlation coefficient is 0.9. Our analysis
shows that DenseNet201 is the most effective DL algorithm. For the
morphological classification, we found for three classes, Bipolar, Elliptical
and Round, half of objects are correctly classified. Further improvement may
require more data and/or training. We discuss the trade-offs and potential
avenues for future work and conclude that deep transfer learning can be
utilized to classify wide-field astronomical images.
