  Deep learning architectures such as convolutional neural networks are the
standard in computer vision for image processing tasks. Their accuracy however
often comes at the cost of long and computationally expensive training, the
need for large annotated datasets, and extensive hyper-parameter searches. On
the other hand, a different method known as convolutional extreme learning
machine has shown the potential to perform equally with a dramatic decrease in
training time. Space imagery, especially about small bodies, could be well
suited for this method. In this work, convolutional extreme learning machine
architectures are designed and tested against their deep-learning counterparts.
Because of the relatively fast training time of the former, convolutional
extreme learning machine architectures enable efficient exploration of the
architecture design space, which would have been impractical with the latter,
introducing a methodology for an efficient design of a neural network
architecture for computer vision tasks. Also, the coupling between the image
processing method and labeling strategy is investigated and demonstrated to
play a major role when considering vision-based navigation around small bodies.
