NASA’s Planetary Data System (PDS)* contains data collected by missions
to explore our solar system. This includes Lunar Reconnaissance Orbiter
(LRO), which has collected as much data as all other planetary missions
combined. Currently, PDS offers no way to search lunar images based on
content. Working with the PDS Cartography and Imaging Sciences Node
(IMG), we develop LROCNet, a deep learning (DL) classifier for imagery
from LRO’s Narrow Angle Cameras (NACs). Data we get from NACs are 5km
swaths, at nominal orbit, so we perform a saliency detection step to
find surface features of interest. A detector developed for Mars HiRISE
(Wagstaff et al, 2021) worked well for our purposes, after updating
based on LROC image resolution. We use this detector to create a set of
image chipouts (small cutouts) from the larger image, sampling the lunar
globe. The chipouts are used to train LROCNet. We select classes of
interest based on what is visible at the NAC resolution, consulting with
scientists and performing a literature review. Initially, we had 7
classes: fresh crater, old crater, overlapping craters, irregular mare
patches, rockfalls and landfalls, of scientific interest, and none.
Using the Zooniverse platform, we set up a labeling tool and labeled
5,000 images. We found that fresh crater made up 11% of the data, old
crater 18%, with the vast majority none. Due to limited examples of the
other classes, we reduced our initial class set to: fresh crater (with
impact ejecta), old crater, and none. We divided the images into
train/validation/test set making sure no image swaths span multiple sets
and fine tuned pre-trained DL models. VGG-11, a standard DL model, gives
the best performance on the validation set, with an overall accuracy of
82% on the test set. We had 83% label agreement in our human label
study; labeling was difficult as there is no clear class boundary. Our
DL model accuracy is similar to human labelers. 64% of fresh craters,
80% old craters, and 86% of the none class are classified correctly.
Predictions from this model will be integrated with IMG’s Atlas,
allowing users to interactively search classes of interest.
*https://pds-imaging.jpl.nasa.gov Copyright © 2022, California Institute
of Technology. U.S. Government sponsorship acknowledged.