LROCNet: Detecting Impact Ejecta and Older Craters on the Lunar Surface

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 5-km 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.) 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 sets making sure no image swaths span multiple sets. We fine tune 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 CL#22-3842 Copyright © 2022, California Institute of Technology. U.S. Government sponsorship acknowledged.